Implication of MFO for Control of 3-link Robotic Manipulator Used for Casting Process

The inverse kinematics of the 3-link robotic manipulator was solved utilizing the moth-flame optimization (MFO) algorithm in this study. The outcomes were compared to those obtained using other optimization methods, including the grey wolf optimization (GWO) algorithm, particle swarm optimization (PSO) algorithm and whale optimization algorithm (WOA). First, the transformation matrices and D-H values of the robotic arm are generated. The end-effector position equations are then developed using the general transformation matrix. Using the MFO, PSO, GWO, and WOA, this robotic manipulator's end-effector position in the working area is estimated. A fitness function is used to estimate the position error or the distance between the current position and the desired place. By utilizing the fitness function to minimize the position error, the inverse kinematics solutions were produced. These algorithms were examined in this study, two distinct examples were used. Error in position and time to solve were estimated in Case-I for one place in the workspace, whereas Case-II estimated error in position and time to solve for 20 arbitrarily chosen areas of the workspace. By comparing it to case-I, case-II demonstrates the superiority of the MFO method over additional optimization techniques (PSO, GWO, and WOA). The MFO algorithm performs significantly better than PSO, GWO, and WOA algorithms with regard to errors in position error and time to solve, according to the results.