應用PSO最佳化演算法鑑別機械手臂之轉動慣量與避障路徑規劃

• Main Authors: Zhi-Shan Zheng, 鄭志善
• Other Authors: Chih-Ying Yang
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/vad5f6

碩士 === 國立中正大學 === 機械工程系研究所 === 106 === This study is devoted into the obstacle avoidance path planning of the six-axis robot manipulators, and the position analysis of the robot manipulator is establishes based on the definition of coordinate system of each axis. The position and orientation of each link is modeled as the line equation, and the obstacle is represented as the equation of the surface. The relationship between the line and the surface is used to collision detection. Then, the obstacle avoidance point is set to avoid the obstacle, and the obstacle avoidance trajectory can be re-planned accordance with the obstacle avoidance point. The collision detection is re-estimated. If the re-planning trajectory has no collision occurrence, the obstacle avoidance trajectory planning is completed. However, the inaccuracy of the structure parameters causes the position/posture error while the robot manipulators under loading condition. The position/posture error will cause the collision detection fail. So that, it is necessary that identifying the structure parameters to increase the precision of the position analysis. This study proposes a particle swarm optimization algorithm to identify the structure parameters of a six-axis robotic manipulator. The particle swarm optimization algorithm sets fewer parameters and can quickly converge to converge the optimal solution. Using this algorithm combined with the Newton-Euler equation, the particle swarm optimization method sets the moment of inertia as the individual particle group and substitutes the dynamic equation of Newton's Euler to calculate the torque value of each axis. Then, the torque value of each axis is compared with the measured torque value. If the torque value does not meet the measured torque value, the new particle group will be set again in the solution space random range, and then substituted into the dynamic equation. The comparison, such as the measured torque value, will be the best solution for the individual particle group, and then the optimal solution of the particle group is obtained by the iterative convergence of each particle group individual, which is the mechanical arm identified in this study. The best solution of moment of inertia and centroid.