Optimizing the substrate for hypercube-based neuroevolution of augmented topologies to design soft actuators

Alcaraz-Herrera, Hugo; Tsompanas, Michail-Antisthenis; Balaz, Igor; Adamatzky, Andrew

Abstract

The characteristics of soft robots make them better candidates for applications such as healthcare, due to their enhanced safety, adaptability and more natural human-robot interaction compared to traditional counterparts. Different actuating systems have been proposed for soft robotics. On the other hand, since this technology is fairly young, the designing process of soft actuators is not yet well formalized. In an attempt to enhance the applicability of this type of actuators, the utilization of a NeuroEvolution algorithm to automatically design them is proposed here. More specifically Hypercube-based NeuroEvolution of Augmented Topologies (HyperNEAT) is investigated for different substrate architectures. These substrates are Artificial Neural Networks that encode the three dimensional representation of the soft actuators. The produced three dimensional sketches are tested within a simulated environment under two different targets (the maximum displacement and the combination of maximum displacement and minimum actuator volume) to identify the suitability of Hy-perNEAT as an efficient designing methodology. Since the evaluation of candidate solutions under a physics simulator is the most computationally demanding process, the proposed methodology was realized under a client-server setting, with the outlook of accelerating the evolutionary optimization of actuator sketches. The evaluation part of the algorithm was outsourced to the server side that can be a specialized and high-performing computational entity. The resulting soft actuators of this study proved to be of higher competence when compared with actuators derived under previously published evolutionary methodologies.

• Journal Article Type: Article
• Acceptance Date: Jul 7, 2025
• Deposit Date: Jul 11, 2025
• Print ISSN: 1532-0626
• Electronic ISSN: 1532-0634
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1002/cpe.70204
• Public URL: https://uwe-repository.worktribe.com/output/14695240