Rapid search and rescue is critical to maximizing survival rates following natural disasters. However, these efforts are challenged by the need to search large disaster zones, lack of reliability in the communications infrastructure, and a priori unknown numbers of objects of interest (OOIs), such as injured survivors. Aerial robots are increasingly being deployed for search and rescue due to their high mobility, but there remains a gap in deploying multi-robot autonomous aerial systems for methodical search of large environments. Prior works have relied on preprogrammed paths from human operators or are evaluated only in simulation. We bridge these gaps in the state of the art by developing and demonstrating a decentralized active search system, which biases its trajectories to take additional views of uncertain OOIs. The methodology leverages stochasticity for rapid coverage in communication denied scenarios. When communications are available, robots share poses, goals, and OOI information to accelerate the rate of search. Extensive simulations and hardware experiments in Bloomingdale, OH, are conducted to validate the approach. The results demonstrate the active search approach outperforms greedy coverage-based planning in communication-denied scenarios while maintaining comparable performance in communication-enabled scenarios.

翻译：快速搜救对于自然灾害后最大化幸存率至关重要。然而，这些工作面临着以下挑战：需要搜索大型灾区、通信基础设施可靠性不足，以及目标对象（如受伤幸存者）数量先验未知。凭借其高机动性，空中机器人正越来越多地被部署用于搜救任务，但在部署多机器人自主空中系统以系统化搜索大型环境方面仍存在差距。先前的研究依赖于人类操作员预设路径，或仅在仿真环境中进行评估。我们通过开发并演示一种去中心化主动搜索系统来弥补现有技术的这些不足，该系统通过偏置其轨迹以获取对不确定目标对象的额外观测。该方法利用随机性在通信受限场景中实现快速覆盖。当通信可用时，机器人共享位姿、目标及目标对象信息以加速搜索进程。我们在俄亥俄州布卢明代尔进行了大量仿真和硬件实验以验证该方法。结果表明，在通信受限场景中，主动搜索方法优于基于贪婪覆盖的规划方法，同时在通信可用场景中保持相当的性能。