Field Experiments in Rover Navigation via Model-Based Trajectory Generation and Nonholonomic Motion Planning in State Lattices

Mihail Pivtoraiko, Thomas Howard, Issa A.D. Nesnas, and Alonzo Kelly. Field Experiments in Rover Navigation via Model-Based Trajectory Generation and Nonholonomic Motion Planning in State Lattices. In Proceedings of the 9th International Symposium on Artificial Intelligence, Robotics, and Automation in Space, 2008.

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Abstract

This paper presents field experiments of two novel approaches to local and regional motion planning applied to planetary rover navigation. The first approach solves the two-point boundary value problem using a model-based trajectory optimization technique that inverts an arbitrary dynamics model to generate a feasible motion plan. The second approach utilizes this result to build a special discretization of the state space that allows employing standard search algorithms for solving the motion planning problem. These approaches enable robot autonomy by considering the robot’s dynamics, efficiently searching a finely discretized state space, and allowing the reuse of previous planning computation to improve runtime. We present results from the experiments on the Rocky 8 and FIDO planetary rover prototypes in the NASA/JPL Mars Yard.

BibTeX

@INPROCEEDINGS{pivtoraiko_etal_isairas08,
  author = {Mihail Pivtoraiko and Thomas Howard and Issa A.D. Nesnas and Alonzo
	Kelly},
  title = {Field Experiments in Rover Navigation via Model-Based Trajectory
	Generation and Nonholonomic Motion Planning in State Lattices},
  booktitle = {Proceedings of the 9th International Symposium on Artificial Intelligence,
	Robotics, and Automation in Space},
  year = {2008},
  abstract = {This paper presents field experiments of two novel
                  approaches to local and regional motion planning
                  applied to planetary rover navigation. The first
                  approach solves the two-point boundary value problem
                  using a model-based trajectory optimization
                  technique that inverts an arbitrary dynamics model
                  to generate a feasible motion plan. The second
                  approach utilizes this result to build a special
                  discretization of the state space that allows
                  employing standard search algorithms for solving the
                  motion planning problem.  These approaches enable
                  robot autonomy by considering the robot’s dynamics,
                  efficiently searching a finely discretized state
                  space, and allowing the reuse of previous planning
                  computation to improve runtime.  We present results
                  from the experiments on the Rocky 8 and FIDO
                  planetary rover prototypes in the NASA/JPL Mars
                  Yard.},
  bib2html_pubtype = {Refereed Conference Papers},
  bib2html_rescat = {Robot Navigation},
  owner = {mihail},
  timestamp = {2010.08.07}
}

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