Arundhati Banerjee, Ramina Ghods, Jeff Schneider
Carnegie Mellon University
Abstract: 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.
Paper: arxiv version
Code : Coming soon!
Following is a video demonstration of our proposed algorithm TS-UnIK with a single ground robot looking for 5 humans (the objects of interest or OOIs) in a 250mx250m search space discretized into 16x16 grid cells, simulated on a pseudo-realistic environment created with Unreal Engine 4 + AirSim.