A Framework for Real-time Path Planning in Changing Environments

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, thus facilitating real-time planning. 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. In this paper, we first discuss the construction of the roadmap, including how random samples of the configuration space are generated using an importance sampling approach and how these samples are connected to form the roadmap. We then discuss the mapping from the workspace to the configuration space roadmap, explaining how the mapping is generated and how it can be encoded efficiently using compression schemes that exploit redundancy in the mapping. We then introduce quantitative robustness measures and show how these can be used to enhance the robustness of the roadmap to changes in the environment. Finally, we evaluate an implementation of our approach for serial-link manipulators with up to 20 joints. KEY WORDS—probabilistic roadmaps, motion planning