Report Number: CS-TR-94-1519
Institution: Stanford University, Department of Computer Science
Title: Probabilistic Roadmaps for Path Planning in High-Dimensional Configuration Spaces
Author: Kavraki, Lydia
Author: Svestka, Petr
Author: Latombe, Jean-Claude
Author: Overmars, Mark
Date: August 1994
Abstract: A new motion planning method for robots in static workspaces is presented. This method proceeds according to 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 edges 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 considered scenes, that is the robots and their workspaces. 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 (approximately 150 MIPS), after learning for relatively short periods of time (a few dozen seconds).