Control of biohybrid actuators using neuroevolution

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

Abstract

In medical-related tasks, soft robots can perform better than conventional robots because of their compliant building materials and the movements they are able perform. However, designing soft robot controllers is not an easy task, due to the non-linear properties of their materials. A formal design process is needed since human expertise to design such controllers is not sufficiently effective. The present research proposes neuroevolution-based algorithms as the core mechanism to automatically generate controllers for biohybrid actuators that can be used on future medical devices, such as a catheter that will deliver drugs. The controllers generated by methodologies based on Neuroevolution of Augmenting Topologies (NEAT) and Hypercube-based NEAT (HyperNEAT) are compared against the ones generated by a standard genetic algorithm (SGA). In specific, the metrics considered are the maximum displacement in upward bending movement and the robustness to control different biohybrid actuator morphologies without redesigning the control strategy. Results indicate that the neuroevolution-based algorithms produce better-suited controllers than the SGA. In particular, NEAT designed the best controllers, achieving up to 25% higher displacement when compared with SGA-produced specialised controllers trained over a single morphology and 23% when compared with general-purpose controllers trained over a set of morphologies.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: ECTA'24 - 16th International Conference of Evolutionary Computation Theory and Applications
• Start Date: Nov 20, 2024
• End Date: Nov 22, 2024
• Acceptance Date: Jul 1, 2024
• Publication Date: Dec 6, 2024
• Deposit Date: Sep 11, 2024
• Publicly Available Date: Dec 10, 2024
• Peer Reviewed: Peer Reviewed
• Pages: 197-204
• Series ISSN: 2184-3236
• Book Title: Proceedings of the 16th International Joint Conference on Computational Intelligence - ECTA
• ISBN: 9789897587214
• DOI: https://doi.org/10.5220/0012919300003837
• Keywords: Neuroevolution; NEAT; HyperNEAT; Genetic Algorithm; Biohybrid actuator
• Public URL: https://uwe-repository.worktribe.com/output/12882295