用神经演化算法自动设计软体生物机器人控制器，提升医疗设备精准度。
Control of Biohybrid Actuators using NeuroEvolution
- 采用NEAT和HyperNEAT等神经演化算法生成控制策略。
- 相比传统遗传算法，最大上弯位移提升25%，鲁棒性提高23%。
- 适合开发可适配多种形态的智能医疗软体机器人。
在医疗任务中，软体机器人因材料柔性及运动能力优于传统机器人。但其材料非线性特性使控制器设计困难，人工设计效率有限，需建立正式设计流程。本文提出基于神经演化算法的自动控制器生成方法，用于未来医疗设备（如药物输送导管）中的生物混合驱动器。对比标准遗传算法（SGA）、NEAT与HyperNEAT算法生成的控制器，评估指标包括向上弯曲的最大位移及对不同生物混合驱动器形态的鲁棒性。结果表明，神经演化算法表现更优：NEAT生成的控制器在单一形态训练下比SGA专用控制器位移高25%，在多形态通用训练下比通用控制器高23%。
原文摘要 · Abstract (English)
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. Since human expertise to design such controllers is yet not sufficiently effective, a formal design process is needed. 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.
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