包囲保証付き協調追跡のためのロバストモデル予測制御
Encirclement Guaranteed Cooperative Pursuit with Robust Model Predictive Control
複数の追跡者が逃げる対象を常に凸包内に囲い込みながら捕獲する問題を、ロバストモデル予測制御と分割スキームで分散的に解く手法を提案した。
著者: Chen Wang, Hua Chen, Jia Pan, Wei Zhang
分類: cs.MA, cs.RO
原文アブストラクト
This paper studies a novel encirclement guaranteed cooperative pursuit problem involving $N$ pursuers and a single evader in an unbounded two-dimensional game domain. Throughout the game, the pursuers are required to maintain encirclement of the evader, i.e., the evader should always stay inside the convex hull generated by all the pursuers, in addition to achieving the classical capture condition. To tackle this challenging cooperative pursuit problem, a robust model predictive control (RMPC) based formulation framework is first introduced, which simultaneously accounts for the encirclement and capture requirements under the assumption that the evader's action is unavailable to all pursuers. Despite the reformulation, the resulting RMPC problem involves a bilinear constraint due to the encirclement requirement. To further handle such a bilinear constraint, a novel encirclement guaranteed partitioning scheme is devised that simplifies the original bilinear RMPC problem to a number of linear tube MPC (TMPC) problems solvable in a decentralized manner. Simulation experiments demonstrate the effectiveness of the proposed solution framework. Furthermore, comparisons with existing approaches show that the explicit consideration of the encirclement condition significantly improves the chance of successful capture of the evader in various scenarios.
