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

TL;DR

This paper introduces a scalable, decentralized control framework for multi-agent ground coverage that leverages neural networks and a novel concept called Anyway Output Controllability to ensure convergence to optimal environmental coverage.

Contribution

It presents a new finite-state, decentralized policy control method combining neural network design with a decentralized MDP and introduces AOC for guaranteed convergence.

Findings

Decentralized convergence to target configuration is proven under AOC.

The framework efficiently encodes spatial configurations via neural network weights.

Agents maintain a scalable communication structure with three neighbors.

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.