Decentralized Localization in Homogeneous Swarms Considering Real-World Non-Idealities
Decentralized Localization in Homogeneous Swarms Considering Real-World Non-Idealities
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DOI：
10.1109/lra.2021.3095032
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发表时间：
2021-10
影响因子：
5.2
通讯作者：
Hanlin Wang;Michael Rubenstein
中科院分区：
文献类型：
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作者：
Hanlin Wang;Michael Rubenstein
这封信提出了一个分散的算法，允许一群相同编程的代理使用局部距离和方位测量来合作估计它们的全局姿势。该算法的设计明确考虑了每个代理的本地时钟的相位异步性，并且算法的执行不需要每个代理在一段时间内主动保持相同的邻居。对每个智能体的感知噪声和通信损耗的影响进行了理论分析，并通过在256个模拟机器人群和100个物理机器人群上运行的实验验证了所提算法的有效性。实验结果表明，该算法能够快速、可靠地估计出各智能体的全局姿态。在[1]和[2]中可以找到执行所述算法的100个机器人的视频以及补充材料。
This letter presents a decentralized algorithm that allows a swarm of identically programmed agents to cooperatively estimate their global poses using local range and bearing measurements. The design of our algorithm explicitly considers the phase asynchrony of each agent's local clock, moreover, the execution of our algorithm does not require each agent to actively keep the same neighbors over time. A theoretical analysis about the effect of each agent's sensing noise and communication loss is given, in addition, we validate the presented algorithm via experiments running on a swarm of up to 256 simulated robots and a swarm of 100 physical robots. The results from the experiments show that the presented algorithm allows each agent to estimate its global pose quickly and reliably. Video of 100 robots executing the presented algorithm as well as supplementary material can be found in [1] and [2].