Distributed cooperative pursuit with encirclement guarantee via robust model predictive control

This paper studies a cooperative pursuit-evasion problem with the encirclement guarantee. Inspired by the importance of forming an encirclement of the evader in practical applications such as surveillance, we explicitly consider the encirclement condition throughout the game in the synthesis of a cooperative pursuit strategy. Different from classical pursuit-evasion problems, the studied problem requires a careful balance between the capture and encirclement conditions. To solve this challenging problem, we exploit the geometric nature of the encirclement condition and develop a robust model predictive control (RMPC) based strategy to account for the encirclement and capture requirements jointly. To address the bilinearity of the centralized RMPC problem and to respect the communication constraint, we proposed a novel approach to decouple the pursuit-evasion problem among the pursuers. Such a distributed strategy relies on a novel Encirclement-Guaranteed Sectors Set (EGSS) concept that inner-approximates the original bilinear RMPC problem with a set of decoupled linear RMPC problems. These linear problems can be efficiently solved by Tube Model Predictive Control (TMPC) with only local information. To validate the effectiveness of the proposed framework, we provide extensive simulation experiments with realistic mobile robot models. Comparisons with the baseline Voronoi-based strategy demonstrate the robustness of the proposed approach in guaranteeing encirclement while achieving successful capture.