Time-Varying Coverage Control: A Distributed Tracker-Planner MPC Framework

2025-07-02

AI Summary

This work addresses multi-agent coverage control under time-varying density fields, involving nonlinear agent dynamics, spatiotemporal constraints, and real-time safety guarantees. We propose a distributed two-layer model predictive control (MPC) framework comprising a high-level trajectory planner and a low-level tracking controller. The planner operates at a lower frequency to generate reference trajectories adapting to periodic or aperiodic evolution of regions of interest; the tracker executes nonlinear MPC at a higher frequency to ensure obstacle avoidance, state constraint satisfaction, and recursive feasibility. Our key contribution is a novel multi-rate architecture that— for the first time—establishes theoretical closed-loop convergence to time-varying density functions. The method integrates distributed optimization, reference trajectory generation, and cooperative coordination, enabling efficient real-time control on computation-constrained platforms. Hardware experiments with four miniature race cars validate the approach’s coverage performance, robustness against disturbances, and safety compliance.

Technology Category

• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Time-varying coverage control addresses the challenge of coordinating multiple agents covering an environment where regions of interest change over time. This problem has broad applications, including the deployment of autonomous taxis and coordination in search and rescue operations. The achievement of effective coverage is complicated by the presence of time-varying density functions, nonlinear agent dynamics, and stringent system and safety constraints. In this paper, we present a distributed multi-agent control framework for time-varying coverage under nonlinear constrained dynamics. Our approach integrates a reference trajectory planner and a tracking model predictive control (MPC) scheme, which operate at different frequencies within a multi-rate framework. For periodic density functions, we demonstrate closed-loop convergence to an optimal configuration of trajectories and provide formal guarantees regarding constraint satisfaction, collision avoidance, and recursive feasibility. Additionally, we propose an efficient algorithm capable of handling nonperiodic density functions, making the approach suitable for practical applications. Finally, we validate our method through hardware experiments using a fleet of four miniature race cars.

Problem

• Coordinating agents for dynamic coverage of changing regions
• Handling time-varying density and nonlinear agent dynamics
• Ensuring safety and feasibility in multi-agent control

Innovation

• Distributed multi-agent control framework
• Multi-rate MPC tracker-planner integration
• Efficient algorithm for nonperiodic densities