Application Of Improved Immune Clonal Selection Algorithm In Robot Trajectory Planning And Multi Objective Optimization

For manipulator trajectory optimization problem in the shortest time, the smoothest track, minimal energy consumption and other targets, the paper studies the inverse kinematics of the robot, path planning and optimization, and immune clonal selection algorithm for single-objective and multi-objective optimization problem. An improved immune clonal selection algorithm with single target was proposed to solve the inverse kinematics. Improved immune clonal selection multi-objective algorithm is subjected to performance analysis and comparison. And improved multi-objective algorithm is applied to trajectory optimization problems.Firstly, the paper describes the trajectory planning of the robot, and introduces the application of the three spline curve in the trajectory planning and optimization. Secondly, based on the standard single objective immune clonal selection algorithm, this algorithm is improved. The dynamic variation method is changed to determine the size of the change according to the individual's own situation and the iteration algebra. And the demise operation of the algorithm is also improved. Then improved multi-objective immune clonal selection algorithm, the Tent chaotic sequence is used to initialize the population and improve the diversity and uniformity of the initial population; adaptive mutation is introduced to improve the global search ability and local search ability of the algorithm; the simulation of binary crossover operator is added to enhance the exchange of information among individuals in the population; by the declining demise operation, it is guaranteed that the diversity of the population can be maintained at the early stage, and the local optimum can be avoided, and the non-feasible solution can be avoided in the final solution.3 test problems are used to simulate the algorithm, and the convergence of the improved algorithm is analyzed by the method of coverage, and the distribution of the optimal solution of the improved algorithm is analyzed by the method of spatial distribution. Simulation results show that the improved algorithm has better convergence, uniformity and wide distribution,Finally, PUMA560 is used as the simulation object. The improved algorithm is used to solve the inverse kinematics of PUMA560 robot, and the improved algorithm can effectively improve the accuracy of the solution. The improved multi-objective immune clonal selection algorithm is used to optimize the trajectory. Compared to single objective and methods will come to the result and weighted multi-objective algorithm. Simulation experiments show that improved multi-objective algorithm can be evenly distributed better Pareto optimal solution set is a more effective target trajectory optimization method.