Decentralized Ergodic Coverage Control in Unknown Time-Varying Environments

2026-04-05

AI Summary

This work addresses the challenge of balancing exploration and sustained monitoring of dynamic regions of interest for multi-UAV systems operating in unknown, time-varying disaster environments with limited perceptual capabilities. The authors propose a decentralized multi-agent coverage control framework that, for the first time, jointly models time-varying importance distributions under partial observability and without prior map knowledge. By integrating Markov chains to generate adaptive ergodic policies, the approach guides UAVs to dwell in regions proportionally to their environmental significance, while online belief updates via Gaussian processes preserve exploratory capacity. Experimental results demonstrate that the proposed framework significantly outperforms existing methods in dynamic disaster scenarios, exhibiting superior transient coverage performance and environmental adaptability.

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.

Problem

• decentralized coverage
• time-varying environments
• multi-UAV coordination
• partial observability
• adaptive exploration

Innovation

• decentralized coverage control
• ergodic policy
• time-varying environments
• Gaussian Processes
• partial observability