Decentralized Ergodic Coverage Control in Unknown Time-Varying Environments

TL;DR

This paper introduces a decentralized multi-agent coverage strategy for UAVs that adaptively tracks evolving importance maps in unknown, time-varying environments, enhancing disaster response capabilities.

Contribution

It proposes a novel ergodic policy framework using Gaussian Processes and Markov chains for adaptive, decentralized coverage in dynamic, partially observable settings.

Findings

Outperforms existing strategies in dynamic environment scenarios

Effectively balances exploration and exploitation in time-varying environments

Demonstrates improved adaptability and transient response in simulations

Abstract

A key challenge in disaster response is maintaining situational awareness of an evolving landscape, which requires balancing exploration of unobserved regions with sustained monitoring of changing Regions of Interest (ROIs). Unmanned Aerial Vehicles (UAVs) have emerged as an effective response tool, particularly in applications like environmental monitoring and search-and-rescue, due to their ability to provide aerial coverage, withstand hazardous conditions, and navigate quickly and flexibly. However, efficient and adaptable multi-robot coverage with limited sensing in disaster settings and evolving time-varying information maps remains a significant challenge, necessitating better methods for UAVs to continuously adapt their trajectories in response to changes. In this paper, we propose a decentralized multi-agent coverage framework that serves as a high-level planning strategy for adaptive coverage in unknown, time-varying environments under partial observability. Each agent computes an adaptive ergodic policy, implemented via a Markov-chain transition model, that tracks a continuously updated belief over the underlying importance map. Gaussian Processes are used to perform those online belief updates. The resulting policy drives agents to spend time in ROIs proportional to their estimated importance, while preserving sufficient exploration to detect and adapt to time-varying environmental changes. Unlike existing approaches that assume known importance maps, require centralized coordination, or assume a static environment, our framework addresses the combined challenges of unknown, time-varying distributions in a more realistic decentralized and partially observable setting. We compare against alternative coverage strategies and analyze our method's response to simulated disaster evolution, highlighting its improved adaptability and transient performance in dynamic scenarios.