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.

在未知环境中设计了由无人机和无人地面车组成的异构机器人系统，通过深度强化学习算法学习的策略，该系统能够在迷宫状的矿山环境中搜索目标并导航到目标；通过引入多阶段强化学习框架和好奇心模块，促使机器人探索未访问的环境；在仿真环境中的实验证明我们的框架能够训练异构机器人系统完成未知目标位置的搜索和导航任务，而现有基线模型可能无法做到，并且加速训练速度。