13 search results - page 1 / 3 » The Stochastic Motion Roadmap: A Sampling Framework for Plan... |
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2007
13 years 5 months ago
2007
— 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 ...
ICRA
2007
IEEE
13 years 10 months ago
2007
IEEE
Abstract— Sampling-based algorithms have dramatically improved the state of the art in robotic motion planning. However, they make restrictive assumptions that limit their applic...
ICRA
2006
IEEE
13 years 10 months ago
2006
IEEE
Abstract— Randomized motion planning techniques are responsible for many of the recent successes in robot control. However, most motion planning algorithms assume perfect and com...
IJRR
2011
12 years 11 months ago
2011
Abstract Partially observable Markov decision processes (POMDPs) are a principled mathematical framework for planning under uncertainty, a crucial capability for reliable operation...
ICRA
2008
IEEE
13 years 11 months ago
2008
IEEE
— Modeling robot motion planning with uncertainty in a Bayesian framework leads to a computationally intractable stochastic control problem. We seek hypotheses that can justify a...