Multi-Agent Active Search using Detection and Location Uncertainty

TL;DR

This paper introduces a novel inference and decision-making framework for multi-agent active search that jointly handles detection and location uncertainties, outperforming existing methods in simulation and real-world transferability.

Contribution

It proposes a new inference method and a decentralized Thompson sampling algorithm for active search that jointly considers detection and location uncertainties.

Findings

Our algorithms outperform baselines that consider only one type of uncertainty.

Simulation results show improved target detection efficiency.

Realistic Unreal Engine 4 environment demonstrates transferability.

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.