A machine learning approach for feature-sensitive motion planning

Marco Morales, Lydia Tapia, Roger Pearce, Samuel Rodriguez, Nancy M. Amato

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Although there are many motion planning techniques, there is no method that outperforms all others for all problem instances. Rather, each technique has different strengths and weaknesses which makes it best-suited for certain types of problems. Moreover, since an environment can contain vastly different regions, there may not be a single planner that will perform well in all its regions. Ideally, one would use a suite of planners in concert and would solve the problem by applying the best-suited planner in each region. In this paper, we propose an automated framework for feature-sensitive motion planning. We use a machine learning approach to characterize and partition C-space into regions that are well suited to one of the methods in our library of roadmap-based motion planners. After the best-suited method is applied in each region, the resulting region roadmaps are combined to form a roadmap of the entire planning space. Over a range of problems, we demonstrate that our simple prototype system reliably outperforms any of the planners on their own.

Original languageEnglish (US)
Title of host publicationAlgorithmic Foundations of Robotics VI
PublisherSpringer-Verlag Berlin Heidelberg
Pages361-376
Number of pages16
ISBN (Print)9783540257288
DOIs
StatePublished - Jan 1 2005

Publication series

NameSpringer Tracts in Advanced Robotics
Volume17
ISSN (Print)1610-7438
ISSN (Electronic)1610-742X

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Artificial Intelligence

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  • Cite this

    Morales, M., Tapia, L., Pearce, R., Rodriguez, S., & Amato, N. M. (2005). A machine learning approach for feature-sensitive motion planning. In Algorithmic Foundations of Robotics VI (pp. 361-376). (Springer Tracts in Advanced Robotics; Vol. 17). Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/10991541_25