用黑箱多目标优化自动设计含旋转与移动关节的机器人
Robot Design Optimization with Rotational and Prismatic Joints using Black-Box Multi-Objective Optimization
- 通过黑箱多目标优化同时优化关节数量和连杆长度
- 生成多样化的帕累托解，涵盖已知与新型关节组合
- 适合机器人结构设计、自动化优化领域的研究者
机器人通常由串联的旋转关节和连杆构成。然而实际应用中还广泛使用滑动关节、闭链结构和缆索驱动系统等多样化关节机制。以往研究多聚焦单一机制，通过优化连杆长度和关节布局来实现特定任务。本文提出一种结合旋转与滑动关节的机器人结构设计优化方法，利用黑箱多目标优化，自动生成在完成指定任务前提下，关节数和连杆长度均最小化的机器人结构。该方法可同时观察多种体态设计方案，所获帕累托解验证了常见且实用的关节组合，并发现了新的有效配置。
原文摘要 · Abstract (English)
Robots generally have a structure that combines rotational joints and links in a serial fashion. On the other hand, various joint mechanisms are being utilized in practice, such as prismatic joints, closed links, and wire-driven systems. Previous research have focused on individual mechanisms, proposing methods to design robots capable of achieving given tasks by optimizing the length of links and the arrangement of the joints. In this study, we propose a method for the design optimization of robots that combine different types of joints, specifically rotational and prismatic joints. The objective is to automatically generate a robot that minimizes the number of joints and link lengths while accomplishing a desired task, by utilizing a black-box multi-objective optimization approach. This enables the simultaneous observation of a diverse range of body designs through the obtained Pareto solutions. Our findings confirm the emergence of practical and known combinations of rotational and prismatic joints, as well as the discovery of novel joint combinations.
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