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JRM Vol.35 No.4 pp. 1007-1015
doi: 10.20965/jrm.2023.p1007
(2023)

Paper:

Evolutionary Design of Cooperative Transport Behavior for a Heterogeneous Robotic Swarm

Razzaq Asad* ORCID Icon, Tomohiro Hayakawa** ORCID Icon, and Toshiyuki Yasuda** ORCID Icon

*Graduate School of Science and Engineering, University of Toyama
3190 Gofuku, Toyama 930-8555, Japan

**Faculty of Engineering, University of Toyama
3190 Gofuku, Toyama 930-8555, Japan

Received:
March 6, 2023
Accepted:
June 19, 2023
Published:
August 20, 2023
Keywords:
swarm robotics, evolutionary robotics, artificial neural networks, heterogeneous, cooperative transport
Abstract

Swarm robotics system (SRS) is a type of artifact that employs multiple robots to work together in a coordinated way, inspired by the self-organizing behavior of social insects such as ants and bees. SRSs are known for their robustness, flexibility, and scalability. This study focuses on evolutionary robotics (ER) which uses artificial neural networks (ANNs) as controllers to operate autonomous robots. In traditional ER research, SRSs were often composed of teams of homogeneous robots, each of which is controlled by a single ANN. In contrast, this study focuses on the implementation of ER in a heterogeneous SRS. To evaluate our approach, we present the concept of employing multiple controllers for sub-teams in a swarm. Heterogeneity was achieved using different controllers for the same physical bodies. We simulated a cooperative transport task, in which the performance of heterogeneity was superior because the two ANN controllers were able to express a variety of behaviors as an entire swarm. Additionally, this study investigated how well the three types of parental selection methods of the heterogeneous approach, can help to optimize the performance of the swarm.

Small differences improve swarm performance

Small differences improve swarm performance

Cite this article as:
R. Asad, T. Hayakawa, and T. Yasuda, “Evolutionary Design of Cooperative Transport Behavior for a Heterogeneous Robotic Swarm,” J. Robot. Mechatron., Vol.35 No.4, pp. 1007-1015, 2023.
Data files:
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