Overview

We present a new method for generating collision-free paths for robots operating in changing environments. Our approach is closely related to recent probabilistic roadmap approaches. These planners use preprocessing and query stages, and are aimed at planning many times in the same environment. In contrast, our preprocessing stage creates a representation of the configuration space that can be easily modified in real time to account for changes in the environment. As with previous approaches, we begin by constructing a graph that represents a roadmap in the configuration space, but we do not construct this graph for a specific workspace. Instead, we construct the graph for an obstacle-free workspace, and encode the mapping from workspace cells to nodes and arcs in the graph. When the environment changes, this mapping is used to make the appropriate modifications to the graph, and plans can be generated by searching the modified graph.

After presenting the approach, we address a number of performance issues via extensive simulation results for robots with as many as twenty degrees of freedom. We evaluate memory requirements, preprocessing time, and the time to dynamically modify the graph and replan, all as a function of the number of degrees of freedom of the robot.

1 Introduction

In this paper, we present a new method for generating collision-free paths for robots operating in changing environments. Our work builds on recent methods that use probabilistic roadmap planners [3,...