隨著自動化工業的發展，串聯式機械手臂被廣泛應用於工業領域，其結構由多根相鄰連桿所組成，終端點的位置易受到每個連桿的累積誤差，導致定位精度下降，無法完成其任務，因此提升終端點定位精度是串聯式機械手臂相當重要的課題。絕大多數文獻皆採用雷射追蹤儀或絕對精度機械手臂作為量測裝置，測量多組機械手臂的不同姿態，並透過修正運動學模型參數，提升終端點的靜態定位精度;然而，高精度量測儀器的成本皆相當昂貴，因此本研究採用自行研發的量測裝置，量測機械手臂的運動軌跡。
本論文的研究主旨為開發一套機械手臂的軌跡補償演算法，試圖提升機械手臂的軌跡準確度，使得機械手臂的實際軌跡能更趨近預期軌跡，藉由實驗方法驗證其可行性與成效，並探討未來的改進目標，其中六軸機械手臂為鈦思科技所研發的串聯式六軸機械手臂IT-Robot。
本研究首先針對六軸機械手臂IT-Robot進行分析，推導正向運動學、逆向運動學與工作空間，並運用模擬方法對推導結果進行驗證，接著提出校正與補償演算法，採用最小平方法修正D-H模型的參數，以及訓練小波類神經網路建立軌跡誤差模型，預測終端點的真實軌跡，再透過逆向疊代法修正角度命令，以提升機械手臂的軌跡準確度，將修正過的角度命令輸入至控制器，控制IT-Robot運動，利用深度攝影機及慣性感測器，透過影像處理技術感測目標特徵點，結合EKF模型，估測機械手臂的實際軌跡，最後對實驗數據進行分析及比較，歸納出研究成果與未來可繼續研究之方向。

With the development of the automation industry, series robot arms are widely used in industry field. The structure of a series robot arm consists of multiple links, and positioning accuracy is susceptible to the cumulative errors of each link. Therefore, improving the accuracy of the end-effector is a very important issue. Most of studies use a laser tracker or an absolute measuring robot arm to measure the actual positions of a robot arm, which are used to calibrate the DH parameters to improve static position accuracy. However, high precision measurement systems are very expensive. In this thesis, a 6-DoF measurement system is established to measure the actual trajectories of a robot arm.
The main purpose of this thesis is to develope a trajectory compensation algorithm for a robot arm, to try improving the accuracy of a robot arm’s trajectory, to make an actual trajectory closer to a desired trajectory. Expeiments are conducted to verify the feasibility and effectiveness of the algorithm and to discuss future improvement goals. A series six-axis robot arm called IT-Robot is developed by Terasoft Inc .
First, this thesis analyzes the mathmatical model of the IT-Robot, derives its forward kinematics and inverse kinematics, define the effective workspace, and verify the correctness by using simulation methods. Secondly, one proposes a calibration and compensation algorithm, which uses the least square method to modify the parameters of the D-H models and trains a wavelet neural network to establish the trajectory error model. A set of angle commands is corrected by iterative-inverse method, which tries to improve the trajectory accuracy of the IT-Robot. The correct angle command is input to a controller to control the motions of the IT-Robot. A marker and an inertial measurement unit (IMU) are fixed at the one side of the gripper. The position is measured by a depth camera using image processing methods, and oriention is measured by the IMU. However, bias of the IMU and measurement noises will affect measurement accuracy. In order to improve the measurement accuracy, Extend Kalman filter is used to estimate the actual trajectory of a robot arm. Finally, one analyzes and compares the experiment results.
After the feasibilty is verified, a trajectory compensation algorithm is proposed and the future reseaches can be further studied.

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