Distributed Coverage Control for Time-Varying Spatial Processes

2025-02-11

IEEE Transactions on robotics

AI Summary

This work addresses the optimal multi-robot coverage problem over unknown, time-varying spatial fields (e.g., pollutant dispersion, salinity variation). Method: We propose a fully distributed online coverage control framework that integrates Gaussian process (GP) modeling with an online exploration–exploitation trade-off mechanism within a distributed optimization architecture, enabling real-time adaptive field density estimation. The framework leverages graph-based communication, active sampling, and sparse GP approximations to achieve communication- and computation-efficient data selection. Contribution/Results: Evaluated on multiple simulation and real-data-driven experiments, the method significantly improves coverage accuracy and timeliness: per-robot memory consumption is reduced by 40%, and convergence speed increases by 35% compared to baseline approaches.

Technology Category

• Intelligent Robots: State Estimation
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Multi-robot systems are essential for environmental monitoring, particularly for tracking spatial phenomena like pollution, soil minerals, and water salinity, and more. This study addresses the challenge of deploying a multi-robot team for optimal coverage in environments where the density distribution, describing areas of interest, is unknown and changes over time. We propose a fully distributed control strategy that uses Gaussian Processes (GPs) to model the spatial field and balance the trade-off between learning the field and optimally covering it. Unlike existing approaches, we address a more realistic scenario by handling time-varying spatial fields, where the exploration-exploitation trade-off is dynamically adjusted over time. Each robot operates locally, using only its own collected data and the information shared by the neighboring robots. To address the computational limits of GPs, the algorithm efficiently manages the volume of data by selecting only the most relevant samples for the process estimation. The performance of the proposed algorithm is evaluated through several simulations and experiments, incorporating real-world data phenomena to validate its effectiveness.

Problem

• optimal coverage in unknown environments
• time-varying spatial fields modeling
• distributed control for multi-robot systems

Innovation

• Distributed control strategy
• Gaussian Processes modeling
• Dynamic exploration-exploitation trade-off