Connectivity-Preserving Multi-Agent Area Coverage via Optimal-Transport-Based Density-Driven Optimal Control (D2OC)

2025-11-23

AI Summary

In multi-agent heterogeneous area coverage, dynamic constraints, fragile communication connectivity, and degraded coverage quality are strongly coupled—posing a fundamental challenge. Method: This paper proposes a density-driven optimal control framework grounded in optimal transport theory. It quantifies the discrepancy between agent distribution and a spatially varying priority reference density via the Wasserstein distance, and introduces a smooth connectivity-penalty term that explicitly enforces persistent communication graph connectivity while preserving convexity of the optimization problem. The resulting model is a convex quadratic program amenable to fully distributed solution. Results: Simulations demonstrate that the method simultaneously guarantees strict connectivity maintenance, significantly improves convergence speed and coverage accuracy, and overcomes the inherent disconnection drawback of conventional density-driven approaches that neglect communication constraints.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

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: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Multi-agent systems play a central role in area coverage tasks across search-and-rescue, environmental monitoring, and precision agriculture. Achieving non-uniform coverage, where spatial priorities vary across the domain, requires coordinating agents while respecting dynamic and communication constraints. Density-driven approaches can distribute agents according to a prescribed reference density, but existing methods do not ensure connectivity. This limitation often leads to communication loss, reduced coordination, and degraded coverage performance. This letter introduces a connectivity-preserving extension of the Density-Driven Optimal Control (D2OC) framework. The coverage objective, defined using the Wasserstein distance between the agent distribution and the reference density, admits a convex quadratic program formulation. Communication constraints are incorporated through a smooth connectivity penalty, which maintains strict convexity, supports distributed implementation, and preserves inter-agent communication without imposing rigid formations. Simulation studies show that the proposed method consistently maintains connectivity, improves convergence speed, and enhances non-uniform coverage quality compared with density-driven schemes that do not incorporate explicit connectivity considerations.

Problem

• Maintaining multi-agent connectivity during non-uniform area coverage tasks
• Ensuring communication while distributing agents according to reference density
• Overcoming connectivity loss in density-driven coverage control methods

Innovation

• Optimal-transport-based density-driven optimal control
• Convex quadratic program with Wasserstein distance
• Smooth connectivity penalty preserving strict convexity