优化可变形机器人设计，使其在特定空间内可达且所需驱动力最小。
Optimal Design of Continuum Robots with Reachability Constraints
- 结合正逆运动学与扭矩最小化，高效分析可达性。
- 用分布估计算法比遗传算法少4-15%的驱动扭矩或总长度。
- 适合需要高精度、低能耗机器人设计的研究者。
多关节可变形机器人虽灵活易控，但因涉及曲率运动学，其最优设计仍具挑战。本文提出一种计算方法，在给定可达性约束下求解最优设计。首先利用正逆运动学高效准确地进行可达性分析；逆运动学中同时引入关节扭矩最小化，以找到达到目标工作空间所需最小驱动扭矩的配置。最后采用分布估计算法（EDA）优化机器人尺寸，目标函数为总长度或操作所需扭矩。通过三个应用案例验证，相同迭代次数下，EDA所获最优解的指标值比遗传算法（GA）低4%-15%，表现更优。
原文摘要 · Abstract (English)
While multi-joint continuum robots are highly dexterous and flexible, designing an optimal robot can be challenging due to its kinematics involving curvatures. Hence, the current work presents a computational method developed to find optimal designs of continuum robots given reachability constraints. First, we leverage both forward and inverse kinematic computations to perform reachability analysis in an efficient yet accurate manner. While implementing inverse kinematics, we also integrate torque minimization at joints such that robot configurations with the minimum actuator torque required to reach a given workspace could be found. Lastly, we apply an estimation of distribution algorithm (EDA) to find optimal robot dimensions while considering reachability, where the objective function could be the total length of the robot or the actuator torque required to operate the robot. Through three application problems, we show that the EDA is superior to a genetic algorithm (GA) in finding better solutions within a given number of iterations, as the objective values of the best solutions found by the EDA are 4-15\% lower than those found by the GA.
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