An evolutionary approach for solving the multimodal inverse kinematics problem of industrial robots

The inverse kinematics solution of an industrial robot may provide multiple robot configurations that all achieve the required goal position of the manipulator. In the absence of obstacles, multiplicity resolution can be achieved by selecting the robot configuration closest to the current robot configuration in the joint space. An evolutionary approach based on a real-coded genetic algorithm is used to obtain the solution of the multimodal inverse kinematics problem of industrial robots. All the multiple configurations obtained by this approach can be displayed using a 3D modeler developed in MATLAB for the purpose of visualization. The multiple configurations are then compared on the basis of their closeness in joint space to the current robot configuration. Simulation experiments are carried out on a SCARA robot and a PUMA robot to illustrate the efficacy of the approach.