Deploying multiple robots for target search and tracking has many practical applications, yet the challenge of planning over unknown or partially known targets remains difficult to address. With recent advances in deep learning, intelligent control techniques such as reinforcement learning have enabled agents to learn autonomously from environment interactions with little to no prior knowledge. Such methods can address the exploration-exploitation tradeoff of planning over unknown targets in a data-driven manner, eliminating the reliance on heuristics typical of traditional approaches and streamlining the decision-making pipeline with end-to-end training. In this paper, we propose a multi-agent reinforcement learning technique with target map building based on distributed Gaussian process. We leverage the distributed Gaussian process to encode belief over the target locations and efficiently plan over unknown targets. We evaluate the performance and transferability of the trained policy in simulation and demonstrate the method on a swarm of micro unmanned aerial vehicles with hardware experiments.

翻译：在未知或部分已知目标场景下规划多机器人进行搜索与追踪具有广泛的实际应用价值，但始终是难以解决的挑战。随着深度学习的最新进展，强化学习等智能控制技术使智能体能够在极少先验知识的情况下通过与环境的交互自主进行学习。这类方法能够以数据驱动的方式解决未知目标规划中的探索-利用权衡问题，消除传统方法对启发式策略的依赖，并通过端到端训练简化决策流程。本文提出一种基于分布式高斯过程构建目标地图的多智能体强化学习技术。我们利用分布式高斯过程对目标位置进行置信度编码，从而高效规划对未知目标的搜索。通过仿真实验评估训练策略的性能与可迁移性，并在微型无人机集群硬件实验中验证该方法的有效性。