Probabilistic Roadmaps for Path Planning in High-Dimensional Configuration Spaces

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Probabilistic Roadmaps for Path Planning in High-Dimensional Configuration Spaces (1996)

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by Lydia Kavraki , Petr Svestka , Jean-claude Latombe , Mark Overmars

Venue:	IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION
Citations:	1244 - 117 self

BibTeX

@INPROCEEDINGS{Kavraki96probabilisticroadmaps,
    author = {Lydia Kavraki and Petr Svestka and Jean-claude Latombe and Mark Overmars},
    title = {Probabilistic Roadmaps for Path Planning in High-Dimensional Configuration Spaces},
    booktitle = {IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION},
    year = {1996},
    pages = {566--580},
    publisher = {}
}

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Abstract

A new motion planning method for robots in static workspaces is presented. This method proceeds in two phases: a learning phase and a query phase. In the learning phase, a probabilistic roadmap is constructed and stored as a graph whose nodes correspond to collision-free configurations and whose edges correspond to feasible paths between these configurations. These paths are computed using a simple and fast local planner. In the query phase, any given start and goal configurations of the robot are connected to two nodes of the roadmap; the roadmap is then searched for a path joining these two nodes. The method is general and easy to implement. It can be applied to virtually any type of holonomic robot. It requires selecting certain parameters (e.g., the duration of the learning phase) whose values depend on the scene, that is the robot and its workspace. But these values turn out to be relatively easy to choose, Increased efficiency can also be achieved by tailoring some components of the method (e.g., the local planner) to the considered robots. In this paper the method is applied to planar articulated robots with many degrees of freedom. Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (=150 MIPS), after learning for relatively short periods of time (a few dozen seconds)

Keyphrases

path planning probabilistic roadmaps high-dimensional configuration space learning phase query phase certain parameter method proceeds short period static workspace goal configuration many degree articulated robot probabilistic roadmap holonomic robot contemporary workstation feasible path local planner collision-free configuration fast local planner experimental result new motion planning method dozen second

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