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2007

The Stochastic Motion Roadmap: A Sampling Framework for Planning with Markov Motion Uncertainty

9 years 3 months ago
The Stochastic Motion Roadmap: A Sampling Framework for Planning with Markov Motion Uncertainty
— We present a new motion planning framework that explicitly considers uncertainty in robot motion to maximize the probability of avoiding collisions and successfully reaching a goal. In many motion planning applications ranging from maneuvering vehicles over unfamiliar terrain to steering flexible medical needles through human tissue, the response of a robot to commanded actions cannot be precisely predicted. We propose to build a roadmap by sampling collision-free states in the configuration space and then locally sampling motions at each state to estimate state transition probabilities for each possible action. Given a query specifying initial and goal configurations, we use the roadmap to formulate a Markov Decision Process (MDP), which we solve using Infinite Horizon Dynamic Programming in polynomial time to compute stochastically optimal plans. The Stochastic Motion Roadmap (SMR) thus combines a sampling-based roadmap representation of the configuration space, as in PRM’...
Ron Alterovitz, Thierry Siméon, Kenneth Y.
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2007
Where RSS
Authors Ron Alterovitz, Thierry Siméon, Kenneth Y. Goldberg
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