Finite-State Decentralized Policy-Based Control With Guaranteed Ground Coverage

2026-01-05

arXiv.org

AI Summary

This work addresses the lack of a scalable, decentralized multi-agent control framework for area coverage tasks that offers theoretical convergence guarantees. The authors propose a finite-state, decentralized policy-based control approach that decouples the coverage problem into two components: a reference-configuration-guided deep neural network design and a distributed control strategy based on agent policies. Central to their method are an anchor-follower triangular communication topology and the novel concept of Anyway Output Controllability (AOC). This framework yields a computationally efficient, time-invariant control policy capable of dynamically adapting to changes in the target region while theoretically ensuring convergence to the optimal coverage configuration, thereby achieving scalable and collaboratively efficient multi-agent coverage control.

Technology Category

• Multiagent Systems: Agent Communication
• Intelligent Robots: State Estimation
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems

Abstract

We propose a finite-state, decentralized decision and control framework for multi-agent ground coverage. The approach decomposes the problem into two coupled components: (i) the structural design of a deep neural network (DNN) induced by the reference configuration of the agents, and (ii) policy-based decentralized coverage control. Agents are classified as anchors and followers, yielding a generic and scalable communication architecture in which each follower interacts with exactly three in-neighbors from the preceding layer, forming an enclosing triangular communication structure. The DNN training weights implicitly encode the spatial configuration of the agent team, thereby providing a geometric representation of the environmental target set. Within this architecture, we formulate a computationally efficient decentralized Markov decision process (MDP) whose components are time-invariant except for a time-varying cost function defined by the deviation from the centroid of the target set contained within each agent communication triangle. By introducing the concept of Anyway Output Controllability (AOC), we assume each agent is AOC and establish decentralized convergence to a desired configuration that optimally represents the environmental target.

Problem

• multi-agent coverage
• decentralized control
• ground coverage
• finite-state control
• coverage guarantee

Innovation

• decentralized control
• deep neural network
• multi-agent coverage
• Anyway Output Controllability
• triangular communication topology