Consensus of Multi-Agent Systems Using Back-Tracking and History Following Algorithms

TL;DR

This paper introduces back-tracking and history following algorithms for multi-agent consensus under communication loss, demonstrating their effectiveness through simulations and hardware tests, with history following generally achieving faster consensus.

Contribution

The paper presents novel algorithms for consensus in multi-agent systems that handle communication loss using back-tracking and history-based strategies, including obstacle avoidance and hardware validation.

Findings

History following reduces average consensus time compared to back-tracking.

History following can sometimes lead to false consensus, causing agents to stop prematurely.

Algorithms are validated through simulations and hardware implementation.

Abstract

This paper proposes two algorithms, namely "back-tracking" and "history following", to reach consensus in case of communication loss for a network of distributed agents with switching topologies. To reach consensus in distributed control, considered communication topology forms a strongly connected graph. The graph is no more strongly connected whenever an agent loses communication.Whenever an agent loses communication, the topology is no more strongly connected. The proposed back-tracking algorithm makes sure that the agent backtracks its position unless the communication is reestablished, and path is changed to reach consensus. In history following, the agents use their memory and move towards previous consensus point until the communication is regained. Upon regaining communication, a new consensus point is calculated depending on the current positions of the agents and they change their trajectories accordingly. Simulation results, for a network of six agents, show that when the agents follow the previous history, the average consensus time is less than that of back-tracking. However, situation may arise in history following where a false notion of reaching consensus makes one of the agents stop at a point near to the actual consensus point. An obstacle avoidance algorithm is integrated with the proposed algorithms to avoid collisions. Hardware implementation for a three robots system shows the effectiveness of the algorithms.