NeuroEvolution algorithms applied in the designing process of biohybrid actuators

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

Abstract

Soft robots diverge from traditional rigid robotics, offering unique advantages in adaptability, safety, and human-robot interaction. In some cases, soft robots can be powered by biohybrid actuators and the design process of these systems is far from straightforward. We analyse here two algorithms that may assist the design of these systems , namely, NEAT (NeuroEvolution of Augmented Topologies) and HyperNEAT (Hypercube-based NeuroEvolution of Augmented Topolo-gies). These algorithms exploit the evolution of the structure of actu-ators encoded through neural networks. To evaluate these algorithms, we compare them with a similar approach using the Age Fitness Pareto Optimisation (AFPO) algorithm, with a focus on assessing the maximum displacement achieved by the discovered biohybrid morphologies. Additionally, we investigate the effects of optimisation against both the volume of these morphologies and the distance they can cover. To further accelerate the computational process, the proposed methodology is implemented in a client-server setting; so, the most demanding calculations can be executed on specialised and efficient hardware. The results indicate that the HyperNEAT-based approach excels in identifying morphologies with minimal volumes that still achieve satisfactory displacement targets.

• Presentation Conference Type: Conference Paper (unpublished)
• Conference Name: IDC’24 – The 17th International Symposium on Intelligent Distributed Computing
• Start Date: Sep 18, 2024
• End Date: Sep 20, 2024
• Acceptance Date: Aug 26, 2024
• Deposit Date: Sep 11, 2024
• Peer Reviewed: Peer Reviewed
• Public URL: https://uwe-repository.worktribe.com/output/12882285