Analogical Path Planning

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Analogical Path Planning
We present a probabilistic method for path planning that considers trajectories constrained by both the environment and an ensemble of restrictions or preferences on preferred motions for a moving robot. Our system learns constraints and preference biases on a robot's motion from examples, and then synthesizes behaviors that satisfy these constraints. This behavior can encompass motions that satisfy diverse requirements such as a sweep pattern for floor coverage, or, in particular in our experiments, satisfy restrictions on the robot's physical capabilities such as restrictions on its turning radius. Given an approximate path that may not satisfy the required conditions, our system computes a refined path that satisfies the constraints and also avoids obstacles. Our approach is based on a Bayesian framework for combining a prior probability distribution on the trajectory with environmental constraints. The prior distribution is generated by decoding a Hidden Markov Model, wh...
Saul Simhon, Gregory Dudek
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2004
Where AAAI
Authors Saul Simhon, Gregory Dudek
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