Improvement of Artificial Bee Colony Algorithm for Inverse Kinematics of Redundant Manipulators
The inverse kinematics of redundant manipulators is one of the most important and complicated problems in robotics,taking the minimum pose error of the end-effector as the optimization objective,it can be transformed into an equivalent optimization problem,which can be solved by intelligent optimization algorithms.To effectively solve the inverse kinematics problem of redundant manipulators,an improved artificial bee colony algorithm is proposed.In improved algorithm,search equations for the employed bee phase,the onlooker bee phase and the scout bee phase are proposed respectively,so as to form a multi-strategy hybrid co-evolution effect in the evolution process,which make the proposed algorithm more capable of balancing global exploration and local exploitation.Thus,the shortcomings of the basic artificial bee colony algorithm,such as slow convergence rate and low computational accuracy caused by using the same one-dimensional search equation in the employed bee phase and the onlooker bee phase,can be effectively overcome.The inverse kinematic of a 7-DOF redundant manipulator is taken as an example to carry out comparative experiments,the experimental results indicate that compared with the comparison algorithms,the improved algorithm has higher convergence accuracy,faster convergence speed and stronger optimization stability,and it can be used to solved the inverse kinematics problem of redundant manipulators effectively.
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