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

TL;DR

This paper introduces a distributed control framework combining trajectory planning and model predictive control for multi-agent coverage of dynamic environments, with formal guarantees and hardware validation.

Contribution

It presents a novel multi-rate distributed control approach for time-varying coverage with formal guarantees and practical nonperiodic density handling.

Findings

Guaranteed convergence to optimal trajectories for periodic densities

Formal safety and collision avoidance guarantees

Validated on hardware with miniature race cars

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.