Motion planning under uncertainty using iterative local optimization in belief space

@article{Berg2012MotionPU,
  title={Motion planning under uncertainty using iterative local optimization in belief space},
  author={Jur P. van den Berg and Sachin Patil and Ron Alterovitz},
  journal={I. J. Robotics Res.},
  year={2012},
  volume={31},
  pages={1263-1278}
}
We present a new approach to motion planning under sensing and motion uncertainty by computing a locally optimal solution to a continuous partially observable Markov decision process (POMDP). Our approach represents beliefs (the distributions of the robot’s state estimate) by Gaussian distributions and is applicable to robot systems with non-linear dynamics and observation models. The method follows the general POMDP solution framework in which we approximate the belief dynamics using an… CONTINUE READING
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Publications referenced by this paper.
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Probabilistic Robotics

  • S Thrun, W Burgard, D Fox
  • 2005
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4 Excerpts

The complexity of Markov decision processes

  • C Papadimitriou, J Tsisiklis
  • Mathematics of Operations Research
  • 1987
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4 Excerpts

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