Multi-Feature Collective Decision Making in Robot Swarms
Multi-Feature Collective Decision Making in Robot Swarms
复制标题
机器人群中的多特征集体决策
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
R. Nagpal
中科院分区：
文献类型：
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作者：
J. Ebert;Melvin Gauci;R. Nagpal
集体决策在多机构系统和群机器人技术领域进行了广泛的研究，其灵感来自于其在蜜蜂和蚂蚁菌落等生物系统中的普遍性。但是，以前的大多数研究都集中在一项功能上的集体决策上。在这项工作中，我们介绍并调查了多种功能的集体决策问题，集体必须同时决定多个二进制功能，因为没有关于其相对困难的先验信息。每个代理只能在任何给定时间估算一个功能，但是代理商可以在本地传达其嘈杂的估计以做出决定。我们演示了一种分散的单场决策算法和动态的任务分配策略，该策略使代理可以在有限的时间内锁定多个功能的决策。我们使用模拟和物理千射击机器人验证方法。我们的结果表明，集体可以正确地对多功能的环境进行分类，即使进行了病理初始药物之间的分配。
Collective decision making has been studied extensively in the fields of multi-agent systems and swarm robotics, inspired by its pervasiveness in biological systems such as honeybee and ant colonies. However, most previous research has focused on collective decision making on a single feature. In this work, we introduce and investigate the multi-feature collective decision making problem, where a collective must decide on multiple binary features simultaneously, given no a priori information about their relative difficulties. Each agent may only estimate one feature at any given time, but the agents can locally communicate their noisy estimates to arrive at a decision. We demonstrate a decentralized algorithm for single-feature decision making and a dynamic task allocation strategy that allows the agents to lock in decisions on multiple features in finite time. We validate our approach using simulated and physical Kilobot robots. Our results show that a collective can correctly classify a multi-feature environment, even if presented with pathological initial agent-to-feature allocations.