Inverse kinematics solutions of a newly designed three-link robotic manipulator for the casting process using the ant lion optimizer

Conventional methods make determining inverse kinematics solutions of a multi-degree of freedom robot difficult. In recent years, soft computing methods have been used, and they are very easy to compute the solutions of inverse kinematics. In this study, the ant lion optimizer (ALO) was used to solve the inverse kinematics of a three-link robotic manipulator, and the results have been compared to other optimization algorithms such as particle swarm optimization (PSO), grey wolf optimization (GWO), and the sine-cosine algorithm (SCA). In the beginning, Denavit-Hartenberg (D-H) parameters for the robotic manipulator are constructed, as well as transformation matrices. The entire transformation matrix is then used to find the end-effector position equations. The ALO, PSO, GWO, and SCA algorithms are used to predict the end-effector location of this robotic manipulator in the workspace. The location error (difference between the real and goal positions) is estimated using a fitness function. The fitness function was used to find the inverse kinematic solutions by reducing the position error. These algorithms were put to the test in this study using two separate scenarios. Position error and solution time for a single point in the workspace were calculated in case I, while position error and solution time for twenty randomly selected locations in the workspace were estimated in case II. After computation, the ALO, PSO, GWO, and SCA give position errors of 6.557 e-06, 0.00835, 0.006881, and 0.00993 meters respectively, for case I. The solution times for the ALO, PSO, GWO, and SCA are 0.88, 14.34, 2.01, and 1.31 seconds, respectively, for case I. Similarly, better results were found for case II as compared to case I in terms of position error and solution time. By comparing case-II to case-I, case-II confirms the quality of the ALO when compared to other optimization algorithms (PSO, GWO, and SCA). In terms of position error and solution time, the ALO algorithm performs significantly better than the PSO, GWO, and SCA algorithms.