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:多机器人适应性取样问题旨在为一组机器人寻找轨迹,以便在机器人的特定耐力预算范围内对感兴趣的现象进行高效抽样。在本文中,我们建议采用一种稳健和可伸缩的方法,使用多代理强化学习方法对准静态环境过程进行合作的适应性取样(MARLAS ) 。鉴于在取样的实地之前,建议的方法为一组机器人了解分散政策,以便在固定预算范围内对高功率区域进行抽样。多机器人适应性取样问题要求机器人相互协调,以避免取样轨迹重叠。因此,我们将邻居位置的估计数和机器人间歇性通信纳入学习过程。我们用多种性能衡量标准对MARLAS进行了评估,发现它比其他基线多机器人取样技术要强。此外,我们证明机器人团队的规模和所采样区域的规模具有伸缩性。我们进一步显示通信失败和机器人失败的稳健性。实验性评估是在真实数据模拟和真实的机器人模拟中进行。