论文标题
群体到劳动互动问题的概率指导方法
A Probabilistic Guidance Approach to Swarm-to-Swarm Engagement Problem
论文作者
论文摘要
本文介绍了针对群体互动问题的概率指导方法。这个想法是基于将受控的群体驱动到对手群的基础上，在那里，对手群的目的是将其汇聚到与防御基本位置相对应的固定分布中。概率方法是基于设计马尔可夫链，用于分布群体以融合固定分布。这种方法是分散的，因此每个代理都可以独立于其他代理传播其位置。我们的主要贡献是将群体与潮湿的参与作为优化问题的制定，其中每个群体的种群随着每次互动而衰减，并确定受控群体的所需分布，以收敛时间变化的分布并消除对手群体的药物，直到对手群体进入防御性的基本位置。我们证明了在几种群参与方案中提出的方法的有效性。
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.
