A multi-step Genetic Algorithm to solve the inverse kinematics problem of the redundant open chain manipulators

This paper presents a new algorithm regarding the inverse kinematics problem of the redundant open-chain manipulators, based on Simple Genetic Algorithm (SGA). The proposed method could be applied for any kind of manipulator configuration independent from number of joints. This method formulates the inverse kinematics problem as an optimization algorithm, solves it using the SGA in two steps and can be extended further. The advantage of splitting the procedure can be beneficial when procedures execute in parallel. At the first step, the SGA looks for successive joint values set for a given manipulator as candidate joints set, and at the second one, SGA would find the optimum joint values. Therefore, the manipulator's end-effector would be smoothly moved from an initial location to its target with minimum joints displacement while avoiding singularity. Simulation studies show that the proposed method represents an efficient approach to solve the inverse kinematics problem of open-chain manipulators with any degree of redundancy.