Decentralized Multi-Agent Active Search for Sparse Signals

Abstract

Active search refers to the problem of efficiently locating targets in an unknown environment by actively making data-collection decisions. In this paper, we are focusing on multiple aerial robots (agents) detecting targets such as gas leaks, radiation sources or human survivors of disasters. One of the main challenges of active search with multiple agents in unknown environments is impracticality of central coordination due to the difficulties of connectivity maintenance. In this paper, we propose two distinct active search algorithms that allow for multiple robots to independently make data-collection decisions without a central coordinator. Throughout we consider that targets are sparsely located around the environment in keeping with compressive sensing assumptions and its applicability in real world scenarios. Additionally, while most common sensing algorithms assume that agents can sense the entire environment (e.g. compressive sensing) or sense point-wise (e.g. Bayesian Optimization) at a time, we make a realistic assumption that each agent can only sense a contiguous region of space at each time step. We provide simulation results as well as theoretical analysis to demonstrate the efficacy of our proposed algorithms.