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2007
136views Robotics» more  RSS 2007»
10 years 5 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 ...
Ron Alterovitz, Thierry Siméon, Kenneth Y. ...
ICRA
2007
IEEE
113views Robotics» more  ICRA 2007»
10 years 10 months ago
Sampling-Based Motion Planning With Sensing Uncertainty
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...
Brendan Burns, Oliver Brock
ICRA
2006
IEEE
144views Robotics» more  ICRA 2006»
10 years 10 months ago
Adapting Probabilistic Roadmaps to Handle Uncertain Maps
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...
Patrycja E. Missiuro, Nicholas Roy
IJRR
2011
218views more  IJRR 2011»
9 years 11 months ago
Motion planning under uncertainty for robotic tasks with long time horizons
Abstract Partially observable Markov decision processes (POMDPs) are a principled mathematical framework for planning under uncertainty, a crucial capability for reliable operation...
Hanna Kurniawati, Yanzhu Du, David Hsu, Wee Sun Le...
ICRA
2008
IEEE
197views Robotics» more  ICRA 2008»
10 years 10 months ago
A Bayesian framework for optimal motion planning with uncertainty
— 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...
Andrea Censi, Daniele Calisi, Alessandro De Luca, ...
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