Robotic solutions for quick disaster response are essential to ensure minimal loss of life, especially when the search area is too dangerous or too vast for human rescuers. We model this problem as an asynchronous multi-agent active-search task where each robot aims to efficiently seek objects of interest (OOIs) in an unknown environment. This formulation addresses the requirement that search missions should focus on quick recovery of OOIs rather than full coverage of the search region. Previous approaches fail to accurately model sensing uncertainty, account for occlusions due to foliage or terrain, or consider the requirement for heterogeneous search teams and robustness to hardware and communication failures. We present the Generalized Uncertainty-aware Thompson Sampling (GUTS) algorithm, which addresses these issues and is suitable for deployment on heterogeneous multi-robot systems for active search in large unstructured environments. We show through simulation experiments that GUTS consistently outperforms existing methods such as parallelized Thompson Sampling and exhaustive search, recovering all OOIs in 80% of all runs. In contrast, existing approaches recover all OOIs in less than 40% of all runs. We conduct field tests using our multi-robot system in an unstructured environment with a search area of approximately 75,000 sq. m. Our system demonstrates robustness to various failure modes, achieving full recovery of OOIs (where feasible) in every field run, and significantly outperforming our baseline.

翻译：为最大限度减少人员伤亡，快速灾难响应的机器人解决方案至关重要，尤其在搜索区域对人类救援人员而言过于危险或广阔时。我们将此问题建模为异步多智能体主动搜索任务，每个机器人旨在未知环境中高效搜寻感兴趣对象（OOI）。该公式满足了搜索任务应聚焦于快速回收OOI而非完全覆盖搜索区域的需求。先前方法未能准确建模感知不确定性、未能考虑植被或地形导致的遮挡，也未考虑异构搜索团队的需求及对硬件和通信故障的鲁棒性。我们提出广义不确定性感知汤普森采样（GUTS）算法，该算法解决了上述问题，适用于在大型非结构化环境中部署异构多机器人系统进行主动搜索。仿真实验表明，GUTS在80%的运行中成功回收所有OOI，持续优于并行化汤普森采样和穷举搜索等现有方法，而后者在不到40%的运行中实现全部回收。我们在面积约75000平方米的非结构化环境中使用多机器人系统进行实地测试。该系统展现出对多种故障模式的鲁棒性，在每次实地运行中均实现OOI的完全回收（可行范围内），显著优于基线方法。