Decentralised Self-Organising Maps for Multi-Robot Information Gathering
Decentralised Self-Organising Maps for Multi-Robot Information Gathering
复制标题
用于多机器人信息收集的分散自组织地图
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Geoffrey A. Hollinger
中科院分区：
文献类型：
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作者：
Graeme Best;Geoffrey A. Hollinger
提出了一种新的分布式多机器人信息采集协调算法。我们考虑计划一个在线变体的多智能体定向问题与社区。这一提法与机器人技术中的一些重要任务密切相关，包括检查、监视和侦察。我们提出了一种分散的自组织映射(SOM)学习过程，称为DEC-SOM，它有效地为一组机器人规划路线点序列。分散化是通过执行分布式分配方案和一系列SOM调整来实现的。我们还提供了一种有效的启发式方法来选择何时执行协商，从而减少了通信资源的使用。在两种情况下的模拟结果，包括具有真实世界石油钻井平台数据集的基础设施检查场景，表明DEC-SOM的性能优于基线方法和其他SOM变体，与集中式SOM具有竞争力，是一种可行的分散信息收集解决方案。
This paper presents a new coordination algorithm for decentralised multi-robot information gathering. We consider planning for an online variant of the multi-agent orienteering problem with neighbourhoods. This formulation closely aligns with a number of important tasks in robotics, including inspection, surveillance, and reconnaissance. We propose a decentralised variant of the self-organising map (SOM) learning procedure, named Dec-SOM, which efficiently plans sequences of waypoints for a team of robots. Decentralisation is achieved by performing a distributed allocation scheme jointly with a series of SOM adaptations. We also offer an efficient heuristic to select when to perform negotiations, which reduces communication resource usage. Simulation results in two settings, including an infrastructure inspection scenario with a real-world dataset of oil rigs, demonstrate that Dec-SOM outperforms baseline methods and other SOM variants, is competitive with centralised SOM, and is a viable solution for decentralised information gathering.