Matching-Based Capture Strategies for 3D Heterogeneous Multiplayer Reach-Avoid Differential Games

TL;DR

This paper develops a comprehensive strategy for heterogeneous pursuer teams to effectively defend a 3D region against multiple evaders in a complex differential game, using game decomposition, potential functions, and approximation algorithms.

Contribution

It introduces a novel decomposition approach, the evasion space method, and a polynomial-time approximation algorithm for pursuer-evader matching in 3D multiplayer reach-avoid games.

Findings

Pursuer teams of at most three members can defend the goal region against an evader.

The evasion space method guarantees pursuer winning strategies in 3D.

The MBMC problem is NP-hard, but a constant-factor approximation algorithm is proposed.

Abstract

This paper studies a 3D multiplayer reach-avoid differential game with a goal region and a play region. Multiple pursuers defend the goal region by consecutively capturing multiple evaders in the play region. The players have heterogeneous moving speeds and the pursuers have heterogeneous capture radii. Since this game is hard to analyze directly, we decompose the whole game as many subgames which involve multiple pursuers and only one evader. Then, these subgames are used as a building block for the pursuer-evader matching. First, for multiple pursuers and one evader, we introduce an evasion space (ES) method characterized by a potential function to construct a guaranteed pursuer winning strategy. Then, based on this strategy, we develop conditions to determine whether a pursuit team can guard the goal region against one evader. It is shown that in 3D, if a pursuit team is able to defend the goal region against an evader, then at most three pursuers in the team are necessarily needed. We also compute the value function of the Hamilton-Jacobi-Isaacs (HJI) equation for a special subgame of degree. To capture the maximum number of evaders in the open-loop sense, we formulate a maximum bipartite matching problem with conflict graph (MBMC). We show that the MBMC is NP-hard and design a polynomial-time constant-factor approximation algorithm to solve it. Finally, we propose a receding horizon strategy for the pursuit team where in each horizon an MBMC is solved and the strategies of the pursuers are given. We also extend our results to the case of a bounded convex play region where the evaders escape through an exit. Two numerical examples are provided to demonstrate the obtained results.