一种高速重载机器人尺度-结构-驱动协同优化设计方法
By using a coupled optimization model and an internal and external dual-loop framework, the scale-structure parameters and drive selection are incorporated into a unified optimization, which solves the problem of the separation between scale-structure and drive in traditional design and realizes the global optimal design and low-cost evaluation of high-speed heavy-duty robots.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- TIANJIN UNIV
- Filing Date
- 2026-05-22
- Publication Date
- 2026-07-17
AI Technical Summary
Traditional robot design methods often separate scale-structure design from drive selection, making it difficult to achieve cycle time optimization and resulting in high-fidelity evaluation costs, which makes it difficult to obtain the globally optimal design.
A coupled optimization model is adopted, which incorporates scale-structure parameters and discrete drive selection into a unified optimization framework. By constructing a "two-stage + internal and external dual-loop" framework, multi-objective collaborative optimization is achieved. Parametric performance modeling and Gaussian process stiffness surrogate model are used for low-cost screening and drive library compression.
It achieves pre-quantification and proactive optimization of cycle time, significantly reduces the cost of high-fidelity evaluation, supports multi-objective engineering trade-offs and global optimal design, and has good scalability and industrial adaptability.
Smart Images
Figure CN122263318B_ABST