论文标题
使用检测和位置不确定性的多代理主动搜索
Multi-Agent Active Search using Detection and Location Uncertainty
论文作者
论文摘要
在环境监控或灾难响应任务等应用程序中，主动搜索涉及使用适应其观察史的决策算法在搜索空间中检测目标的自主媒介。主动搜索算法必须与两种类型的不确定性抗衡：检测不确定性和位置不确定性。机器人技术中更常见的方法是通过将检测概率归为零或一个来关注位置不确定性并消除检测不确定性。相反，在稀疏信号处理文献中，假设目标位置是准确的，而是专注于检测的不确定性。在这项工作中，我们首先提出了一种共同处理目标检测和位置不确定性的推理方法。然后，我们对这种推理方法构建决策算法，该方法使用汤普森采样来实现分散的多代理主动搜索。我们执行仿真实验，以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。最终，我们使用使用AirSim插件在Unreal Engine 4平台上创建的逼真的模拟环境来证明我们的算法的现实可传递性。
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.
