Multi-Agent Active Search using Realistic Depth-Aware Noise Model

TL;DR

This paper introduces Noise-Aware Thompson Sampling (NATS), a decentralized algorithm for multi-agent active search that effectively handles depth-aware noise, occlusions, and detection uncertainties in unknown environments, outperforming existing methods.

Contribution

The paper presents NATS, a novel decentralized active search algorithm that incorporates depth-aware noise modeling and occlusion handling, advancing multi-agent search capabilities in robotics.

Findings

NATS outperforms existing search algorithms in simulation.

NATS demonstrates real-world viability in a pseudo-realistic environment.

The method effectively manages detection uncertainty and environmental occlusions.

Abstract

The active search for objects of interest in an unknown environment has many robotics applications including search and rescue, detecting gas leaks or locating animal poachers. Existing algorithms often prioritize the location accuracy of objects of interest while other practical issues such as the reliability of object detection as a function of distance and lines of sight remain largely ignored. Additionally, in many active search scenarios, communication infrastructure may be unreliable or unestablished, making centralized control of multiple agents impractical. We present an algorithm called Noise-Aware Thompson Sampling (NATS) that addresses these issues for multiple ground-based robots performing active search considering two sources of sensory information from monocular optical imagery and depth maps. By utilizing Thompson Sampling, NATS allows for decentralized coordination among multiple agents. NATS also considers object detection uncertainty from depth as well as environmental occlusions and operates while remaining agnostic of the number of objects of interest. Using simulation results, we show that NATS significantly outperforms existing methods such as information-greedy policies or exhaustive search. We demonstrate the real-world viability of NATS using a pseudo-realistic environment created in the Unreal Engine 4 game development platform with the AirSim plugin.