基于深度强化学习的异构机器人系统目标搜索与导航
Target Search and Navigation in Heterogeneous Robot Systems with Deep Reinforcement Learning
AI总结：
针对异构机器人协作搜救训练中协作奖励难获取的问题，提出多阶段强化学习框架与好奇心模块，可在目标位置未知的迷宫式矿井环境中完成搜索导航，且训练速度优于现有基线。
摘要：
协作式异构机器人系统可大幅提升目标搜索与导航任务的效率。本文设计了一套由无人机（UAV）和无人地面车辆（UGV）组成的异构机器人系统，用于未知环境中的搜救任务。该系统借助深度强化学习算法习得的策略，能够在迷宫式矿井环境中搜索目标并导航至目标位置。训练过程中，若同时训练两台机器人，二者协作相关的奖励可能无法被合理获取。因此，我们提出了一种多阶段强化学习框架以及一个好奇心模块，以鼓励智能体探索未访问过的环境。仿真环境中的实验表明，我们的框架能够训练异构机器人系统在目标位置未知的情况下完成搜索与导航任务，而现有基线方法可能无法实现，同时该框架还能提升训练速度。
英文摘要：
Collaborative heterogeneous robot systems can greatly improve the efficiency of target search and navigation tasks. In this paper, we design a heterogeneous robot system consisting of a UAV and a UGV for search and rescue missions in unknown environments. The system is able to search for targets and navigate to them in a maze-like mine environment with the policies learned through deep reinforcement learning algorithms. During the training process, if two robots are trained simultaneously, the rewards related to their collaboration may not be properly obtained. Hence, we introduce a multi-stage reinforcement learning framework and a curiosity module to encourage agents to explore unvisited environments. Experiments in simulation environments show that our framework can train the heterogeneous robot system to achieve the search and navigation with unknown target locations while existing baselines may not, and accelerate the training speed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
