Studies on Optimal Path Planning and Self-Collision Avoidance of 7-DOF Manipulators

• Main Authors: Tsai, Ho-Hsuan, 蔡和軒
• Other Authors: Hsiao, Te-Sheng
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/87023595970063708387

碩士 === 國立交通大學 === 電控工程研究所 === 104 === In industry, six-degree-of-freedom (DOF) manipulators are most widely used, because the 3D position and orientation of the end-effector can be completely determined. However, if there exist obstacles in the work space, there may be no solutions for 6-DOF manipulators to avoid the obstacles. Seven-DOF manipulators can avoid the obstacle by exploiting the redundant DOF; therefore, they are worthy of further researches. The seven-DOF manipulator in this thesis has the similar structure to an anthropomorphic manipulator. The difference is that the manipulator in this thesis has an offset between the wrist and the elbow. To describe the relationship between the position of the end-effector of the manipulator and the angle of each joint, we built the manipulator's model by the D-H rules, and derived the kinematic model, and the dynamic model which includes motor models. The motors' torque is limited by the hardware. In such situation, trajectory commands are not always fulfilled by the manipulator because the motors cannot afford the torques required to follow the desired trajectory. In addition, it is an important issue in industry to reduce the moving time of manipulators, because moving faster means working more efficiently. Manipulators' self-collision-avoidance must be fulfilled by all the path planning methods. Once self-collision happens, manipulators will be damaged. In this thesis, we formulated two problems mentioned above as optimal control problems with the constraints of manipulators' self-collision-avoidance and maximum-motor-torque, and solved the path by using GPOPS software. However, dynamic equations of seven-DOF manipulators are very complicated, such that it spent a long time for GPOPS to solve the problem. To find a more efficient solver, we use dynamic programming, and compared the result with the solution derived by GPOPS software by means of simulations. Then we verify correctness and efficiency of both solutions.