Robot Motion Planning: A Distributed Representation Approach

TLDR

The paper proposes a novel robot path‑planning method that builds and searches a graph of local minima of a potential function over the configuration space. The method exploits the favorable properties of the potential function and uses efficient escape techniques, notably a Monte Carlo Brownian motion, together with distributed bitmap representations of workspace and configuration space to construct and search the graph. Implemented experiments on simulated robots ranging from 3 to 31 degrees of freedom show that the planner is considerably faster than prior planners and can handle high‑DOF robots.

Abstract

We propose a new approach to robot path planning that consists of building and searching a graph connecting the local minima of a potential function defined over the robot's configuration space. A planner based on this approach has been implemented. This planner is consider ably faster than previous path planners and solves prob lems for robots with many more degrees of freedom (DOFs). The power of the planner derives both from the "good" properties of the potential function and from the efficiency of the techniques used to escape the local min ima of this function. The most powerful of these tech niques is a Monte Carlo technique that escapes local min ima by executing Brownian motions. The overall approach is made possible by the systematic use of distributed rep resentations (bitmaps) for the robot's work space and configuration space. We have experimented with the plan ner using several computer-simulated robots, including rigid objects with 3 DOFs (in 2D work space) and 6 DOFs (in 3D work space) and manipulator arms with 8, 10, and 31 DOFs (in 2D and 3D work spaces). Some of the most significant experiments are reported in this article.