Agents with differential constraints, although common in the real world, pose
a particular difficulty for motion planning algorithms. Methods for solving
such problems are still relatively slow and inefficient. In particular,
current motion planners generally can neither "see" the world around them,
nor generalize from experience. That is, their reliance on collision tests as
the only means of sensing the environment yields a tactile, myopic perception
of the world. Such short-sightedness greatly limits any potential for
detection, learning, or reasoning about frequently encountered situations. In
result these methods solve each problem in exactly the same way, whether the
first or the hundredth time they attempt it, each time none the wiser. The
key component of this thesis proposes a general approach for motion planning
in which local sensory information, in conjunction with prior accumulated
experience, are exploited to improve planner performance. The approach relies
on learning viability models for the agent's "perceptual space", and the use
thereof to direct planning effort. In addition, a method is presented for
improving runtimes of the RRT motion planning algorithm in heavily constrained
search-spaces, a common feature for agents with differential constraints.
Finally, the thesis explores the use of viability models for maintaing safe
operation of user-controlled agents, a related application which could be
harnessed to yield additional, more "natural" experience data for further
improving motion planning.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/11215 |
| Date | 31 July 2008 |
| Creators | Kalisiak, Maciej |
| Contributors | van de Panne, Michiel |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
| Format | 29646387 bytes, application/pdf |
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