In this paper, we propose a probabilistic consensus-based multi-robot search strategy that is robust to communication link failures, and thus is suitable for disaster affected areas. The robots, capable of only local communication, explore a bounded environment according to a random walk modeled by a discrete-time discrete-state (DTDS) Markov chain and exchange information with neighboring robots, resulting in a time-varying communication network topology. The proposed strategy is proved to achieve consensus, here defined as agreement on the presence of a static target, with no assumptions on the connectivity of the communication network. Using numerical simulations, we investigate the effect of the robot population size, domain size, and information uncertainty on the consensus time statistics under this scheme. We also validate our theoretical results with 3D physics-based simulations in Gazebo. The simulations demonstrate that all robots achieve consensus in finite time with the proposed search strategy over a range of robot densities in the environment.

中文翻译：

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在离散马尔可夫链上使用概率共识的多机器人目标搜索

在本文中，我们提出了一种基于概率共识的多机器人搜索策略，该策略对通信链路故障具有鲁棒性，因此适用于受灾地区。机器人只能进行本地通信，根据由离散时间离散状态 (DTDS) 马尔可夫链建模的随机游走探索有界环境，并与相邻机器人交换信息，从而形成随时间变化的通信网络拓扑。所提出的策略被证明可以达成共识，这里定义为就静态目标的存在达成一致，对通信网络的连通性没有任何假设。使用数值模拟，我们研究了机器人种群大小、域大小和信息不确定性对这种方案下共识时间统计的影响。我们还通过 Gazebo 中基于 3D 物理的模拟验证了我们的理论结果。模拟表明，所有机器人在有限的时间内通过所提出的搜索策略在环境中的机器人密度范围内达成共识。