Robotic swarms and mobile sensor networks are used for environmental monitoring in various domains and areas of operation. Especially in otherwise inaccessible environments decentralized robotic swarms can be advantageous due to their high spatial resolution of measurements and resilience to failure of individuals in the swarm. However, such robotic swarms might need to be able to compensate misplacement during deployment or adapt to dynamical changes in the environment. Reaching a collective decision in a swarm with limited communication abilities without a central entity serving as decision-maker can be a challenging task. Here we present the CIMAX algorithm for collective decision making for maximizing the information gathered by the swarm as a whole. Agents negotiate based on their individual sensor readings and ultimately make a decision for collectively moving in a particular direction so that the swarm as a whole increases the amount of relevant measurements and thus accessible information. We use both simulation and real robotic experiments for presenting, testing and validating our algorithm. CIMAX is designed to be used in underwater swarm robots for troubleshooting an oxygen depletion phenomenon known as "anoxia".

中文翻译：

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CIMAX：使用本地通信的机器人群中的集体信息最大化

机器人群和移动传感器网络用于各个领域和操作区域的环境监测。特别是在其他无法进入的环境中，分散式机器人群可能是有利的，因为它们具有高空间分辨率的测量和对群中个体故障的恢复能力。然而，这样的机器人群可能需要能够在部署过程中补偿错位或适应环境的动态变化。在没有中央实体作为决策者的情况下，在沟通能力有限的群体中达成集体决策可能是一项具有挑战性的任务。在这里，我们提出了用于集体决策的 CIMAX 算法，以最大化整个群体收集的信息。代理根据他们各自的传感器读数进行协商，并最终做出集体向特定方向移动的决定，以便整个群体增加相关测量的数量，从而增加可访问的信息。我们使用模拟和真实机器人实验来展示、测试和验证我们的算法。CIMAX 旨在用于水下群体机器人，用于解决称为“缺氧”的缺氧现象。