Rover Trajectory Planning: Constrained Global Planning and Path Relaxation

Mihail Pivtoraiko and Alonzo Kelly. Rover Trajectory Planning: Constrained Global Planning and Path Relaxation. In Workshop on Motion Planning at the International Conference on Robotics and Automation, 2010.

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Abstract

We focus on efficient goal acquision and obstacle avoidance using autonomous wheeled robots operating in cluttered natural environments. The approach a hierarchical structure that consists local and global motion planners operating in tandem. A conventional approach to designing the local planner in this setting is to evaluate a fixed number of constant-curvature arc motions and pick one that is the best balance between the quality of obstacle avoidance and minimizing traversed path length to the goal (or a similar measure of operation cost). Here we describe an approach based on path relaxation: optimizing the sampled action space for the perceived environment. The gradient based approach minimizes the cost of each motion in the path set until convergence into a local optimum is reached. Simulation experiments show that relaxed arc sets offer better approximations of the acceptable path continuum and lead to safer navigation in rough terrain and dense obstacle fields.

BibTeX

@INCOLLECTION{pivtoraiko_kelly_icra10,
  author = {Mihail Pivtoraiko and Alonzo Kelly},
  title = {Rover Trajectory Planning: Constrained Global Planning and Path Relaxation},
  booktitle = {Workshop on Motion Planning at the International Conference on Robotics and Automation},
  year = {2010},
  abstract = {We focus on efficient goal acquision and obstacle
                  avoidance using autonomous wheeled robots operating
                  in cluttered natural environments. The approach a
                  hierarchical structure that consists local and
                  global motion planners operating in tandem. A
                  conventional approach to designing the local planner
                  in this setting is to evaluate a fixed number of
                  constant-curvature arc motions and pick one that is
                  the best balance between the quality of obstacle
                  avoidance and minimizing traversed path length to
                  the goal (or a similar measure of operation cost).
                  Here we describe an approach based on path
                  relaxation: optimizing the sampled action space for
                  the perceived environment. The gradient based
                  approach minimizes the cost of each motion in the
                  path set until convergence into a local optimum is
                  reached.  Simulation experiments show that relaxed
                  arc sets offer better approximations of the
                  acceptable path continuum and lead to safer
                  navigation in rough terrain and dense obstacle
                  fields.},
  bib2html_pubtype = {Workshop Papers},
  bib2html_rescat = {Robot Navigation}
}

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