论文标题
使用离散马尔可夫链上的概率共识的多机器人目标搜索
Multi-Robot Target Search using Probabilistic Consensus on Discrete Markov Chains
论文作者
论文摘要
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，该策略对通信链接失败非常可靠，因此适合受灾地区。这些机器人只能仅能本地通信，根据由离散时间离散状态（DTD）模型的随机步行探索一个有限的环境，马尔可夫链和与相邻机器人交换信息，从而产生了时间变化的通信网络拓扑。事实证明，所提出的策略可以达成共识，在这里被定义为在存在静态目标上的一致性，而没有关于通信网络连通性的假设。使用数值模拟，我们研究了机器人总体规模，域大小和信息不确定性对此方案中共识时间统计数据的影响。我们还通过凉亭中的基于3D物理学的模拟来验证我们的理论结果。模拟表明，所有机器人都在有限的时间内与拟议的搜索策略在环境中的一系列机器人密度上达成共识。
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.
