CHOMP: Covariant Hamiltonian optimization for motion planning

@article{Zucker2013CHOMPCH,
  title={CHOMP: Covariant Hamiltonian optimization for motion planning},
  author={Matthew Zucker and Nathan D. Ratliff and Anca D. Dragan and Mihail Pivtoraiko and Matthew Klingensmith and Christopher M. Dellin and J. Andrew Bagnell and Siddhartha S. Srinivasa},
  journal={The International Journal of Robotics Research},
  year={2013},
  volume={32},
  pages={1164 - 1193}
}
In this paper, we present CHOMP (covariant Hamiltonian optimization for motion planning), a method for trajectory optimization invariant to reparametrization. CHOMP uses functional gradient techniques to iteratively improve the quality of an initial trajectory, optimizing a functional that trades off between a smoothness and an obstacle avoidance component. CHOMP can be used to locally optimize feasible trajectories, as well as to solve motion planning queries, converging to low-cost… 
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References

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It is experimentally show that the stochastic nature of STOMP allows it to overcome local minima that gradient-based methods like CHOMP can get stuck in.
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