Xiang Wenping, Chen Junhua, Li Hao, Chai Zhiyuan, Lou Yinghou
College of Science and Technology, Ningbo University, Ningbo 315300, China.
Faculty of Mechanical Engineering and Mechanics, Ningbo University, Ningbo 315211, China.
Sensors (Basel). 2025 Jan 10;25(2):378. doi: 10.3390/s25020378.
Industrial robotic arms are often subject to significant end-effector pose deviations from the target position due to the combined effects of nonlinear deformations such as link flexibility, joint compliance, and end-effector load. To address this issue, a study was conducted on the analysis and compensation of end-position errors in a six-degree-of-freedom robotic arm. The kinematic model of the robotic arm was established using the Denavit-Hartenberg (DH) parameter method, and a rigid-flexible coupled virtual prototype model was developed using ANSYS and ADAMS. Kinematic simulations were performed on the virtual prototype to analyze the variation in end-effector position errors under rigid-flexible coupling conditions. To achieve error compensation, an approach based on an Enhanced Crayfish Optimization Algorithm (ECOA) optimizing a BP neural network was proposed to compensate for position errors. An experimental platform was constructed for error measurement and validation. The experimental results demonstrated that the positioning accuracy after compensation improves by 75.77%, fully validating the effectiveness and reliability of the proposed method for compensating flexible errors.
由于连杆柔性、关节柔顺性和末端执行器负载等非线性变形的综合影响，工业机器人手臂的末端执行器姿态往往会与目标位置产生显著偏差。为了解决这个问题，针对六自由度机器人手臂的末端位置误差分析与补偿展开了一项研究。采用Denavit-Hartenberg（DH）参数法建立了机器人手臂的运动学模型，并利用ANSYS和ADAMS开发了刚柔耦合虚拟样机模型。在虚拟样机上进行运动学仿真，以分析刚柔耦合条件下末端执行器位置误差的变化情况。为实现误差补偿，提出了一种基于增强型小龙虾优化算法（ECOA）优化BP神经网络的方法来补偿位置误差。搭建了用于误差测量与验证的实验平台。实验结果表明，补偿后的定位精度提高了75.77%，充分验证了所提柔性误差补偿方法的有效性和可靠性。
