Simulated annealing quantum genetic algorithm for welding robot trajectory planning
For solving the trajectory planning problem that often occurs in the welding process of welding robots,a six-degree-of-freedom robotic arm PUMA 560 was taken as the research object,Cartesian space was used to plan the trajectory,and a hybrid al-gorithm was used to optimize the inertia moment of each joint at different end positions with the minimum amount of joint inertia moment variation as the optimization objective,thus the problems of unstable operation and unstable motion of the robot arm dur-ing welding were eliminated.The problems of slow convergence of the simulated annealing algorithm and poor local optimization capability of the quantum genetic algorithm were overcome,and the optimal trajectory of the joint inertia moments of the robot arm was successfully planed.MATLAB simulation results show that,the convergence time of the simulated annealing quantum genetic algorithm is reduced by 30.56％compared with the traditional genetic algorithm,and the inertia moment of the joint is optimized,which verifies the feasibility of the algorithm and lays the foundation for the subsequent research.
robotic arm trajectory planningsimulated annealing algorithmquantum genetic algorithminertia moment
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维普
