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.

在异步的多智能体主动搜索任务中，我们提出了广义不确定性感知汤普森抽样算法(GUTS)来实现灾难响应的机器人解决方案，并将其部署在大型不规则环境中的异构多机器人系统上。我们通过模拟实验和现场测试表明，GUTS算法能够高效准确地找到感兴趣的物体(OOIs)，并在各种故障模式下实现了系统的鲁棒性，这使得其在回收OOIs方面的表现优于现有方法。

GUTS：针对多智能体主动搜索的广义不确定性感知 Thompson抽样