Robust, compact representations for real-time path planning in changing environments

We have previously developed a new method for generating collision-free paths for robots operating in changing environments. Our approach relies on creating a representation of the configuration space that can be easily modified in real time to account for changes in the environment. In this paper we address the issues of efficiency and robustness. First, we develop a novel, efficient encoding scheme that exploits the redundancy in the map from robot's Euclidean workspace to its configuration space. Then, we introduce the concept of ε-robustness, and show how it can be used to enhance the representations that are used by the planner. Along the way, we present quantitative results that illustrate the efficiency and robustness of our approach.