Topology-Aware Efficient Path Planning in Dynamic Environments

This study presents a path-planning approach toward efficient obstacle avoidance in dynamic environments. The developed approach features the awareness of the topological structure of the dynamic environment at a planning instant. It is achieved by employing a homology class path planner to generate a set of non-homotopy global paths. The global paths are cast into tree structures separately and optimized by the developed sampling-based path-planning methods. This mechanism can adaptively adjust the optimizing step size according to the change in the dynamic environment, and the sampling module uses the Gaussian Mixture Model (GMM) Optimizer to control the sampling space. The approach seeks the globally optimal path as it maintains and optimizes homology classes of admissible candidate paths of distinctive topologies in parallel. We conduct various experiments in dynamic environments to verify the developed method’s effectiveness and efficiency. It is demonstrated that the developed method can perform better than the state of the art.