Version 1
: Received: 27 August 2024 / Approved: 27 August 2024 / Online: 28 August 2024 (09:59:47 CEST)
How to cite:
Rasouli, S.; Dautenhahn, K.; L. Nehaniv, C. Simulation of a Bio-Inspired Flocking-Based Aggregation Behaviour in Swarm Robotics. Preprints2024, 2024082007. https://doi.org/10.20944/preprints202408.2007.v1
Rasouli, S.; Dautenhahn, K.; L. Nehaniv, C. Simulation of a Bio-Inspired Flocking-Based Aggregation Behaviour in Swarm Robotics. Preprints 2024, 2024082007. https://doi.org/10.20944/preprints202408.2007.v1
Rasouli, S.; Dautenhahn, K.; L. Nehaniv, C. Simulation of a Bio-Inspired Flocking-Based Aggregation Behaviour in Swarm Robotics. Preprints2024, 2024082007. https://doi.org/10.20944/preprints202408.2007.v1
APA Style
Rasouli, S., Dautenhahn, K., & L. Nehaniv, C. (2024). Simulation of a Bio-Inspired Flocking-Based Aggregation Behaviour in Swarm Robotics. Preprints. https://doi.org/10.20944/preprints202408.2007.v1
Chicago/Turabian Style
Rasouli, S., Kerstin Dautenhahn and Chrystopher L. Nehaniv. 2024 "Simulation of a Bio-Inspired Flocking-Based Aggregation Behaviour in Swarm Robotics" Preprints. https://doi.org/10.20944/preprints202408.2007.v1
Abstract
This paper presents a biologically inspired flocking-based aggregation behaviour of a swarm of mobile robots. Aggregation behaviour is essential to many swarm systems, such as swarm robotics systems, in order to accomplish complex tasks that are impossible for a single agent. We developed a robot controller using Reynolds’ flocking rules to coordinate the movements of multiple e-puck robots during the aggregation process. To improve aggregation behaviour among these robots and address the scalability issues in current flocking-based aggregation approaches, we used a K-means algorithm to identify clusters of agents. Using the developed controller, we simulated the aggregation behaviour among the swarm of robots. Five experiments were conducted using Webots simulation software. The performance of the developed system was evaluated under a variety of environments and conditions, such as various obstacles, agent failure, different numbers of robots and arena sizes. The results of the experiments demonstrated that the proposed algorithm is robust and scalable. Moreover, we compared our proposed algorithm with another implementation of the flocking-based self-organizing aggregation behaviour based on Reynolds’ rules in a swarm of e-puck robots. Our algorithms outperformed this method in terms of cohesion performance and aggregation completion time.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.