An Optimization Approach for the Inverse Kinematics of a Highly Redundant Robot

Abstract

Abstract. This paper describes a robot with 12 degrees of freedom for pick-and-place operations using bricks. In addition, an optimization approach is proposed, which determines the state of each joint (that establishes the pose for the robot) based on the target position while minimizing the effort of the servomotors avoiding the inverse kinematics problem, which is a hard task for a 12 DOF robot manipulator. Therefore, it is a multi-objective optimization problem that will be solved using two optimization methods: the Stretched Simulated Annealing method and the NSGA II method. The experiments conducted in a simulation environment prove that the proposed approach is able to determine a solution for the inverse kinematics problem. A real robot formed by several servomotors and a gripper is also presented in this research for validating the solutions.

A Constrained Intelligent Nonlinear Control Method for Redundant Robotic Manipulators

“…Forward kinematics of a general n-dof robotic manipulator could be obtained using homogenous-transformation computation [5], [6], [7]:…”

“…Implementation time of the biomimetic algorithm [3] was impressively fast while the universal ability was exhibited by the neural approach [4]. The inverse-kinematics problem could be also well treated by various optimization methods such as the stretched simulated annealing (SSA) algorithm [5], and Jacobian pseudo inverse method [6], or the damped least-squares (DLS) law [7]. However, physical constraints are open issues of these advanced controllers.…”

Industrial robot trajectory optimization- a review

“…There are several studies that seek the best algorithms for the motion control of the joints of the snake robots, as M. Jafari and A. Shahmansoorian [56] show. In one such study, P. Costa, J. Lima, A. I. Pereira, P. Costa, A. Pinto [57]" propose a multi-criteria optimization for a robot with 12 degrees of freedom, Tentacle Robot, by minimizing the Euclidean distance between the target positions and by minimizing the effort of the servomotors.…”