使用检测和位置不确定性的多智能体主动搜索
Multi-Agent Active Search using Detection and Location Uncertainty
AI总结：
本文提出了一种联合处理目标检测和位置不确定性的推理方法，并基于Thompson sampling构建去中心化多智能体主动搜索决策算法，仿真和真实环境实验证明其优于仅考虑单一不确定性的基线方法。
摘要：
主动搜索在环境监测或灾难响应任务等应用中，涉及自主智能体使用适应其观察历史的决策算法来检测搜索空间中的目标。主动搜索算法必须应对两种类型的不确定性：检测不确定性和位置不确定性。机器人学中更常见的方法是关注位置不确定性，并通过将检测概率阈值化为零或一来消除检测不确定性。相比之下，稀疏信号处理文献中通常假设目标位置是准确的，转而关注其检测的不确定性。在这项工作中，我们首先提出了一种推理方法来联合处理目标检测和位置不确定性。然后，我们在此推理方法上构建了一个决策算法，该算法使用Thompson sampling（汤普森采样）来实现去中心化的多智能体主动搜索。我们进行了仿真实验，表明我们的算法优于仅考虑目标检测或位置不确定性的竞争基线。最后，我们使用在Unreal Engine 4平台上创建的带有AirSim插件的真实仿真环境，证明了我们算法的现实世界可迁移性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
