Using neuroevolution for designing soft medical devices
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913739647746048 |
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| author | Alcaraz-Herrera, Hugo Tsompanas, Michail-Antisthenis Adamatzky, Andrew Balaz, Igor |
| author_facet | Alcaraz-Herrera, Hugo Tsompanas, Michail-Antisthenis Adamatzky, Andrew Balaz, Igor |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_09107 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Using neuroevolution for designing soft medical devices Alcaraz-Herrera, Hugo Tsompanas, Michail-Antisthenis Adamatzky, Andrew Balaz, Igor Robotics 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. |
| title | Using neuroevolution for designing soft medical devices |
| topic | Robotics |
| url | https://arxiv.org/abs/2408.09107 |