Using neuroevolution for designing soft medical devices
<|reference_start|>Using neuroevolution for designing soft medical devices: 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.<|reference_end|>
@article{alcaraz-herrera2024using,
    title={Using neuroevolution for designing soft medical devices},
    author={Hugo Alcaraz-Herrera, Michail-Antisthenis Tsompanas, Andrew Adamatzky,
  Igor Balaz},
    journal={arXiv preprint arXiv:2408.09107},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09107},
    primaryClass={cs.RO}
}