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Abstract-Inverse kinematics is a nonlinear problem that may have multiple solutions. A Genetic Algorithm(GA) for solving the inverse kinematics of a serial robotic manipulator is presented. The algorithm is capable of finding multiple solutions of the inverse kinematics through niching methods.Despite the fact that the number and position of solutions in the search space depends on the the position and orientation of the end-effector as well as the configuration of the robot, the number of GA parameters that must be set by a user are limited to a minimum through the use of an adaptive niching method. The only requirement of the algorithm is the forward kinematics equations which can be easily obtained from the link parameters and joint variables of the robot. For identifying and processing the outputs of this GA, a modified filtering and clustering phase is also added to the algorithm. The algorithm was tested to solve the inverse kinematic problem of a 3 degreeof-freedom(DOF) robotic manipulator.

SUMMARYInverse kinematics (IK) is a nonlinear problem that may have multiple solutions. A modified genetic algorithm (GA) for solving the IK of a serial robotic manipulator is presented. The algorithm is capable of finding multiple solutions of the IK through niching methods. Despite the fact that the number and position of solutions in the search space depends on the position and orientation of the end-effector as well as the kinematic configuration (KC) of the robot, the number of GA parameters that must be set by a user are limited to a minimum through the use of an adaptive niching method. The only requirement of the algorithm is the forward kinematics (FK) equations which can be easily obtained from the Denavit–Hartenberg link parameters and joint variables of the robot. For identifying and processing the outputs of the proposed GA, a modified filtering and clustering phase is also added to the algorithm. For the postprocessing stage, a numerical IK solver is used to achieve convergence to the desired accuracy. The algorithm is validated on three KCs of a modular and reconfigurable robot (MRR).

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