基于多约束条件下的机器人尺寸综合优化  

Dimension optimization of robots based on multi-constraints

作　　者：肖宇光 高峰[1] XIAO Yu-guang;GAO Feng(School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai 200240)

机构地区：[1]上海交通大学机械与动力工程学院,上海200240

出　　处：《机械设计》2021年第4期1-8,共8页Journal of Machine Design

摘　　要：为实现机器人在多约束条件下的尺寸优化,提出一种基于多约束条件下的机器人尺寸优化方法。首先建立6足机器人的简化数学模型,并给出该机器人需要综合优化的设计尺寸;然后分析了该机器人存在的多种约束条件,并进行约束条件的综合。通过对该机器人的运动学求解和力雅克比矩阵的推导,得到机器人的关节转矩表达式;以关节转矩为目标函数,多约束条件为限定,应用遗传算法进行尺寸综合优化,得到设计尺寸的最优值。对比初选值和优化值的转矩曲线,结果表明:优化后的设计尺寸使得机器人的关节转矩变小。In this article,a method for dimension optimization of robots is proposed based on multi-constraints. Firstly,through the simplified mathematical model of the hexapod robot,a series of design variables for optimization are identified. Secondly,the analysis is conducted on the multi-constraints of the hexapod robot,and then the constraints are synthesized. By means of deriving the kinematics solution and the force Jacobian matrix of the robot,the expression joint torques is worked out. With the joint torques as the objective function and the multi-constraints as the limits,the genetic algorithm is used to carry out dimension optimization,in order to obtain the optimal value of dimension. The comparison between the torque curves of the primary values and those of the optimized values shows that thanks to the optimized dimension,the robot’s joint torques has reduced.

关 键 词：尺寸综合优化 多约束条件 雅克比矩阵 遗传算法 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]