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.

翻译：协作异构机器人系统可显著提升目标搜索与导航任务的效率。本文设计了一种由无人机(UAV)与无人地面车(UGV)组成的异构机器人系统，用于未知环境中的搜救任务。该系统能够通过深度强化学习算法训练出的策略，在类似迷宫的地下环境中搜索并导航至目标位置。训练过程中，若同时训练两个机器人，与其协作相关的奖励信号可能无法被有效获取。为此，我们提出了一种多阶段强化学习框架及好奇心模块，以激励智能体探索未访问环境。仿真实验表明，该框架能够训练异构机器人系统在未知目标位置条件下完成搜索与导航任务（现有基线方法无法实现），同时可加速训练进程。