The multi-robot adaptive sampling problem aims at finding trajectories for a team of robots to efficiently sample the phenomenon of interest within a given endurance budget of the robots. In this paper, we propose a robust and scalable approach using Multi-Agent Reinforcement Learning for cooperated Adaptive Sampling (MARLAS) of quasi-static environmental processes. Given a prior on the field being sampled, the proposed method learns decentralized policies for a team of robots to sample high-utility regions within a fixed budget. The multi-robot adaptive sampling problem requires the robots to coordinate with each other to avoid overlapping sampling trajectories. Therefore, we encode the estimates of neighbor positions and intermittent communication between robots into the learning process. We evaluated MARLAS over multiple performance metrics and found it to outperform other baseline multi-robot sampling techniques. Additionally, we demonstrate scalability with both the size of the robot team and the size of the region being sampled. We further demonstrate robustness to communication failures and robot failures. The experimental evaluations are conducted both in simulations on real data and in real robot experiments on demo environmental setup.

翻译：多机器人自适应采样问题旨在为机器人团队寻找轨迹，以在给定的机器人续航预算内高效采样感兴趣的现象。本文提出了一种鲁棒且可扩展的方法——基于多智能体强化学习的协同自适应采样（MARLAS），用于准静态环境过程。在给定被采样场先验信息的情况下，该方法为机器人团队学习分散式策略，以在固定预算内对高价值区域进行采样。多机器人自适应采样问题要求机器人之间相互协调以避免采样轨迹重叠。因此，我们将邻居位置估计和机器人间间歇通信编码到学习过程中。我们在多个性能指标上评估了MARLAS，发现其优于其他基线多机器人采样技术。此外，我们展示了该方法在机器人团队规模和采样区域规模上的可扩展性，并进一步证明了其应对通信故障和机器人故障的鲁棒性。实验评估既在基于真实数据的仿真环境中进行，也在演示环境设置的实际机器人实验中开展。