Path planning using learned constraints and preferences

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Path planning using learned constraints and preferences
— In this paper we present a novel method for robot path planning based on learning motion patterns. A motion pattern is defined as the path that results from applying a set of probabilistic constraints to a “raw” input path. For example, a user can sketch an approximate path for a robot without considered issues such as bounded radius of curvature and our system would then elaborate it to include such a constraint. In our approach, the constraints that generate a path are learned by capturing the statistical properties of a set of training examples using supervised learning. Each training example consists of a pair of paths: an unconstrained (raw) path and an associated preferred path. Using a Hidden Markov Model in combination with multi-scale methods, we compute a probability distribution for successive path segments as a function of their context within the path and the raw path that guides them. This learned distribution is then used to synthesize a preferred path from an a...
Gregory Dudek, Saul Simhon
Added 04 Jul 2010
Updated 04 Jul 2010
Type Conference
Year 2003
Where ICRA
Authors Gregory Dudek, Saul Simhon
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