Consensus-Based Distributed Connectivity Control in Multi-Agent Systems

Abstract

This paper addresses a distributed connectivity control problem in networked multi-agent systems. The system communication topology is controlled through the algebraic connectivity measure, the second smallest eigenvalue of the communication graph Laplacian. The algebraic connectivity is estimated locally in a decentralized manner through a trustbased consensus algorithm, in which the agents communicate the perceived quality of the communication links in the system with their set of neighbors. In the presented approach, link qualities represent the weights of the communication graph from which the adjacency matrix is estimated. The Laplacian matrix and its eigenvalues, including the algebraic connectivity, are then calculated from this local estimate of the global adjacency matrix. A method for network topology control is proposed, which creates and deletes communication links based on the Albert-Barabási probabilistic model, depending on the estimated and referenced connectivity level. The proposed algebraic connectivity estimation and connectivity maintenance strategy have been validated both in simulation and on a physical robot swarm, demonstrating the method performance under varying initial topology of the communication graph, different multi-agent system sizes, in various deployment scenarios, and in the case of agent failure.

Containment control of multiple unmanned surface vessels with NN control via reconfigurable hierarchical topology

“…A topology reconfiguration algorithm was given by Griparic et al ( 2022 ), which enabled the algebraic connectivity of the system to reach the desired value through its proposed method of adding/removing links. The inputs of the topology reconfiguration algorithm are the initial local adjacency matrix of each marine craft ( means that the marine craft is a follower and l = 1, 2, 3, ..., n − m represents the index of marine craft), desired level of system performance λ 2 ref , and user-defined value K λ 2 .…”

“…Therefore, considering system safety, it is necessary to seek a reasonable way to reconfigure the system topology from the perspective of the followers' convergence positions. That is to say, through some reasonable topology reconfiguration method, these topologies that may adversely affect the system are reconfigured to achieve specific control objectives such as collision avoidance, accelerating or decelerating the system convergence rate, and maintaining the system communication continuity (Mikulski et al, 2012 ; Haus et al, 2014 ; Griparic et al, 2022 ).…”

Multi-Agent Active Perception Based on Reinforcement Learning and POMDP

“…Furthermore, consensus complexity increases linearly with the number of agents involved [38], which makes it suitable for decentralized approach presented herein. In general two factors influence convergence time in the proposed approach: (i) speed of the consensus algorithm that depends on the connectivity -higher the connectivity, shorter the time to reach consensus [41], and (ii) quality of sensors -better sensors decrease convergence time. Proposed approach assumes that the agent's communication graph is fully connected (condition for the convergence of consensus is connected graph, [42]).…”