This paper presents a distributed scalable multi-robot planning algorithm for informed sampling of quasistatic spatial fields. We address the problem of efficient data collection using multiple autonomous vehicles and consider the effects of communication between multiple robots, acting independently, on the overall sampling performance of the team. We focus on the distributed sampling problem where the robots operate independent of their teammates, but have the ability to communicate their current state to other neighbors within a fixed communication range. Our proposed approach is scalable and adaptive to various environmental scenarios, changing robot team configurations, and runs in real-time, which are important features for many real-world applications. We compare the performance of our proposed algorithm to baseline strategies through simulated experiments that utilize models derived from both synthetic and field deployment data. The results show that our sampling algorithm is efficient even when robots in the team are operating with a limited communication range, thus demonstrating the scalability of our method in sampling large-scale environments.
翻译:本文介绍了用于对准静态空间领域进行知情抽样的分布式可扩缩多机器人规划算法。我们解决了使用多自动车辆有效收集数据的问题,并审议了多个机器人独立行动对小组总体取样业绩的影响。我们侧重于分散式抽样问题,即机器人独立于其队友运作,但有能力在固定通信范围内将其现状告知其他邻居。我们建议的方法是可扩缩和适应各种环境情景,改变机器人团队配置,并实时运行,这是许多现实世界应用的重要特征。我们通过模拟实验将我们提议的算法的性能与基线战略进行比较,这些模拟实验利用了来自合成和实地部署数据的模型。结果显示,即使小组中的机器人在有限的通信范围内运作,我们的采样算法也是有效的,从而表明我们取样大规模环境的方法的可扩缩性。