On Inverse Kinematics Of Super Redundant Manipulator Using An Adaptive Genetic Algorithm

The demand of on-orbit service in multi-obstacle narrow environment makes the super redundant manipulator with fine operation ability gradually become the research hotspot of space robot technology.The serial multi-joint structure makes it highly flexible and adaptable to complex environments,but it also increases the difficulty of solving kinematics and dynamics.The existing inverse kinematics solution method is difficult to solve quickly and accurately.In this paper,an inverse kinematics solution method that can meet the path planning requirements of super-redundant manipulators is proposed by improving the genetic algorithm.Firstly,the kinematics model of the manipulator is established and the shortcomings of the existing inverse kinematics methods are analyzed.Taking 9 DOF manipulator as an example,pseudo inverse method and standard genetic algorithm are used to solve its inverse kinematics.The simulation results show that the pseudo-inverse method can only solve the position solution.The fitness function values solved by the standard genetic algorithm are randomly distributed in the range of 0.6～1,which cannot be solved accurately.Secondly,the standard genetic algorithm is improved based on the local space search principle.A local space search genetic algorithm,pseudo-inverse-genetic algorithm,is proposed.The accurate position solution is obtained by solving the pseudo-inverse method,and the local search space is obtained based on the position solution expansion and solved by genetic algorithm.The inverse kinematics simulation results of 9 DOF manipulator show that the pseudo inverse genetic algorithm optimizes the solution effect,and the fitness function value can reach more than 0.95.On this basis,an adaptive search space genetic algorithm is proposed,which is divided into the solution of fixed search interval size and the solution of exponential attenuation of search interval size.The simulation results show that the fitness function values obtained by the two adaptive search space genetic algorithms with different search space optimization strategies are greater than 0.995,the maximum position error is about 10^-4 m,and the maximum attitude error is about10^-4 rad.Finally,the feasibility of the proposed adaptive search space genetic algorithm in solving the inverse kinematics of a higher degree of freedom super redundant manipulator is verified.Taking 12 and 15 degrees of freedom manipulators as examples,the standard genetic algorithm,the fixed search interval size and the exponential decay adaptive search space genetic algorithm are used to solve the inverse kinematics.The simulation results show that the standard genetic algorithm the standard genetic algorithm are poor,while the fitness function values of the two adaptive search space genetic algorithms are greater than 0.995,the maximum position error is about 10^-3 m,and the attitude error is about 10^-3 rad.The results of inverse kinematics of 9,12 and 15-DOF manipulators show that the adaptive search space genetic algorithm can efficiently and accurately solve the inverse kinematics of super-redundant manipulators.