Real-Time Area Coverage and Target Localization using Receding-Horizon Ergodic Exploration

TL;DR

This paper introduces a receding-horizon ergodic control method that unifies coverage, search, and target localization tasks, providing real-time, stable, and multi-agent capable solutions adaptable to various scenarios.

Contribution

It develops a hybrid systems-based nonlinear model predictive control approach that unifies coverage, search, and localization in a real-time, distributed framework with stability guarantees.

Findings

Effective in simulation and real-world experiments

Independent of the number of targets tracked

Operates in real-time on limited hardware

Abstract

Although a number of solutions exist for the problems of coverage, search and target localization---commonly addressed separately---whether there exists a unified strategy that addresses these objectives in a coherent manner without being application-specific remains a largely open research question. In this paper, we develop a receding-horizon ergodic control approach, based on hybrid systems theory, that has the potential to fill this gap. The nonlinear model predictive control algorithm plans real-time motions that optimally improve ergodicity with respect to a distribution defined by the expected information density across the sensing domain. We establish a theoretical framework for global stability guarantees with respect to a distribution. Moreover, the approach is distributable across multiple agents, so that each agent can independently compute its own control while sharing statistics of its coverage across a communication network. We demonstrate the method in both simulation and in experiment in the context of target localization, illustrating that the algorithm is independent of the number of targets being tracked and can be run in real-time on computationally limited hardware platforms.