The inverse kinematics of redundant manipulators is one of the most important and complicated problems in robotics. Simultaneously,\nit is also the basis for motion control, trajectory planning, and dynamics analysis of redundant manipulators. Taking the\nminimum pose error of the end-effector as the optimization objective, a fitness function was constructed. Thus, the inverse kinematics\nproblem of the redundant manipulator can be transformed into an equivalent optimization problem, and it can be solved\nusing a swarm intelligence optimization algorithm. Therefore, an improved fruit fly optimization algorithm, namely, the hybrid\nmutation fruit fly optimization algorithm (HMFOA), was presented in this work for solving the inverse kinematics of a redundant\nrobot manipulator. An olfactory search based on multiple mutation strategies and a visual search based on the dynamic real-time\nupdates were adopted in HMFOA. The former has a good balance between exploration and exploitation, which can effectively solve\nthe premature convergence problem of the fruit fly optimization algorithm (FOA). The latter makes full use of the successful search\nexperience of each fruit fly and can improve the convergence speed of the algorithm.....................
Loading....