用多目标优化设计更高效、易控的仿人行走机器人
Optimal Design of a Walking Robot: Analytical, Numerical, and Machine Learning Methods for Multicriteria Synthesis
- 结合解析、数值与遗传算法，实现多目标腿结构优化
- 首次引入各向同性准则优化，提升全向力与运动传递效率
- 实测验证了地形适应与激光导航能力，具实用价值
本文针对行走机器人的设计关键环节展开研究，包括最优结构综合、提出一种新型'合理'机械结构以提升效率并简化控制，同时克服现有设计中的实际限制。研究开发了新型多目标综合方法，实现最优腿部设计，融合解析与数值方法。此外，提出基于非支配排序遗传算法II（NSGA-II）的优化方案。研究考察了转向模式，并首次将通常用于并联机器人中的各向同性准则应用于行走机器人参数优化，以确保各方向上力与运动传递的最佳性能。研制了多个物理原型，实验验证了不同机构的功能，包括适应表面不平度及利用激光雷达进行导航的能力。
原文摘要 · Abstract (English)
This paper addresses several critical stages of designing a walking robot, including optimal structural synthesis, introducing a novel 'rational' mechanical structure aimed at enhancing efficiency and simplifying control system, while addressing practical limitations observed in existing designs. The study includes development of novel multicriteria synthesis methods for achieving optimal leg design, integrating analytical and numerical methods. In addition, a method based on Non-dominated Sorting Genetic Algorithm II is presented. Turning modes are investigated, and for the first time, the isotropy criterion, typically applied to parallel manipulators, is used for optimizing walking robot parameters to ensure optimal force and motion transfer in all directions. Several physical prototypes are developed to experimentally validate the functionality of different mechanisms of the robot, including adaptation to the surface irregularities and navigation using LiDAR.
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