Path Set Relaxation for Mobile Robot Navigation

Philipp Krüsi, Mihail Pivtoraiko, Alonzo Kelly, Thomas M. Howard, and Roland Siegwart. Path Set Relaxation for Mobile Robot Navigation. In Proceedings of the International Symposium on Artificial Intelligence, Robotics and Automation in Space, 2010.

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Abstract

This paper addresses autonomous navigation and goal acquisition for mobile robots operating in difficult, cluttered environments. In particular a hierarchical approach to navigation is of interest, which subdivides the problem into global and local components. Local planners attempt to search the continuum of actions for a best (safest, efficient) route towards a goal. To achieve real-time performance, the search space is often sampled in lowdimensional action or state space. This paper explores a relaxation-based approach that optimizes 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. Additional experiments explore the benefits of sampled action spaces composed of higher-order action primitives (clothoids) and a graduated fidelity inspired lookahead technique.

BibTeX

@INPROCEEDINGS{kruesi_etal_isairas10,
  author = {Philipp Kr\"{u}si and Mihail Pivtoraiko and Alonzo Kelly and Thomas
	M. Howard and Roland Siegwart},
  title = {Path Set Relaxation for Mobile Robot Navigation},
  booktitle = {Proceedings of the International Symposium on Artificial Intelligence,
	Robotics and Automation in Space},
  year = {2010},
  abstract = {This paper addresses autonomous navigation and goal
                  acquisition for mobile robots operating in
                  difficult, cluttered environments. In particular a
                  hierarchical approach to navigation is of interest,
                  which subdivides the problem into global and local
                  components. Local planners attempt to search the
                  continuum of actions for a best (safest, efficient)
                  route towards a goal. To achieve real-time
                  performance, the search space is often sampled in
                  lowdimensional action or state space. This paper
                  explores a relaxation-based approach that optimizes
                  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. Additional
                  experiments explore the benefits of sampled action
                  spaces composed of higher-order action primitives
                  (clothoids) and a graduated fidelity inspired
                  lookahead technique.},
  bib2html_pubtype = {Refereed Conference Papers},
  bib2html_rescat = {Robot Navigation}
}