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;

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Title:	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
Authors:	鄭志善;Zheng, Zhi-Shan
Contributors:	機械工程系研究所
Keywords:	六軸機械手臂;轉動慣量;粒子群最佳化演算法;避障路徑規劃;牛頓尤拉動態方程式;six-axis robotic manipulator;moment of inertia;particle swarm optimization algorithm;obstacle avoidance trajectory planning;Newton-Euler equation
Date:	2018
Issue Date:	2019-05-23 12:52:47 (UTC+8)
Publisher:	機械工程系研究所
Abstract:	本研究針對六軸機械手臂進行避障路徑規劃，首先建立機械手臂之各軸的座標系統並進行位置分析，由此座標系統推算各軸的轉換矩陣，以得知各軸末端點位置及方位，而畫出機械手臂各軸末端點的軌跡，且根據各軸末端點位置建立線方程式以標示機械手臂桿件，並以面的的方程式來表示障礙物，最後以線與面的關係式進行碰撞偵測，如有碰撞會由碰撞點的位置進行偏移至障礙物外，同時設置避障點以進行避障的路徑規劃，並重新判斷是否有碰撞，如沒有即完成本研究避障路徑規劃。然而，當機械手臂因自重或負載將造成軌跡估測不精確，將使得碰撞偵測結果不正確，因此本研究需再針對機械手臂之實體參數進行鑑別，包含質心位置與慣量等與結構剛性相關之參數，以達成精準之位置分析。本研究提出應用粒子群最佳化演算法進行六軸機械手臂之參數鑑別，透過粒子群最佳化演算法設定較少的參數，並可快速疊代收斂出最佳解，運用此演算法結合機械手臂的動態方程式(Newton-Euler equation)，粒子群最佳化法會將轉動慣量設為粒子群個體，並代入牛頓尤拉的動態方程式計算出各軸的扭力值，接著與實驗量測所得之各軸扭力值進行比對，如不符合量測的扭力值，將會重新在空間範圍內亂數設定新的粒子群個體，再代入動態方程式進行比對，如符合量測的扭力值，將為粒子群個體最佳解，之後透過每個粒子群個體的疊代收斂得到粒子群體的最佳解，即是本研究所鑑別所得的機械手臂的參數最佳解。 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.
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