Active search, in applications like environment monitoring or disaster response missions, involves autonomous agents detecting targets in a search space using decision making algorithms that adapt to the history of their observations. Active search algorithms must contend with two types of uncertainty: detection uncertainty and location uncertainty. The more common approach in robotics is to focus on location uncertainty and remove detection uncertainty by thresholding the detection probability to zero or one. In contrast, it is common in the sparse signal processing literature to assume the target location is accurate and instead focus on the uncertainty of its detection. In this work, we first propose an inference method to jointly handle both target detection and location uncertainty. We then build a decision making algorithm on this inference method that uses Thompson sampling to enable decentralized multi-agent active search. We perform simulation experiments to show that our algorithms outperform competing baselines that only account for either target detection or location uncertainty. We finally demonstrate the real world transferability of our algorithms using a realistic simulation environment we created on the Unreal Engine 4 platform with an AirSim plugin.

翻译：主动搜索在环境监测或灾难响应任务等应用中，涉及自主智能体利用适应其观测历史信息的决策算法，在搜索空间中检测目标。主动搜索算法需应对两种不确定性：检测不确定性与定位不确定性。机器人学中更常见的做法是聚焦定位不确定性，并通过将检测概率阈值化为0或1来消除检测不确定性。相比之下，稀疏信号处理文献通常假定目标位置准确，而关注其检测的不确定性。本研究首先提出一种联合处理目标检测与定位不确定性的推断方法。在此基础上，我们构建了基于该推断方法的决策算法，采用汤普森采样实现去中心化多智能体主动搜索。仿真实验表明，我们的算法优于仅考虑目标检测或定位不确定性的竞争基线方法。最后，我们利用在虚幻引擎4平台上结合AirSim插件创建的真实模拟环境，展示了算法的实际可迁移性。