Swift Pursuer: A Topology-Accelerated and Robust Approach for Pursuing an Evader in Obstacle Environments With State Measurement Uncertainty

Abstract

This letter presents a topology-accelerated and robust pursuit framework for environments with obstacles considering state measurement uncertainty. Our framework consists of three primary components: the selection of virtual target points using topological heuristic method to encourage path diversity, the computation of safe pursuit regions based on Voronoi cell (VC) and the solution of an adaptive robust path controller based on Control Barrier Function (CBF) to guarantee safety under state measurement uncertainty. Topological heuristics broadly capture the topological structure of the environment and provide guidance for the selection of target points for each pursuer. Then the chance constrained obstacle-aware Voronoi cell (CCOVC) for each pursuer is constructed by calculating separation hyperplane and buffer terms. Finally, we formulate chance CBF and chance Control Lyapunov Function (CLF) constraints based on CCOVC, using convex approximation to determine their upper bounds. We then find the adaptive robust path controller by solving a Quadratically Constrained Quadratic Program (QCQP). Benchmark simulation and experimental results demonstrate the efficiency and robustness of the proposed framework.