Distributed Pursuit of an Evader with Adaptive Robust Path Control Under State Measurement Uncertainty

Abstract

This paper presents a distributed pursuit frame-work for environments with obstacles considering state measurement uncertainty. Our framework consists of two primary components: 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). Initially, the chance constrained obstacle-aware Voronoi cell (CCOVC) for each pursuer is constructed by calculating separation hyperplane and buffer terms. Subsequently, we formulate chance CBF and chance Control Lyapunov Function (CLF) constraints, using convex approximation to determine their upper bounds. We then find the adaptive robust path controller by solving a Quadratically Constrained Quadratic Program (QCQP). The advantage of this framework lies in its capability to adaptively compute the path controller and ensure robust collision avoidance among pursuers and with obstacles. Simulation and experimental results demonstrate the effectiveness and robustness of the proposed framework.