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

@inproceedings{Alterovitz2007TheSM,
  title={The Stochastic Motion Roadmap: A Sampling Framework for Planning with Markov Motion Uncertainty},
  author={Ron Alterovitz and Thierry Sim{\'e}on and Kenneth Y. Goldberg},
  booktitle={Robotics: Science and Systems},
  year={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 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… CONTINUE READING
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