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Autonomous Vehicles in Support of Naval Operations (2005)

Chapter:3 Autonomy Technology: Capabilities and Potential

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Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
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3
Autonomy Technology: Capabilities and Potential

Autonomous vehicles (AVs) have demonstrated that they can significantly increase the operational capabilities of modern armed forces, and it is evident that they will become an even more important element of warfighting capability in the future. This chapter discusses the state of the art of autonomous systems, examines some promising autonomy technology that will be available in the near future, and identifies some shortfalls in autonomy capability that need to be alleviated. The chapter goes on to explore the level of autonomy as a design choice and autonomy technologies.

TODAY’S AUTONOMOUS VEHICLE SYSTEMS

Types of Systems

There are three types of autonomous vehicle systems: scripted, supervised, and intelligent. Scripted autonomous systems use a preplanned script with embedded physical models to accomplish the intended mission objective. Examples of these systems include smart bombs and guided weapons. Such systems can be generally described as “point, fire, and forget” systems that have no human interaction after they are deployed.

Supervised autonomous systems automate some or all of the functions of planning, sensing, monitoring, and networking to carry out the activities associated with an autonomous vehicle, while using the cognitive abilities of human operators via a communications link to make decisions, perceive the meaning of

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
×

sensor data, diagnose problems, and collaborate with other systems. Most conventional autonomous vehicles and their controlling elements form an autonomous system that fall into this category.

Intelligent autonomous systems use intelligent autonomy technology to embed attributes of human intelligence in the software of autonomous vehicles and their controlling elements. This intelligent autonomy software does the following: (1) it makes decisions, given a set of (generally automated) planned options; (2) it perceives and interprets the meaning of sensed information; (3) it diagnoses vehicle, system, or mission-level problems detected through monitoring; and (4) it collaborates with other systems using communications networks and protocols.

This major section discusses technologies relating to supervised and intelligent autonomous systems. The systems and technology associated with such systems generally reside in the Mission Management System or Command and Control System elements of an the autonomous system (see Figure 3.1), while the actions that implement higher-level decisions are done today (generally autonomously) by the Vehicle Management System (VMS) (e.g., by autopilots). Following is a descriptive list of the various systems that comprise the elements of an AV system.

FIGURE 3.1 The elements of an autonomous vehicle system. NOTE: C2, command and control; C4ISR, command, control, communications, computers, intelligence, surveillance, and reconnaissance; MCG&I, mapping, charting, geodesy, and imagery; ECM, electronic countermeasures; FLT CNTL, flight control; SA, situation awareness.

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
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  • Planning and Decision. Planning and decision is the process of developing a sequence of actions capable of achieving AV mission goals or activity goals, given the state of the world. The Planning and Decision System dynamically plans and commands functions within the VMS to carry out mission activities, given situation-awareness information from the Sensing and Perception System and self-awareness information from the Monitoring and Diagnosis System. A plan diagnosis assesses the need to replan on the basis of situational changes derived from updated information. Planning and decision systems often use human-machine collaboration to complete their tasks.

  • Sensing and Perception. The Sensing and Perception System collects, fuses, and interprets sensor data from local sensors and from the Networking and Collaboration System, which receives data from external sources. This information is used to develop a mission-relevant picture or digital map representation of the current mission situation for use by the Planning and Decision System. The digital map, which is dynamically updated, contains information on the location of the AV with respect to all known threats, targets, terrain, obstacles, and friendly forces. Sensing and perception systems often use human-machine collaboration to complete their tasks.

  • Monitoring and Diagnosis. The Monitoring and Diagnosis System collects, fuses, and interprets sensor information relating to the health of the AV. Its responsibilities include the fault detection and isolation (FDI) of system, subsystem, or component failures. FDI helps prevent loss of the AV resulting from system failures and increases the probability of mission success if vehicle systems can be reconfigured during a mission using redundant capability. This system may also include sensors to monitor health trends in key subsystems in order to enable preventive maintenance and prognostication of future failures.

  • Networking and Collaboration. The Networking and Collaboration System manages the use of data links, frequencies, and information content for purposes of collaboration. Collaboration involves the sharing of information with other autonomous or manned vehicles operating as a team or with other vehicles operating in the same space. The types of information shared are, for example, navigation state for collision avoidance, pop-up threat locations, new target locations or targets of opportunity, and vehicle mission plans or plan fragments required to support the collaboration.

  • Human-System Interface. The Human-System Interface System is an extremely important element of an autonomous system. Even in highly autonomous systems, humans are required to provide high-level objectives, set rules of engagement, supply operational constraints, and support launch-and-recovery operations. Humans are also needed by autonomous systems to help interpret sensor information, monitor systems and diagnose problems, coordinate mission time lines, manage consumables and other resources, and authorize the use of weapons or other mission activities.

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
×
  • Other Autonomous Behaviors. Some VMS functions contain autonomous modes or behaviors that can be commanded and controlled by the Planning and Decision System. A common example is an autopilot function of the Guidance, Navigation, and Control System, which may have multiple modes depending on the flight phase, flight conditions, or operating environment.

The State of the Art

Contemporary autonomous systems employ a wide range of autonomy technology, depending on the vehicle domain (i.e., air, ground, sea) and the operating requirements of the system. The following subsections present a brief summary of the current state of the art for the autonomy capability areas developed in the preceding section, “Types of Systems.”

Planning and Decision

The general problem of planning and decision has been addressed in operations research and artificial intelligence for more than 30 years, with the research addressing increasingly complex formulations of the planning problem. Path planning or route planning is commonly available today in all domains. Autonomous mission planning, which involves the development of plans to achieve mission goals, is primarily accomplished through automated tools that are defined premission and subsequently executed. The Navy’s Portable Flight Planning System (PFPS) for aircraft is an example of a planning system in use today. The PFPS and the developmental Joint Mission Planning System (JMPS) are excellent premission flight-planning systems with large databases of information to support high-fidelity flight planning; however, both lack the ability to rapidly accommodate evolving mission events through dynamic planning.

The modification of mission plans owing to the occurrence of unanticipated events is heavily dependent on “humans in the loop” for all autonomous vehicle domains. Dynamic mission planning that enables autonomous mission replanning to take into account unanticipated events is not common today, although capabilities on unmanned undersea vehicles (UUVs) have advanced the state of the art in this area. Dynamic mission-level planning is also a current thrust in the Office of Naval Research’s (ONR’s) Maritime Reconnaissance Demonstration (MRD) Program and its Intelligent Autonomy Program (e.g., the Risk-Aware, Mixed-Initiative Dynamic Replanning Program).

Some collaborative multivehicle planning development, at a low level of autonomy, has also been done in the past for unmanned aerial vehicles (UAVs) at ONR in the Uninhabited Combat Air Vehicle (UCAV) Demonstrations Program and at the Air Force Research Laboratory (AFRL) in the Cooperative Manned/ Unmanned Systems Program. Both programs used a single ground station to

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
×

control a team of UAVs that shared Global Positioning System (GPS) navigation solutions for route deconfliction.

Finally, the National Aeronautics and Space Administration’s (NASA’s) Remote Agent Experiment was executed for several days onboard the NASA Deep Space One mission,1 representing a significant demonstration of autonomy in space operations. This mission emphasized planning and decision capabilities to maintain the spacecraft in a desired internal state by planning time lines of activities, sequencing lower-level steps together to achieve higher-level goals, and executing plans in a reliable fashion. The system made use of probabilistic models of the subsystem hardware to detect and diagnose failures and replan the mission activities. Temporal planners, such as the Remote Agent Planner, can take hours to generate plans of large size unless hand-coded heuristics are provided, but alternatives are under development to improve searches for feasible time bounds of mission activities when generating mission time lines.

Sensing and Perception

Sensing and perception technology in today’s fielded systems is primarily used for AV navigation and avoidance of terrain hazards. Most AVs employ GPS-aided inertial navigation systems, although UUVs also employ Doppler velocity logs or other velocity correction sensors to aid the inertial system for navigation. Terrain sensing—using sonar for UUV bottom following and unmanned ground vehicle (UGV) behaviors such as wall following or road following—is also in use today. Cruise missiles employ terrain-matching and scene-matching technology that may have application for some UAV missions.

Obstacle-detection technologies have also been a research focus over the past decade, with emphasis on AV operations in complex terrain. This capability is particularly important for off-road UGV operations, littoral UUV operations, urban environment UAV operations, and undercanopy UAV applications. Obstacle-detection systems use a variety of sensors, including electro-optic cameras (stereo and mono), infrared cameras, ultrawideband radars, sonars, and light detection and ranging (LIDAR). The ONR Maritime Reconnaissance Demonstration Program is using bathymetry maps and forward-looking sonar to perform obstacle avoidance. The Defense Advanced Research Projects Agency (DARPA)/Army Demonstration III Program employed LIDAR and stereo cameras to build a three-dimensional map of the vehicle’s immediate surroundings, which was then used to plan local paths that move toward a goal while avoiding the obstacles.

Autonomous systems that detect, classify, and identify targets or threats are limited primarily to the UUV domain, although manned aircraft also include

1  

For further information, see the Web site <http://nmp.jpl.nasa.gov/ds1/>. Last accessed on April 5,

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
×

technologies to support the pilot that could be utilized for UAVs. The creation of situation-awareness maps is also rare today, except in UUVs used for mapping the location of underwater mines, which was done in the mid-1990s in the DARPA Autonomous Minehunting and Mapping Technologies Program and is a part of the Remote Environmental Monitoring Unit System (REMUS), Remote Minehunting System (RMS), and Long-range Mine Reconnaissance System (LMRS). The ONR Maritime Reconnaissance Demonstration Program (part of the Autonomous Operations Future Naval Capability (FNC)) is using a situation-awareness sensor suite, including communications intelligence (COMINT), electronic intelligence (ELINT), and video to detect, map, and avoid surface threats. A Virginia-class submarine (VSSN) provides mission command and control for the UUV. The MRD UUV transmits the threat type, location, and bearing to the VSSN, which provides the new threat information to update the battlegroup’s common operational picture. The VSSN also provides target-identification objectives to the MRD UUV for searching out and verifying surface targets. This capability was demonstrated in April 2003 during Fleet Battle Experiment Kilo.

Much work has been done and is still ongoing in the area of automatic target-recognition and threat-detection systems. Many techniques have been explored for a variety of sensors, but most methods are limited in their capability owing to unfavorable lighting conditions, weather, and viewing geometry, or obscurations such as foliage or terrain. Still, it is likely that some of this research will be used to field automatic target-cueing systems in the near term. These systems will not likely be fully autonomous, but will help either to increase operations tempo or to reduce operator workload.

Monitoring and Diagnosis

As described above, monitoring and diagnosis systems are used to detect and isolate failures within AV subsystems. The monitoring and diagnosis systems in use today primarily employ built-in test equipment to sense the malfunctioning of subsystems and equipment. This information is generally used for diagnostics and maintenance support, but is also infrequently used to support the reconfiguration of the autonomous system or the replanning of the mission, particularly in UUVs. System reconfiguration and mission replanning typically require redundant systems to be available onboard the AV. Some UUVs today also make use of triplex or quad-redundant, fault-tolerant computers that choose among input and output signals to detect and isolate failures. This technology, more common in manned systems, is infrequently used today for autonomous vehicles. DARPA’s Autonomous Minehunting and Mapping Technologies Program was an example of the use of quad-redundant, fault-tolerant computing in a UUV.

Analytical redundancy—which makes use of mathematical models of hardware subsystems to provide estimates of the expected sensor measurements or

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
×

vehicle responses for failure detection and isolation—is employed in manned systems, but is infrequently used in autonomous vehicles today.

Networking and Collaboration

Most of today’s AVs do not directly or autonomously collaborate with other manned or unmanned vehicles. Those that do primarily exchange navigation state to permit collision avoidance with other vehicles and often do so through ground control stations with human intervention. Collaboration among vehicles is largely accomplished by the operators controlling the mission.

Research on networking and collaboration for AVs has increased in recent years, with programs such as DARPA’s Mobile Autonomous Robot Software (MARS)2 and Software for Distributed Robotics (SDR).3 These programs are researching soft computing, initiative learning, coordinated control, and networking and communications autonomy technology to enable future collaborative robot capabilities.

LEVELS OF AUTONOMY

In order to classify systems for purposes of comparison, it is useful to identify the level of autonomy (LOA) that systems exhibit. Defining LOA in a simple, useable form has proven to be a difficult task. As yet, no single scale expressing LOAs has been found acceptable across the broad range of users. Intuitively, it seems that the mix of human and machine capabilities to be found in any particular system (or vehicle) implementation could be appropriately characterized by position along a linear axis with manual operation at one end and fully autonomous operation at the other. The many such attempts to define simple LOAs in this fashion have resulted in scales with differing numbers and definitions of the intermediate levels. These scales are summarized below, together with an expanded view of LOA as recommended by the committee.

Autonomy Scales Defined by the Department of Defense

One level-of-autonomy scale, created by the DARPA/U.S. Air Force (USAF)/Boeing X-45 program team, represents a rather high-level, broad-brush view of autonomy, with only four levels. This scale is presented in Box 3.1.

2  

For additional information, see the Web site <http://www.darpa.mil/ipto/programs/mars/vision.htm>. Last accessed on April 5, 2004.

3  

For additional information, see the Web site <http://www.darpa.mil/ipto/programs/sdr/vision.htm>. Last accessed on April 5, 2004.

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
×

BOX 3.1
Levels of Autonomy as Defined by the Uninhabited Combat Air Vehicle Program

Level 1 (Manual Operation)

  • The human operator directs and controls all mission functions.

  • The vehicle still flies autonomously.

Level 2 (Management by Consent)

  • The system automatically recommends actions for selected functions.

  • The system prompts the operator at key points for information or decisions.

  • Today’s autonomous vehicles operate at this level.

Level 3 (Management by Exception)

  • The system automatically executes mission-related functions when response times are too short for operator intervention.

  • The operator is alerted to function progress.

  • The operator may override or alter parameters and cancel or redirect actions within defined time lines.

  • Exceptions are brought to the operator’s attention for decisions.

Level 4 (Fully Autonomous)

  • The system automatically executes mission-related functions when response times are too short for operator intervention.

  • The operator is alerted to function progress.

Another, more detailed LOA scale, with 10 levels, was created by the Army for the Future Combat System (FCS) Program. That scale is shown in Table 3.1. Still other LOA scales similar to these have been created by other programs in connection with developing autonomy technology or autonomous vehicles. These include the Air Force’s autonomous control levels, which are defined for the observe-orient-decide-act (OODA) loop.4 The OODA loop defines different LOAs for each of the four primitive elements of closed-loop autonomy, namely—observe, orient, decide, and act.

The intermediate levels of one scale often seem to be unrelated to those of another, so a one-to-one correspondence between the levels defined by different scales is difficult to establish. The source of this confusion lies in the one-dimensional nature of most attempted definitions of LOAs, as well as in the

4  

For additional information, see the Web site <http://www.adtdl.army.mil/cgi-bin/atdl.dll/fm/6-0/appa.htm>. Last accessed on April 5, 2004.

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
×

differing focus of each of the groups defining the LOAs. The application of autonomy concepts and technology to a system is inherently a complex issue, with several degrees of freedom that must be addressed. Thus, it is impossible to characterize the implemented degree of autonomy completely with a single number.

An Expanded View of Level of Autonomy

The main expectation for Navy and Marine Corps autonomous vehicles is that they be able to carry out mission goals reliably, effectively, and affordably with an appropriate level of independence from human involvement. However, in practice it is difficult to assign a single level of autonomy to any AV. This is largely because AVs and their controlling systems are designed to perform complex missions made up of many activities, each of which may be implemented with a different level of autonomy. This fact implies that the notion of complexity must also be considered when assigning an LOA to an AV.

This section proposes a new view of level of autonomy, which is hereafter called the level of mission autonomy. As described below, mission autonomy is made up of two degrees of freedom—mission complexity and degree of autonomy. “Mission complexity” captures the number of functional mission capabilities inherent in any given system or the number of different mission activities that can be implemented by the system, independent of whether they are accomplished autonomously or not. “Degree of autonomy” captures the amount of autonomy used to implement any specific mission activity or functional capability.

Mission complexity, the first degree of freedom, is not to be confused with system complexity, which increases as the number and variety of system elements (e.g., vehicles, operators, processors, data links, sensors, databases, power bases, and so on) become greater and as the level of predictability of the system decreases. System complexity results, in part, from the selection of mission autonomy requirements.

To further elaborate on mission complexity, it is useful to view it in the context of an autonomous vehicle mission. A mission is a hierarchical collection of mission activities that are sequenced to accomplish mission goals. High-level activities (i.e., mission phases such as launch, ingress, operations, egress, and recovery) are broken down into subordinate activities, which are themselves further decomposed into primitive activities. Each mission activity can be accomplished by a different mix of human and/or machine collaboration. The human involvement in the mission can be categorized in terms of control and authorization, coordination, and intelligence, as the examples in Box 3.2 suggest.

The number of mission activity levels (e.g., high, medium, low), the number of mission activities within each level, and the degree of human-equivalent functionality (e.g., intelligence) required for each are design choices that, once made, define the complexity of the AV itself. Mission complexity is then characterized by the number of functional mission capabilities that can be performed by the

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
×

TABLE 3.1 Levels of Autonomy in the Army Scale for the Future Combat System

Level

Level Description

Observation Perception and Situation Awareness

Decision-Making Ability

Capability

Example

1

Remote control

Driving sensors

None

Remote operator steering commands

Basic teleoperation

2

Remote control with vehicle state knowledge

Local pose

Reporting of basic health and state of vehicle

Remote operator steering commands, using vehicle state knowledge

Teleoperation with operator knowledge of vehicle pose situation awareness

3

External preplanned mission

World model database—basic perception

Autonomous Navigation System (ANS)-commanded steering based on externally planned path

Basic path following, with operator help

Close path following intelligent teleoperation

4

Knowledge of local and planned path environment

Perception sensor suite

Local plan/replan—world model correlation with local perception

Robust leader-follower with operator help

Remote path following—convoying

5

Hazard avoidance or negotiation

Local perception correlated with world model database

Path planning based on hazard estimation

Basic open and rolling semiautonomous navigation, with significant operator intervention

Basic open and rolling terrain

6

Object detection, recognition, avoidance or negotiation

Local perception and world model database

Planning and negotiation of complex terrain and objects

Open, rolling terrain with obstacle negotiation, limited mobility speed, with some operator help

Robust, open, rolling terrain with obstacle negotiation

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
×

7

Fusion of local sensors and data

Local sensor fusion

Robust planning and negotiation of complex terrain, environmental conditions, hazards, and objects

Complex terrain with obstacle negotiation, limited mobility speed, and some operator help

Basic complex terrain

8

Cooperative operations

Data fusion of similar data among cooperative vehicles (such as UAVs)

Advanced decisions based on shared data from other similar vehicles

Robust, complex terrain with full mobility and speed. Autonomous coordinated group accomplishments of ANS goals with supervision

Robust, coordinated ANS operations in complex terrain

9

Collaborative operations

Fusion of ANS and reconnaissance, surveillance, and target acquisition (RSTA) information among operational-force UGVs

Collaborative reasoning, planning, and execution

Accomplishment of mission objectives through collaborative planning and execution, with operator oversight

Autonomous mission accomplishment with differing individual goals and little supervision

10

Full autonomy

Data fusion from all participating battlefield assets

Total independence to plan and implement to meet defined objectives

Accomplishment of mission objectives through collaborative planning and execution, with operator oversight

Fully autonomous mission accomplishment with no supervision

SOURCE: LTC Warren O’Donell, USA, Office of the Assistant Secretary of the Navy (Acquisition, Logistics, and Technology), “Future Combat Systems Review,” presentation to the committee, April 25, 2003.

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
×

BOX 3.2
Examples of Human Performance Capabilities in Autonomous Vehicle Missions

Control and Authorization

  • Authorize activities

  • Provide tasking orders

  • Control the autonomous vehicle’s path

  • Monitor the autonomous vehicle’s systems

  • Disseminate information to users

Coordination

  • Manage resources (i.e., vehicles, consumables, sensors)

  • Generate time lines

  • Generate subordinate tasking orders

  • Communicate subsystem failures

Intelligence

  • Interpret and exploit sensor data

  • Develop situation awareness

  • Plan mission activities

  • Diagnose system failures

combined human-machine system. Functional capability is an amalgamation of human-machine capabilities embodied in the sensing, processing, ground system, and human operator/pilot capabilities. Examples of functional capability include launch, threat response, terrain following, weather avoidance, target search, target prosecution, and formation flight, to name just a few. Systems that are capable of implementing more functional mission capabilities (whether autonomous or not) are said to be more complex.

The second degree of freedom, which is largely independent of the first, is the degree of autonomy to be implemented for each of the mission activities. The degree of autonomy implemented at each mission level or in each activity can be chosen from a range of possibilities—from complete dependence on the human to complete independence from the human. Between these extremes, the degree of autonomy to be implemented is a design choice, subject to standard design trade-offs of such factors as performance, cost, and supportability.

It is clear then that no single number can precisely characterize the total autonomy content of the system implementation. As a result, the current Department of Defense (DOD)-defined autonomy scales are at best qualitative, and strongly dependent on the aspect of the mission (and system) that has been chosen as a focus.

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
×

Today’s Autonomous Vehicles

It is useful to consider the current status of existing AV systems plotted against the two orthogonal axes of degree of autonomy and mission complexity. Together, these two degrees of freedom represent the level of mission autonomy for an AV as discussed in the preceding section. The axis labeled “degree of autonomy” (or percentage time without operator intervention) can be thought of as roughly representing the percentage of required mission capabilities that are handled by the system itself without direct, real-time human interaction. Accordingly, this axis is labeled with percentages—0 percent represents a situation in which the human has total control of all aspects,5 while 100 percent represents the totally autonomous, completely hands-off system with no human real-time control or interactions at all. For the axis labeled “mission complexity” (involves more uncertainty, requires more system adaptability), a highly simplified scale is used, with three bins representing the main levels (low, medium, and high). With these crude definitions, the parameters for several well-known current AV systems were estimated and are plotted on Figure 3.2. Also indicated are two examples of the extreme possibilities—a manned fighter aircraft, which has high mission complexity with a small amount of autonomous functionality at the lowest levels (e.g., autopilot), and a thermostat that is 100 percent autonomous but which performs only a very simple task.

It is interesting to note that several of the AVs currently developed or under development (i.e., Dragon Runner, Predator Fire Scout, Global Hawk, LMRS, multi-reconfigurable UUV (MRUUV), and so on) fall closely along a trend line suggesting almost 100 percent correlation of these two variables. That is, the more autonomy utilized, the more challenging the task (i.e., complexity) that can be undertaken, or vice versa. This observation suggests that current AV design practice is not treating the LOA as a design parameter to be traded off against various system performance criteria. Rather, it would appear that LOA is being interpreted as is implied by the several one-dimensional scales of autonomy, which assume precisely the correlation seen in Figure 3.2—that is, the more “autonomous” the system the more complicated the tasks it performs. It seems that the several one-dimensional scales of LOAs defined to date are in fact defined not along the “autonomy” axis, as suggested by the name “levels of autonomy,” but more or less along the 45° line in the autonomy-complexity plane. This represents an implicit design choice that is probably not explicitly recognized by the AV development teams. Moving off this artificially constrained design path in the autonomy-complexity plane opens up a broad range of design

5  

Somewhat unrealistic, as all hardware implementations have some low-level components that operate “automatically.”

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
×

FIGURE 3.2 Mission autonomy in today’s autonomous vehicles (AVs), based on estimated parameters for several current AV systems and two extreme possibilities (manned fighter and thermostat). NOTE: A list of acronyms is provided in Appendix D.

options that could greatly affect the overall merits of the final system implementation.

Figure 3.3 plots mission autonomy versus system complexity. “Mission autonomy” is defined here to be the product of mission complexity and the degree of autonomy, placed on a scale from 1 to 10, with 10 being a notional maximum level of mission autonomy. AVs with a high level of mission autonomy are those that simultaneously have a high mission complexity and a high degree of autonomy. It is apparent from this figure that for today’s autonomous systems, higher mission autonomy typically results in higher system complexity. There are two reasons for this. First, autonomy capability often is distributed throughout the system and offboard the AV platforms. Second, the perception is that a higher level of autonomy results in less system predictability, which results in added complexity to provide more human oversight. It is expected that higher levels of mission autonomy will actually result in lower system complexity in future systems, as confidence in autonomy capability increases and as more autonomy capability is migrated onboard autonomous vehicles. An indication of such changes can be seen by the fact that UUVs tend to have less system complexity than UAVs have for the same level of mission autonomy. This is due in part to the higher degree of onboard

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
×

FIGURE 3.3 Relationship between mission autonomy and system complexity. “Mission autonomy” is defined here to be the product of mission complexity and the degree of autonomy, placed on a scale from 1 to 10, with 10 as the notional maximum. NOTE: A list of acronyms is provided in Appendix D.

autonomy required by UUVs to operate in the absence of communications with an operator. Operational speed and desired response time/consequences of failure would appear to result in increased complexity as well.

USING LEVEL OF MISSION AUTONOMY AS A DESIGN CHOICE

Autonomous vehicles have the potential to increase U.S. military operational capability significantly. They will become an even more important element of our warfighting capability in the future. As discussed in some depth below, advances in autonomy capability are the key to providing this enhanced warfighting capability. These advances will improve the mission effectiveness and affordability of these systems and increase their ability to survive in hostile, threat-dense environments. To realize the payback of increased autonomy, the Navy and Marine Corps can take aggressive steps to make this evolving capability integral to their future. This effort begins with taking the view that level of mission autonomy is a design choice that can be leveraged in up-front system

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
×

trade-off studies to impact mission effectiveness, vehicle survivability, and system affordability. This section expands on this view and discusses some promising autonomy technology for the near future and some shortfalls in autonomy capability that will ultimately be needed to enable the Naval Services vision for Sea Power 21.

Trade-off Studies on Autonomous Vehicle Systems

The level of mission autonomy (as defined above in the subsection entitled “An Expanded View of Level of Autonomy”) is a design choice that, when exploited through system trade-off studies, can be used to evaluate the pros and cons of various concepts of operation. This evaluation can be made by comparing the operational capability provided by one level of mission autonomy versus that provided by another. The incorporation of level of mission autonomy in the design trade space with other, more traditional design choices (e.g., vehicle performance, range, endurance, stealth, and shipboard operations) allows system designers to compare the relative merits of various levels of vehicle capability having various levels of autonomy. This comparison is done in terms of the ability of each to achieve the overall desired operational capability or to enable new capabilities. It should be emphasized that by including autonomy capability in the trade space, it is possible that the best mission capability, for a given cost of ownership, will be achieved through a high level of mission autonomy but with a modest vehicle capability. Such trade-off studies will be extremely useful to Navy and Marine Corps requirements developers and program managers in conceptualizing highly capable, yet cost-constrained, autonomous systems during program development. This approach allows the Navy to methodically sort the surfeit of available or emerging autonomy technologies in order to focus on developing beneficial system-level autonomy capabilities that result from the integration of a number of fundamental autonomy technologies.

Figure 3.4 provides an illustration of a trade-off study methodology for incorporating level of mission autonomy as a design choice. The methodology can be viewed as an iterative evaluation of concept of operations (CONOPS) and top-level mission and system requirements, given different design choices. The operational capabilities, enabled by a set of system design choices, are subsequently used to adjust the design choices, CONOPS, and requirements. Operational capabilities are metrics associated with the key goals of the program and might include, for example, items such as the number of targets detected and identified, the number of targets prosecuted, the probability of vehicle survivability against various threats, and the total cost of ownership for the system (i.e., nonrecurring development cost plus operations and support cost).

The vertical integration of autonomy for the AV’s command-and-control system (C2S), mission management system (MMS), and vehicle management

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
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FIGURE 3.4 A trade-off study methodology incorporating level of mission autonomy as a design choice. NOTE: C2S, command-and-control system; MMS, mission management system; VMS, vehicle management system.

system (VMS) should be considered in this process. Vertical integration of autonomy for all levels of the system is particularly important if agile response to rapidly changing conditions is needed so as to achieve the desired operational capability. Vertical integration permits such things as route deconfliction with other vehicles operating in the same space, efficient exploitation and prosecution of targets of opportunity, and rapid response to system failures that impact mission objectives. It also permits retasking of the AV to accomplish new, higher-priority objectives, and it helps reduce “friendly fire” incidents through the better coordination of all controlled assets within the battlespace, including AVs.

The design of autonomous systems is traditionally accomplished by trading vehicle capability with subsystem capability to produce the desired mission or system capability for the given CONOPS and mission requirements. To fully realize the benefits described above, autonomy capability must become a part of this trade-off process.

The selection of autonomy capability associated with an autonomous system is intertwined with the selection of subsystem capability and vehicle capability.

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
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For example, high levels of autonomy drive up processing, sensing, and monitoring requirements, while they relax communications requirements and needs for operating and support personnel. Conversely, low levels of autonomy drive communications requirements. The distribution of autonomy, both offboard and onboard the vehicle, is another degree of freedom often used by designers to mitigate constraints due to processing limitations, but this distribution drives communications system requirements.

Another key factor in the selection of autonomy capability is the complexity of the operating environment for the AV. Mission operations in complex environments (e.g., urban environments, under tree canopy, in littoral waters, or in forested regions) often require a high degree of autonomy because communications in such environments are intermittent at best. High levels of vehicle capability (e.g., sensing, perception, agility) may also be required in order to permit operations in these environments. This combination of high vehicle capability with high degree of autonomy makes the development of autonomous systems for these environments very challenging.

A final, additional factor in the selection of autonomy capability is the concern of robustness to the unanticipated events inherent in complex autonomous systems. Also of concern are emergent behaviors or system behaviors that unexpectedly occur during the execution of a mission owing to an implemented, autonomous decision-making capability.

Impacting Mission and Vehicle Characteristics

The primary value of autonomy—performing military missions without risking human life—hardly needs debate. But the use of autonomy has other benefits, too. The most obvious of these, which are discussed in more detail below, include faster response times for planning, decision making, perception, and diagnosis; and a lower overall labor cost for operations.

The goal of the trade-off study suggested in the previous section is to design an autonomous system with operational capabilities that enhance mission effectiveness, improve vehicle survivability, and reduce the total cost of ownership. It has long been accepted that parameters representing vehicle and subsystem capability can be traded so as to impact these three metrics. As shown in the following subsections, several key drivers associated with autonomy capability also have an influence on these metrics and therefore can be made part of the overall system design trade-offs.

Mission Effectiveness

Selecting higher levels of mission autonomy can enhance the overall mission effectiveness of an autonomous vehicle. The level of mission autonomy is a

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
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“knob” that can be used to tune several key drivers, each of which directly influences mission effectiveness. Some examples of key drivers are as follows:

  • Time to plan and replan mission activities;

  • Time to assimilate and correctly interpret onboard and offboard sensor information;

  • Time to assimilate and correctly interpret command-and-control sensor information;

  • Time to detect, isolate, and correctly assess the impact of system problems; and

  • Distribution of mission objectives and tasks among collaborators.

The time that it takes to plan and replan mission activities owing to mismodeled or unmodeled system dynamics, system failures, pop-up threats, or other unanticipated events directly impacts the number of mission objectives that can be achieved in a given amount of mission time. Similarly, the time needed to assimilate and interpret onboard and offboard sensor data to create situation awareness directly impacts the number of achievable mission objectives. Overall, higher levels of autonomy support faster, closed-loop, dynamic planning cycles, which are composed of the closed-loop process of sensing, estimation, interpretation, and replanning. Faster dynamic planning cycles allow more mission objectives to be accomplished for a given vehicle endurance (however, the autonomy technology to enable this vision is not in place now). Low-endurance vehicles with fast planning cycles (a high level of autonomy) can be as effective as high-endurance vehicles with slow planning cycles (a low level of autonomy).

The probability of mission success is also determined by the ability of the system to adapt to system failures by detecting, isolating, and correctly assessing the impact of system problems on the mission. Reconfiguration of a redundant system can accommodate system failures, but it will result in lower system reliability and may result in degraded performance. The impact of both must be weighed, and a decision must be made about whether to continue the mission under such circumstances. The faster this decision can be made, the higher the overall probability of mission success for a given AV and the more effective the mission will be in terms of the number of objectives accomplished.

Finally, the distribution of autonomy among collaborators adds redundancy to the system, enables the redistribution of mission roles and objectives when system failures occur, and increases the number of mission objectives that can be achieved. Combined, these capabilities increase the probability of mission success.

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
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Vehicle Survivability

Selecting higher levels of autonomy can improve autonomous vehicle survivability and provide better overall knowledge of the system’s health. The level of mission autonomy is a design choice that can be used to tune several key drivers, each of which directly influences vehicle survivability. Following are some examples of key drivers:

  • Time to assimilate and correctly interpret onboard threat information;

  • Time to assimilate and correctly interpret command-and-control threat information;

  • Time to assimilate and correctly interpret collaborator threat information;

  • Time to plan the response to threats;

  • Time to detect, isolate, and correctly assess the impact of system problems;

  • Time to plan the response to system problems;

  • The frequency and duration of communications; and

  • Increased requirement for sensing and processing.

The speed of response of an AV to threats is a key to its survivability. The AV must detect, identify, classify, and then plan a response tactic to the threat. Every step that requires operator involvement through communications will slow the speed of response and increase the likelihood that the vehicle will be lost. The tactic employed will sometimes depend on the threat stage—that is, on whether the threat is in search mode, or in tracking mode, or has already engaged the AV with a weapon. Prompt early detection and classification allow a wider range of response tactics to be employed and a higher probability of the AV’s surviving the threat. Threat awareness, and hence vehicle survivability, is further enhanced by the number of sources providing threat information to the AV. Threat awareness can be greatly improved if the AV can pull and assimilate threat information from its command-and-control network or from collaborating vehicles. When an AV does not have its own threat-detection equipment, collaborating vehicles can provide this threat awareness. System trade-off studies could evaluate concepts of operation that assume a distribution of autonomy among collaborating vehicles, since this may be the most cost-effective approach to implementing a particular mission capability.

The probability of the loss of a vehicle is directly impacted by the overall reliability of the autonomous system. System reliability is a dynamic metric determined by the probability of system failure occurrence (the failure rate), the probability of detecting and isolating a system failure when it occurs (the coverage rate), and the ability to accommodate the failure through reconfiguration. System failure rates can be lowered through system architecture design, the use of higher-quality parts, and changing out degraded subsystems or components detected through

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
×

vehicle health monitoring and prognostication technology (requiring a higher level of autonomy). Coverage rates can be improved through the use of higher levels of autonomy that make use of analytical redundancy (e.g., hypothesis testing, detection filtering, and estimation). Autonomous reconfiguration of a redundant system can accommodate system failures if those failures can be quickly detected and isolated. Overall, rapid failure detection, isolation, and accommodation increase vehicle survivability, as does the ability to autonomously monitor and predict future failures or the need for subsystem maintenance.

Finally, higher levels of autonomy mean less-frequent and shorter-duration communications between the operators and the AVs they control. The result is a reduction in overall signature, allowing the AVs to operate more covertly.

The improvements gained in mission effectiveness and vehicle survivability through increased levels of autonomy come at the expense of increased sensing and processing requirements. These requirements may indirectly reduce the vehicle’s survivability owing to lower vehicle performance, a larger visible signature, and a larger radar cross-section. This problem highlights the need to include mission workload, subsystem, and vehicle models in the up-front trade-off studies. The distribution of autonomy through collaboration and networking reduces the sensing and processing requirements for any given vehicle, which reduces the impact of these indirect influences on vehicle survivability. This approach is analogous to the wingman or fighter escort approach used for some manned aircraft missions.

System Affordability

Selecting higher levels of mission autonomy can reduce the total cost of ownership for an autonomous vehicle. The level of mission autonomy can be used to optimize several key drivers, each of which directly influences system affordability through reduced costs for life-cycle operations and support (O&S). However, increased autonomy comes with an attendant increase in the cost of system development. This latter cost must be weighed in system trade-offs against the reduced O&S costs when selecting a level of mission autonomy, as shown in Figure 3.5. In this figure, it is assumed that as levels of autonomy increase, there is a diminishing effect on their ability to reduce O&S cost, while these same levels of mission autonomy come with an increasing rate of development cost (including science and technology investments). The specific shapes of these curves will be domain-, mission-, and system-dependent, however, and therefore are an important element of the system trade-offs. Nonetheless, there is some optimal level of autonomy for each mission scenario.

Some examples of the key autonomy drivers affecting system affordability are as follows:

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
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FIGURE 3.5 An example of the trade-offs of development cost versus life-cycle operations and support (O&S) cost for an autonomous vehicle.

  • Improved mission effectiveness,

  • Improved vehicle survivability and system reliability,

  • Reduced requirement for operator and command-and-control support,

  • Reduced requirement for maintenance support, and

  • Increased system development cost.

Improved mission effectiveness will improve the unit cost per mission objective achieved (e.g., the cost per target detected or destroyed), although this cost must be traded against the increased development cost to achieve the improved mission effectiveness. Improved vehicle survivability reduces the number of vehicles to be procured or the rate of vehicle production. This difference will need to be balanced by the expected attrition rate of the AV, since it may be called on to operate in higher-risk operations than manned vehicles would be.

The increases in level of mission autonomy that were mentioned previously as a way to improve mission effectiveness and vehicle survivability also reduce operator and maintenance staff workloads and therefore reduce the overall O&S cost for the system, although the training cost element for operators and maintenance may increase. Even given these considerations, the level of improvements

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
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indicated are not the only reasons to increase autonomy capability. Higher levels of autonomy will in general reduce O&S costs for sensor interpretation, system-failure monitoring, problem diagnosis, and mission planning, even when the increased autonomy does not impact mission effectiveness or vehicle survivability. Similarly, higher levels of mission autonomy for system health monitoring reduce the maintenance and support staff workload needed to achieve a given level of system reliability. More capable AVs also reduce the workload needed of their command-and-control systems.

Finally, increased levels of autonomy may result in increased costs for system development and training, or they may simply result in a redistribution of cost from vehicle development to autonomy subsystem development. As noted above: It should be emphasized that by including autonomy capability in the trade space, it is possible that the best mission capability, for a given cost of ownership, will be achieved through a high level of mission autonomy, but with a modest vehicle capability. A corollary to this statement is that no more autonomy need be included than that required to do the task: for example—a cruise missile is smart enough to do its job.

AUTONOMY TECHNOLOGY

It is a daunting challenge for a vehicle to operate autonomously in a complex, threat-filled environment. The vehicle must be able to form plans to achieve its goals, plan its motion so as to reach objectives while avoiding threats, sense its environment in order to detect unanticipated threats and opportunities and respond to them in a timely fashion, monitor its own actions to make sure that its plans are in fact making progress toward its goals, monitor the health status and capabilities of its subsystems, and modify its plans when unanticipated events occur. Ideally, the AV must be capable of interacting collaboratively with other vehicles, human commanders, and command-and-control systems.

This section explores promising autonomy technology currently under development within the DOD and identifies the key technologies needed to achieve the DOD’s vision as expressed in the 2001 Quadrennial Defense Review6 and in the Navy’s Sea Power 21.7 Achieving the operational goals comprising these visions will depend upon several key operational capabilities, each of which requires advancements in autonomy capability to fully enable the attainment of the visions. These capabilities include the following:

6  

Donald H. Rumsfeld, Secretary of Defense. 2001. Quadrennial Defense Review Report, U.S. Government Printing Office, Washington, D.C., September 30. Available online at <http://www.defenselink.mil/pubs/qdr2001.pdf>. Accessed on May 13, 2005.

7  

ADM Vern Clark, USN. 2002. “Sea Power 21: Projecting Decisive Joint Capabilities,” U.S. Naval Institute Proceedings, Vol. 128, No. 10, pp. 32-41.

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
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  • AV shipboard operations (e.g., UUV or unmanned surface vehicle (USV) launch and recovery, UAV parking onboard carriers);

  • AV operations in threat-dense environments;

  • AV operations in complex terrain (e.g., in urban, forested, and littoral areas);

  • Multimission AV operations (e.g., changing mission objective or mission type);

  • Autonomous collaboration of AVs with other manned and unmanned vehicles;

  • Autonomous operations of AVs with noncollaborating vehicles in shared space;

  • Autonomous target acquisition and engagement by AVs; and

  • Tight integration of AVs with command-and-control systems (e.g., a vertically integrated command-and-control, mission management, and vehicle management software structure).

Most of the technologies required for full autonomy in these operational capabilities are not yet fully mature, and many are still reasonably far in the future. The following subsection describes some of the promising autonomy technologies under development today throughout the DOD to enable these key operational capabilities. The subsection entitled “Key Shortfalls in Autonomy Capability” then addresses the matter of where more intensive technology development may be warranted, given the relative value in achieving the overall vision.

Promising Technologies for the Future

Over the past 30 years, many DOD programs have developed and matured autonomy technologies too numerous to discuss in detail in this report. More recently, ONR’s Autonomous Operations FNC initiated a four-pronged autonomy technology effort.8 It includes the development of autonomy technology to be transitioned to the fleet for UAVs, UUVs, and UGVs, as well as the development of general-purpose autonomy technology under the Intelligent Autonomy Program. The UAV, UUV, and UGV domain efforts are primarily focused on vehicles, sensors, and sensor data processing technologies, with emphasis on transitioning those that are mature. It is important to emphasize sensor interpretation technology for scene interpretation (local terrain and other environment modeling) and for threat detection and identification, because as more autonomous functions are used for mission planning and collaborative control, the more automatic the sensor interpretation must be. The Intelligent Autonomy Program is focused on developing general-purpose autonomy technology for air, land, and

8  

For additional information, see the Web site <http://www.onr.navy.mil/fncs/auto_ops/>. Last accessed on May 18, 2004.

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
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undersea systems. Many of these technologies have matured to the extent that they can enable new capabilities in Naval Services autonomous vehicles. The sampling of six technologies listed here, which are believed to have applicability in multiple autonomous vehicle domains (e.g., air, land, surface, and undersea), are discussed in the subsections below:

  • Dynamic real-time mission planning and replanning,

  • Simultaneous localization and mapping (SLAM),

  • Threat detection and identification,

  • Analytical redundancy and failure-detection filtering,

  • Supervised learning and adaptation/learning technology, and

  • Human-machine collaborative decision making.

Dynamic Real-Time Mission Planning and Replanning

The improvement of mission effectiveness, vehicle survivability, and system affordability for Naval Services autonomous vehicles will result in an increase in the number of functional mission capabilities (increased mission complexity) to be implemented in an AV and an increase in the degree of autonomy implemented in each. This increase in level of mission autonomy, coupled with an increase in environmental complexity (e.g., in threat-dense environments), drives the need for more agile and dynamic mission planning if operational tempo is not to be compromised. Dynamic mission-planning capabilities will be needed to autonomously generate time lines for mission activities, to handle failures and their possible impacts on other activities, to accommodate uncertainty in the description of the threats, to manage resources and consumables, and to plan mission activities collaboratively with other vehicles. Several new technologies are becoming available to help deal with this increasing mission complexity as described below. Moving this dynamic mission-planning capability onboard AVs will also reduce overall system complexity.

Conventional premission batch planning systems (e.g., the Tactical Aircraft Mission Planning System, Portable Flight Planning System, and Air Force Mission Support System) have the downside of slow operational tempo—that is, slow planning cycles with heavy human involvement to dynamically accommodate uncertainty or unanticipated events in complex systems. More recently, software frameworks for real-time planning systems have been developed to manage the complexity associated with a hierarchy of mission activities autonomously and dynamically, removing the burden from the human operators and mission planners. These software frameworks provide an application programming interface for dynamic, closed-loop planning of mission activities to generate activity time lines subject to constraints, accommodate perturbations in the plan owing to model uncertainties, manage failures and their impacts on other

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
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activities, negotiate resources with subordinate activities, and plan operations for aborting missions or removing vehicles to safety. These frameworks are then populated by planning, monitoring, and problem-diagnosis algorithms for each specific mission activity. Integration of these new software frameworks with conventional mission-planning systems will enable increases in operational tempo while reducing the burden on and size of the mission-planning and operations workforce.

The specific planning algorithms used within the software framework depend on the planning problem to be solved. Planning problems are traditionally optimization or classic branch and bound search problems. More recently, techniques have been developed for recasting planning problems as constraint satisfaction problems (CSPs) or for using composite variables to transform the optimization problem into a mathematical description of the operator’s intent. A family of extremely fast CSP solution algorithms has been developed that, when carefully handcrafted, can provide solution times for even relatively complex planning problems within reactive time frames.9

Hierarchical task net planning is another important planning technology, which develops the plan through hierarchical refinement. At each level of the process, a plan capable of achieving the goal is retrieved from a library of existing plans. This plan is only partially refined—some of the substeps are primitive operators, but others are merely represented as subgoals to be achieved by further planning. Hybrid approaches are also possible: in particular, it is possible for a human to develop or select the higher levels of a plan while relying on computational techniques to transform the remaining subgoals into fully elaborated plans either at planning or execution time. “Reactive programming languages” have been developed as a means to express such higher-level plans.

More recently, robust hybrid automatons have been developed that make use of an algorithm that permits real-time generation of complex paths from a basic set of offline-generated agile maneuvers. The complex path is then generated online using an optimal solver that pieces together the required path, subject to a set of path constraints. This technique is used for obstacle avoidance when extremely fast reaction times are required of the system; it is especially useful for AV operations in complex environments.

9  

A. López-Ortiz, C. Quimper, J. Tromp, and P. van Beek. 2003. “A Fast and Simple Algorithm for Bounds Consistency of the Alldifferent Constraint,” Paper #310, presented at the 18th International Joint Conference on Artificial Intelligence, Acapulco, Mexico, August.

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
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Simultaneous Localization and Mapping

Navigation in GPS-denied environments has received considerable attention in recent years in order to improve the navigation of UGVs in most operating environments, that of UAVs in urban environments or under tree canopy, and that of UUVs in littoral waters (in Chapter 5, see the section entitled “Naval Operational Needs and Technology Issues,” for UUVs). Sophisticated processing means are becoming popular for combining the functions of navigation and mapping to improve the quality of both. SLAM is a technique by which terrain objects or topography are entered into a map at the same time that the position and orientation of the vehicle is being estimated in those same map coordinates. A crucial effect of this technique is that when a piece of terrain (e.g., a feature or object) is seen again after the vehicle has moved significantly, the system performs a correlation between the old observations and the new, giving simultaneously a tremendous improvement in the map accuracy and in the vehicle navigation state. Such techniques can give highly accurate estimates of vehicle position and terrain topography. Furthermore, cooperative execution of such algorithms by multiple vehicles sharing a common data structure can quickly produce high-quality maps and localizations for all of the vehicles. This technique has been applied in relatively structured environments (e.g., inside buildings or tunnels), where features are noncomplex and easily recognized, using LIDAR, sonar, and vision sensor systems. This technology is less mature for operations in unstructured environments where features or map objects are of various shapes and sizes.

Most of the SLAM work to date has used commercial off-the-self sensors and focused on algorithm and software development. But in most cases the sensors involved are not in a form suitable for fielding. Thus, there is a significant gap in sensor development, particularly for intelligent autonomy for small UGVs. The Army Research Laboratory’s Collaborative Technology Alliances Program is funding sensors germane to vehicles the size of the FCS Multifunction Utility Logistics Equipment (MULE) vehicle or larger.10

Threat Detection and Identification

As autonomous vehicles become more accepted, they will be called on to operate in more threat-dense environments. Real-time capability for threat detection and identification will be required for AV operations in these environments. Today, manned aircraft, surface ships, and submarines make use of threat radars, electro-optical (EO) and infrared (IR) sensing, and COMINT signal processing to detect and identify adversary threats and threat types. Many of these technologies

10  

For additional information, see the Web site <http://www.arl.army.mil/alliances/Default.htm>. Last accessed on May 18, 2004.

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
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are transferable to AVs to enable operations in threat-dense environments. In order to operate on an AV, these systems will need to be augmented by planners for threat-response tactics that take the place of the pilots or operators to implement one of various strategies in response to a threat. Also, in many cases the sensors used on large, manned vehicles will be too big for AVs, or the ability for autonomous threat detection with high probability of detection and low false alarm rate is not very mature. Thus, more work is needed in this area.

Analytical Redundancy and Failure-Detection Filtering

Conventional approaches to monitoring and diagnosis of vehicle systems include the use of hardware redundancy for failure detection and isolation using input-output voting schemes, midvalue selection, and built-in testing. These methods by their nature can substantially increase the weight of the Vehicle Management System and do not by themselves help determine the lost functionality within a subsystem or the mode of the system owing to the failure. The latter is critical for a dynamic planning system to be able to determine the right course of action following a failure.

Analytical redundancy, which makes use of mathematical models of hardware subsystems to provide estimates of the expected sensor measurements or vehicle responses, does not require redundant hardware and can be used to determine the lost functionality within the affected subsystem. Analytical redundancy provides estimates of the expected sensor measurements or vehicle responses through estimation of theoretical approaches developed beginning in the 1940s and 1950s (e.g., the Wiener filter and the Kalman-Bucy filter).

Failure detection and isolation using analytical redundancy employ estimation of theoretical technologies such as hypothesis testing, maximum-likelihood detection, generalized likelihood ratio tests, and robust estimation, to detect and isolate system failures. These methods use linear filters to generate residuals between a model of the system and the measurements being received from onboard sensors. A failure in the dynamic system can be detected as a change in one or more of the plant parameters, or input signals. These faults can correspond to failed actuators or sensors or to failures that cannot be assigned to any system components (e.g., a UUV getting caught in a net).

In detection filter design, the filter gain is chosen so that the residual vector has a different fixed direction for each hypothesized component failure. Hypothesis tests describe the expected response of the system to the no-failure case and to selected candidate failures. Ratios of probabilities of the various failures to the no-failure response are computed and compared to a threshold to detect and isolate failures. The generalized likelihood ratio test is a statistical test that looks for a change in the statistical properties of the filter to declare a failure of a specific type. Robust estimation approaches modify the filter gains to accommo-

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
×

date uncertainties in the mathematical description of the subsystem processes being used.

These methods have been developed and tested for UUVs and are in use today in aircraft-engine health-monitoring systems, commercial-airline diagnostics and prognostication systems, and the guidance, navigation, and control systems of spacecraft and military aircraft.

Supervised Learning and Adaptation/Learning Technology

Learning and adaptation technologies have applicability for autonomous vehicle control, mission planning, failure diagnosis, sensing and perception, and collaboration. These technologies have matured over the past two decades to the point of being a useful component technology to improve mission effectiveness for specific mission activities or to improve vehicle survivability for specific critical-failure scenarios. However, this technology has not matured to the extent that it should be viewed as a panacea for the accommodation of unanticipated events for all mission activities.

There are three primary categories of learning and adaptation technology: (1) model approximation, (2) supervised learning and adaptation, and (3) reinforcement learning. The technologies within the first and second categories are mature enough today to be used on a limited basis for specific AV functions if the overall mission effectiveness and vehicle survivability will truly benefit from the expanded capability. Technologies within the third category are not mature enough to be used in AVs today.

Model approximation (category 1) makes use of connectionist (learning) networks of radial basis functions, sigmoidal functions, or Gaussian functions to represent complex physical processes that are otherwise difficult to model. Model-referenced adaptive control systems make use of this technology to expand the operating space for vehicle control systems and reduce modeling complexity. Learning-based model approximation has been used to model such things as the nonlinear flight dynamics of aircraft for flight control, aircraft jet-engine combustion for failure detection and isolation, helicopter gearbox models for failure detection, and chemical propagation for the detection and tracking of underwater plumes. Learning-based model approximation has also been used to generate models within planning systems, for state estimators, or for analytical failure detection and isolation. These techniques are heavily supported by simulation data to provide the initial network training, and subsequently they are supported by experiential data collected during the AV’s operations.

The technique of supervised learning and adaptation uses a learning system in order to select the best (or a good) action to be implemented, given the current state of the system. The learning is said to be supervised since the selection of a good action uses a network trained through human supervision or simulation. The

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
×

network is trained by computing a value function. The value function is a complex mapping that represents the benefit to be derived by the implementation of each possible action for all possible system states. It can be a mathematical function (e.g., a weighted combination of the system states and possibly previous actions) or the subjective opinion of “goodness,” as determined by a human supervisor. The value function represents the benefit to be derived by implementation of all possible actions. Once the learning system has been trained via this supervision, the system has the ability to generate a good action given an arbitrary system state. Supervised learning systems have been applied to such things as AV controls, mission activity planning, and fault detection and isolation.

The technique of reinforcement learning and adaptation is the most difficult and by far the least mature at this stage of development. Reinforcement learning systems are systems capable of learning without access to an a priori provided value function. In this case, the system must learn the value function “on-the-fly,” which requires that trial actions be explored for the inputs that currently exist, and then be quickly evaluated for “goodness.” Many techniques have been developed for this purpose, including Q-Learning and neuro-dynamic programming, but each requires substantial computational resources or processing delays to implement the existing algorithms.

Human-Machine Collaborative Decision Making

Most autonomous vehicles for the foreseeable future will continue to operate under mixed-initiative control, in which decision making is shared by humans and automated systems. UUVs may be an exception to this rule, owing to the difficulty of communications in the underwater environment. For there to be a force-multiplier effect in the use of AVs, such decision making must involve a single human operator controlling several vehicles. Remote control of every vehicle by a single operator becomes impossible. This level of operator control (or conversely, level of autonomy) is a system design choice, as was previously pointed out. The desired level of human interaction to perform the functions described in Box 3.2 must be selected for each mission activity in the mission activity hierarchy for a particular system of AVs. As the number of vehicles to be controlled increases, so too does the required complexity of the human-machine interaction. The operator must know when to, and then be able to, take more control over mission activities at any time and for any level of the mission activity hierarchy, when required. Similarly, the automated systems must be better able to assess their ability to achieve the desired goals presented by the operator and then request help when needed. This variable or adjustable autonomy will likely be required to enable the Navy’s vision of the future.

Technologies available to implement mixed-initiative control today are fairly limited and primarily point solutions to specific portions of the autonomous

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
×

systems. For example, the planning and decision frameworks discussed in the section above entitled “Dynamic Real-Time Mission Planning and Replanning” provide a rudimentary (first) capability for the operator to interact with the system during any mission activity and at any level of the mission activity hierarchy. These interactions can be for the purpose of mission planning, plan execution monitoring, plan problem diagnosis, and authorization of planned activities. Although these frameworks do not preclude the use of variable levels of autonomy for mission activities, they do not presently support this capability either. Similarly, systems that are used to generate situation awareness (e.g., threat detection and response) typically implement a fixed, human-machine interaction protocol. Much work remains in order to develop a system architecture for autonomous systems and the methods that support mixed-initiative control with variable levels of autonomy for planning and decision, sensing and perception, and monitoring and diagnosis.

Key Shortfalls in Autonomy Capability

Despite the autonomy capabilities that can now be leveraged from the DOD’s autonomy technology portfolio or that are currently being developed via ONR’s Autonomous Operations FNC, much remains to be done if the Navy’s future vision is to be fully realized. The focus of future Naval Services’ investments and the pace of autonomy technology development must be carefully mapped, with cognizance of work being done across the DOD, including work by the Army, the Air Force, and the Defense Advanced Research Projects Agency (DARPA). Table 3.2 lists the top two or three general shortfalls in autonomy capability that need to be remedied in order to enable the operational capabilities described by the DOD’s vision expressed in the 2001 Quadrennial Defense Review11 and in the Navy’s Sea Power 21.12 These shortfalls represent areas in which more intensive, Navy- or Marine Corps-specific development focus may provide the greatest value in enabling new operational capabilities for the Naval Services. For each shortfall in capability, the table lists the level of technology development recommended by the committee, possible future programs (transition targets) that would benefit from the development, a description of the capability needed, and some items to be considered as part of the technology development. Implicit in the recommended level of technology development is the current level of technology maturity that could be built upon to create the new operational capability.

11  

Donald H. Rumsfeld, Secretary of Defense. 2001. Quadrennial Defense Review Report, U.S. Government Printing Office, Washington, D.C., September 30. Available online at <http://www.defenselink.mil/pubs/qdr2001.pdf>. Accessed on May 13, 2005.

12  

ADM Vern Clark, USN. 2002. “Sea Power 21: Projecting Decisive Joint Capabilities,” U.S. Naval Institute Proceedings, Vol. 128, No. 10, pp. 32-41.

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
×

TABLE 3.2 Key Autonomy Capabilities Shortfalls, by Technology Area, with the Level of Technology Development Recommended by the Committee and Future Programs That Would Benefit

Technology Area

Shortfalls in Autonomy Capability

Recommended S&T Levela

Of Benefit to Possible Future Programs

Description of Needed Capability

Key Considerations

Planning and decision

Dynamic mission planning for teams

6.2/6.3

LCS

Develop a capability for dynamic planning of high-level mission activities involving small teams of vehicles (manned or unmanned). Includes planning for all phases, e.g., launch, ingress, operations, egress, and recovery.

Vertical integration of team mission planning with C2, including method of team control (e.g., through master vehicle in master-slave arrangement or through each member of team in peer-to-peer arrangement). How will integrated system deal with targeting?

UCAV

Mine interdiction warfare systems

 

Threat-response tactics planning

6.1

UCAV

Develop a capability for real-time threat-response-tactics planning, which decides among options involving avoidance of threat, defense against threat with countermeasures, evasion of threat through maneuvering, or attack of threat with available weapons.

Explore benefit of dynamic concurrent threat-response planning (concurrent with nominal mission planning) versus using reactive preprogrammed tactics.

Sensing and perception

Human-machine collaborative threat and/or target identification and classification

6.3

UCAV

Integrate currently available algorithms with appropriate sensor modalities to demonstrate automatic target cueing capability.

Operationalize existing, but immature, technology by adding human interface to allow human-machine collaboration.

Consider focusing on currently available EO, IR, SAR sensor technology.

Multi-reconfigurable unmanned undersea vehicle (MRUUV)

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
×

 

Human-machine collaborative exploitation of sensor information

6.1/6.2

FORCEnet

Develop human-machine collaborative decision-making tools that have increased levels of autonomy for target-of-opportunity detection, classification and/or identification, exploitation in context, verification, and prioritization. Integrate these autonomy tools with system.

Consider the distribution of autonomy between autonomous vehicles collecting information, operator control stations, and intelligence exploitation centers.

 

Sensor development for intelligent autonomy for small vehicles. Perception for autonomous navigation.

Monitoring and diagnosis

Mission- and/or system-level problem detection, diagnosis, and reconfiguration

6.1/6.2

UCAV

Develop a capability to use FMEA, with system component coverage and failure rates to detect and diagnose current and emerging problems in autonomous systems and then to assess the impact of the problem on mission plans. Includes systems of multiple vehicles and communication back to home base(s).

Consider multiple levels of autonomy through human-machine collaboration.

MRUUV

Networking and collaboration

Secure, assured networking for multivehicle collaboration

6.1/6.2

LCS

Develop a capability to autonomously manage the network of a small team of vehicles collaboratively planning and generating situation awareness.

Consider missions in which all vehicles are operating in open terrain and missions in which one or more vehicles are operating in complex environments (e.g., urban environments, underwater, under canopy).

UCAV

Mine interdiction warfare systems

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
×

Technology Area

Shortfalls in Autonomy Capability

Recommended S&T Levela

Of Benefit to Possible Future Programs

Description of Needed Capability

Key Considerations

Learning and adaptation

Real-time learning for adaptation to unanticipated events

6.1

UCAV

Develop a capability to learn to adapt “on-the-fly” to unanticipated events. Includes events that were not anticipated but that might occur prior to the mission and for which the value function for several competing responses to the event needs to be learned quickly.

Solution may be application-specific. For example, problems of failure reconfiguration may require approaches different from those for problems involving learning and responding to adversary tactics.

MRUUV

Submarine track and trail

Human-system interface

Natural user interfaces (e.g., natural language, gestures, symbology)

6.1

General

Develop the capability for an autonomous system to understand natural language or gestures of military operators or controllers.

Very difficult problem to be solved generally. Consider focusing on specific high-value Navy or Marine Corps needs such as UAV deck operations, manned-unmanned aircraft operations, UGV control via gestures or hand signals, or launch-and-recovery operations.

 

Variable initiative control

6.2

UCAV

Develop the capability for human operators to exert temporal variations of control over missions and activities during mission operations.

Multiple levels of autonomy depending on operator workload and vehicle and/or mission state.

MRUUV

Other multimission capable AVs

aScience and technology (S&T) levels: 6.1, basic research; 6.2, applied research; 6.3, advanced technology development.

NOTE: A list of acronyms is provided in Appendix D.

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
×

CONCLUSIONS AND RECOMMENDATIONS

Autonomous Vehicle Concepts and Developments

As discussed above, the Office of Naval Research’s Autonomous Operations Future Naval Capability has initiated a four-pronged autonomy technology development effort. This effort, in concert with the DOD’s autonomy technology portfolio and ongoing DOD programs, provides a pipeline of maturing technologies that can be used to create, in the near term, new Navy and Marine Corps autonomous vehicle capabilities. Some examples include the following:

  • For UAVs and UGVs, the adoption and adaptation of the dynamic real-time mission-planning technology used in UUVs and on spacecraft;

  • The adoption of avionics architectures from spacecraft and manned systems to permit the migration of mission management autonomy software onboard autonomous vehicles;

  • The adaptation of a dynamic real-time mission-level planning module, such as that developed under DARPA Mixed Initiative Control of Automa-Teams or the ongoing DARPA Jaguar Programs, with existing flight-planning systems such as the Navy’s Portable Flight Planning System or the Joint Mission Planning System;

  • The automation of existing manned aircraft threat-detection and -response capabilities for use in autonomous vehicles of all types;

  • The adaptation of existing automatic target-recognition technology to operationalize semiautonomous versions of the technology using human collaboration; and

  • The use of analytical redundancy and the built-in test and diagnostics capabilities in subsystem equipment to provide enhanced system reliability.

Autonomous Vehicle Technologies

The focus of future Naval Services investments and the pace of autonomy technology development need to be carefully mapped, with cognizance of work being done across the DOD, including that of the Army, Air Force, and DARPA. Table 3.2 lists some of the shortfalls in autonomy capability that need to be remedied in order to achieve the Navy’s future vision—in these areas the committee believes that development focused on Navy-unique capabilities is required to raise the maturity of the technology to moderate levels. The committee believes that investments are needed in those technologies that improve the following:

  • The ability for AVs to operate in threat-dense environments,

  • The ability for human operators and/or intelligence analysts to collaborate with computers to interpret and exploit AV sensor data,

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
×
  • The ability of AVs to network and collaborate with other autonomous and manned vehicles,

  • The ability of AVs to detect and diagnose mission- and system-level problems and to reconfigure in order to accommodate them,

  • The ability of AVs to perform multiple missions, and

  • The ability of UAVs and UUVs to perform autonomous shipboard launch-and-recovery operations.

Incorporate Level of Mission Autonomy as Autonomous Vehicle Design Trade-off

System designers of autonomous vehicles often neglect the potential operational benefits to be derived by employing level of mission autonomy as a design choice in up-front trade-off studies, instead electing to focus on trade-offs relating to vehicle performance characteristics (e.g., speed, range, endurance, stealth) and subsystem capability (e.g., sensing and communications). This approach constrains the level of autonomy that can be implemented later in the development and prevents designs that might provide greater operational benefit in terms of impacting mission effectiveness, vehicle survivability, and system afford-ability. Early-stage AV design trade-offs can include the vertical integration of the AV system with its command-and-control system for the end-to-end operations to be performed by the system, including allocation and assignment, mission tasking (e.g., intelligence, surveillance, and reconnaissance; strike; logistics), collection, exploitation, and dissemination. Including the level of mission autonomy as a design choice enables several additional benefits to be derived, such as these:

  • Prioritized, targeted technology development investments for Navy and Marine Corps autonomous vehicle needs based on determining those technologies that will have the greatest benefit;

  • Reduced system complexity achieved through an increase in onboard mission autonomy;

  • Improved autonomous vehicle mission effectiveness and survivability resulting from shorter planning and decision-making cycles; faster assimilation and interpretation of sensor information; faster detection, isolation, and assessment of system problems; shared mission objectives among collaborators; and expanded use of offboard sensor information; and

  • Reduced total cost of autonomous vehicle ownership resulting from reduced operator support for planning, decision, and collaboration; reduced operator support for sensor interpretation and exploitation; reduced operator support for monitoring and problem diagnosis; reduced maintenance labor for trouble-shooting and prognostication; higher system reliability and reduced probability

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
×

of loss of vehicle; and shared use of distributed resources (e.g., sensors, weapons, and so on).

Autonomous Technology Recommendations

Recommendation: The Assistant Secretary of the Navy for Research, Development, and Acquisition (ASN(RD&A)) and the Chief of Naval Research (CNR) should direct the Navy and Marine Corps Systems Commands, the Office of Naval Research (ONR), and the Marine Corps Warfighting Laboratory (MCWL) to partner with the operational community and monitor the concepts and development of critical autonomous vehicle-related technologies considered essential to the accomplishment of future naval missions. The progress of these developments should be tracked year to year. Specifically:


Pursue New Autonomy Concepts and Technology Developments. The ASN (RD&A) should direct appropriate agencies in the Navy and Marine Corps to formulate and maintain a list of the most promising moderately to highly mature autonomy technologies (Technology Readiness Level: TRL > 4) that can enable, critical near-term autonomous vehicle capabilities. Plans to pursue further development of these capabilities should be developed and funded, and progress should be tracked year to year to ensure the proper pace of development.

The ONR should develop autonomous vehicle research and development (R&D) needs and a technology roadmap to achieve the goals defined by the various vision documents of the Naval Services. ONR should leverage the current operational experience and the recommended increase in future operational experience with autonomous vehicles in order to define R&D needs to address specific, high-value operational needs.


Recommendation: The Assistant Secretary of the Navy for Research, Development, and Acquisition (ASN(RD&A)) should mandate that level of mission autonomy be included as a required up-front design trade-off in all unmanned vehicle system development contracts. Specifically:


Incorporate Level of Mission Autonomy as an Autonomous Vehicle Design Trade-off. The ASN(RD&A) should direct appropriate agencies in the Navy and Marine Corps to exploit level of mission autonomy as a degree of freedom for impacting concepts of operations, mission effectiveness, vehicle survivability, and system affordability by including a level of mission autonomy as a design choice in the early-stage system trade-off studies. The architecture of all new autonomous vehicles should be such that increasing levels of autonomy can be implemented in the field by modular replacement and/or software upgrade.

Suggested Citation:"3 Autonomy Technology: Capabilities and Potential." National Research Council. 2005. Autonomous Vehicles in Support of Naval Operations. Washington, DC: The National Academies Press. doi: 10.17226/11379.
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Autonomous vehicles (AVs) have been used in military operations for more than 60 years, with torpedoes, cruise missiles, satellites, and target drones being early examples.1 They have also been widely used in the civilian sector--for example, in the disposal of explosives, for work and measurement in radioactive environments, by various offshore industries for both creating and maintaining undersea facilities, for atmospheric and undersea research, and by industry in automated and robotic manufacturing.

Recent military experiences with AVs have consistently demonstrated their value in a wide range of missions, and anticipated developments of AVs hold promise for increasingly significant roles in future naval operations. Advances in AV capabilities are enabled (and limited) by progress in the technologies of computing and robotics, navigation, communications and networking, power sources and propulsion, and materials.

Autonomous Vehicles in Support of Naval Operations is a forward-looking discussion of the naval operational environment and vision for the Navy and Marine Corps and of naval mission needs and potential applications and limitations of AVs. This report considers the potential of AVs for naval operations, operational needs and technology issues, and opportunities for improved operations.

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