Using neuroevolution for designing soft medical devices

Abstract

Soft robots can exhibit better performance in specific tasks compared to conventional robots, particularly in healthcare related tasks. However, the field of soft robotics is still young, and designing them often involves mimicking natural organisms or relying heavily on human experts’ creativity. A formal automated design process is required. The use of neuroevolution-based algorithms to automatically design initial sketches of soft actuators that can enable the movement of future medical devices, such as drug-delivering catheters, is proposed. The actuator morphologies discovered by algorithms like Age-Fitness Pareto Optimisation, NeuroEvolution of Augmenting Topologies (NEAT), and Hypercube-based NEAT (HyperNEAT) were compared based on the maximum displacement reached and their robustness against various control methods. Analysing the results granted the insight that neuroevolution-based algorithms produce better-performing and more robust actuators under diverse control methods. Specifically, the best-performing morphologies were discovered by the NEAT algorithm.

Keywords

NEAT / HyperNEAT / AFPO / Soft robot / Actuator / Catheter

1 CRediT authorship contribution statement

Hugo Alcaraz-Herrera: Writing - review & editing, Writing - original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Data curation. Michail-Antisthenis Tsompanas: Writing - review & editing, Methodology, Funding acquisition, Conceptualization. Igor Balaz: Writing - review & editing, Project administration, Funding acquisition, Conceptualization. Andrew Adamatzky: Writing - review & editing, Supervision, Funding acquisition, Conceptualization.

2 Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

3 Acknowledgements

This project has received funding from the European Union’s Horizon Europe research and innovation programme (101070328). UWE researchers were funded by the UK Research and Innovation (10044516).

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