Intelligent Trajectory Planning Of A Robot Manipulator With Six Degrees Of Freedom

Industrial manipulator is now widely employed in manufacturing processes, for example, welding, painting and assembly, with high precision and endurance. It plays a vital role not only in the scientific and technological innovation, but also in the economic and social progress. Trajectory planning is one of the key research topics as a manipulator may exhibit various degrees of autonomy. Furthermore, artificial intelligence is becoming an increasingly important part of the modern industrial robot. In the past, several intelligent optimization algorithms are used to find the optimal trajectory of a manipulator, but their advantages and disadvantages still remain unknown.Therefore, the purpose of the thesis is to compare three different intelligent optimization algorithms, namely ant colony, simulated annealing and tabu search, in the process of optimal trajectory planning for an industrial manipulator with six degree of freedom (6-DOF).The main contributions of the thesis can be described as follows. Firstly, a forward kinematics model is set up by using D-H method. An inverse kinematics model is then deduced based on the forward model, which provides the theoretic background for movement control of the manipulator. Secondly, the three above-mentioned optimization algorithms are introduced and their results of optimal trajectory planning are analyzed and compared in terms of robustness, accuracy and probability of finding the shortest path. Finally, the6-DOF manipulator is used as an automatic spot welding machine under different algorithms to find the optimal movement path of the robot hand. Experimental results show that the ant colony algorithm has the best accuracy and robustness, while simulated annealing algorithm has the highest probability of finding the shortest path.