This paper introduces a probabilistic guidance approach for the swarm-to-swarm engagement problem. The idea is based on driving the controlled swarm towards an adversary swarm, where the adversary swarm aims to converge to a stationary distribution that corresponds to a defended base location. The probabilistic approach is based on designing a Markov chain for the distribution of the swarm to converge a stationary distribution. This approach is decentralized, so each agent can propagate its position independently of other agents. Our main contribution is the formulation of the swarm-to-swarm engagement as an optimization problem where the population of each swarm decays with each engagement and determining a desired distribution for the controlled swarm to converge time-varying distribution and eliminate agents of the adversary swarm until adversary swarm enters the defended base location. We demonstrate the validity of proposed approach on several swarm engagement scenarios.

翻译：本文为群集至群集的接触问题引入了一种概率性指导方法。 其基础思想是将控控的群落推向敌体群, 敌体群旨在汇合到与防守基地位置相对的固定分布点。 概率性方法的基础是设计一个Markov链条, 以分配群集, 凝聚固定分布点。 这种方法是分散的, 使每个代理人可以独立于其他代理人而传播其位置。 我们的主要贡献是将控控的群落形成一个优化问题, 使每群群群群群的人口随每次参与而衰减, 并确定受控的群群群的预期分布点, 以聚集时间变化分布, 消灭敌体群的代理人, 直至敌体群进入防御基地位置。 我们展示了在数种群集的接触情景上拟议方法的有效性 。