Pursuit Winning Strategies for Reach-Avoid Games with Polygonal Obstacles
<|reference_start|>Pursuit Winning Strategies for Reach-Avoid Games with Polygonal Obstacles: This paper studies a multiplayer reach-avoid differential game in the presence of general polygonal obstacles that block the players' motions. The pursuers cooperate to protect a convex region from the evaders who try to reach the region. We propose a multiplayer onsite and close-to-goal (MOCG) pursuit strategy that can tell and achieve an increasing lower bound on the number of guaranteed defeated evaders. This pursuit strategy fuses the subgame outcomes for multiple pursuers against one evader with hierarchical optimal task allocation in the receding-horizon manner. To determine the qualitative subgame outcomes that who is the game winner, we construct three pursuit winning regions and strategies under which the pursuers guarantee to win against the evader, regardless of the unknown evader strategy. First, we utilize the expanded Apollonius circles and propose the onsite pursuit winning that achieves the capture in finite time. Second, we introduce convex goal-covering polygons (GCPs) and propose the close-to-goal pursuit winning for the pursuers whose visibility region contains the whole protected region, and the goal-visible property will be preserved afterwards. Third, we employ Euclidean shortest paths (ESPs) and construct a pursuit winning region and strategy for the non-goal-visible pursuers, where the pursuers are firstly steered to positions with goal visibility along ESPs. In each horizon, the hierarchical optimal task allocation maximizes the number of defeated evaders and consists of four sequential matchings: capture, enhanced, non-dominated and closest matchings. Numerical examples are presented to illustrate the results.<|reference_end|>
@article{yan2024pursuit,
    title={Pursuit Winning Strategies for Reach-Avoid Games with Polygonal
  Obstacles},
    author={Rui Yan, Shuai Mi, Xiaoming Duan, Jintao Chen and Xiangyang Ji},
    journal={arXiv preprint arXiv:2403.06202},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06202},
    primaryClass={eess.SY cs.GT cs.SY}
}