Using neuroevolution for designing soft medical devices

TL;DR

This paper explores the use of neuroevolution algorithms to automatically design soft actuators for medical devices, demonstrating improved performance and robustness over traditional methods.

Contribution

It introduces a neuroevolution-based framework for designing soft robotic actuators, comparing multiple algorithms and identifying NEAT as the most effective.

Findings

Neuroevolution algorithms outperform traditional design methods.

NEAT produces the most effective soft actuator morphologies.

Designed actuators show robustness across different control strategies.

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. We propose 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. The actuator morphologies discovered by algorithms like Age-Fitness Pareto Optimization, 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. Analyzing the results granted the insight that neuroevolution-based algorithms produce better-performing and more robust actuators under different control methods. Moreover, the best-performing morphologies were discovered by the NEAT algorithm. As a future work aspect, we propose using the morphologies discovered here as test beds to optimize specialized controllers, enabling more effective functionality towards the desired deflections of the suggested soft catheters.