Frontiers of Engineering Reports on Leading-Edge Engineering from the 2010 Symposium (2011) / Chapter Skim
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Intelligent Autonomy in Robotic Systems--Mark Campbell
Intelligent Autonomy in Robotic Systems--Mark Campbell
Pages 77-88
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From page 77... ...
For example, although autonomy in deep-space missions is impres sive, it is still well behind autonomous ground systems. Reasons for this gap range from proximity to the hardware and environmental hardships to scientists tending not to trust autonomous software for projects on which many years and dollars have been spent.
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From page 78... ...
One critical problem was the mismatch between perception, which is typi cally probabilistic because sensors yield data that are inherently uncertain compared to the true system, and planning, which is deterministic because plans must be implemented in the real world. To date, perception research typically provides robotic planners with probabilistic "snapshots" of the environment, which leads to "reactive," rather than "intelligent," behaviors in autonomous robots.
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From page 79... ...
Center: Notional illustration of cooperative tracking using UAVs. Right: Flight test data of two UAVs tracking a truck over a communication network with losses (e.g., dropped packets)
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From page 80... ...
The necessary information includes detecting humans, locating survivors in clutter, and tracking moving cars -- even if there are visual obstruc tions, such as trees or buildings. Actions based on this information then include deciding where to fly, a decision strongly influenced by sensing and coverage, and deciding what information to share (among UAVs and/or with ground operators)
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From page 81... ...
Interestingly, many of these braking events occurred during multiple passes near the same areas; the most frequent (18 times) took place near a single concrete barrier jutting out from the others, making it appear (to the perception algorithms)
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From page 82... ...
For example, a robot vacuuming a floor requires minimal interaction with humans, but search and tracking using a team of UAVs equipped with sensors and weapons is much more challenging, not only because of the complexity of the tasks and system, but also because of the inherent stress of the situation. FIGURE 4 Example of using probabilistic anticipation for provably safe plans in autonomous driving.
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From page 83... ...
scalable theory that enables easy adoption as well as formal analysis. Fusion of Human and Robotic Information Humans typically provide high-level commands to autonomous robots, but clearly they can also contribute important information, such as an opinion about which area of Mars to explore or whether a far off object in a cluttered environment is a person or a tree.
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From page 84... ...
This experiment demonstrated initial decision modeling and fusion results, but the human decision was decidedly simple. To be useful, however, research, particularly in the area of machine learning, must model more complex outputs, such as strategic decisions over time or decisions made with little a priori data.
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From page 85... ...
Center: A robot exploring an environment. Right: A screen shot showing how the human selected a robot, drew a potential path, and selected an area to explore.
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From page 86... ...
2008. Behavioral Recognition and Prediction of an Operator Supervising Multiple Heterogeneous Unmanned Vehicles.
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From page 87... ...
2007. The Effects of Multi modal Collaboration Technology on Subjective Workload Profiles of Tactical Air Battle Manage ment Teams.
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From page 88... ...
2009. Receding Horizon Temporal Logic Planning for Dynamical Systems.
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