Intelligent Trajectory Tracking Behavior of a Multi-Joint Robotic Arm via Genetic–Swarm Optimization for the Inverse Kinematic Solution

Abstract

It is necessary to control the movement of a complex multi-joint structure such as a robotic arm in order to reach a target position accurately in various applications. In this paper, a hybrid optimal Genetic–Swarm solution for the Inverse Kinematic (IK) solution of a robotic arm is presented. Each joint is controlled by Proportional–Integral–Derivative (PID) controller optimized with the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), called Genetic–Swarm Optimization (GSO). GSO solves the IK of each joint while the dynamic model is determined by the Lagrangian. The tuning of the PID is defined as an optimization problem and is solved by PSO for the simulated model in a virtual environment. A Graphical User Interface has been developed as a front-end application. Based on the combination of hybrid optimal GSO and PID control, it is ascertained that the system works efficiently. Finally, we compare the hybrid optimal GSO with conventional optimization methods by statistic analysis.

Inverse Kinematics of Robot Manipulator Based on BODE-CS Algorithm

“…Similar to the Jacobian IK method, the iteration of the Newton method is complex and difficult to implement. Due to the problems with the Jacobian and Newton methods, various intelligent algorithms have been proposed, such as the GA (Genetic Algorithm) [17,18], PSO (Particle Swarm Optimization) [19], DE (Differential Evolution) [20], NN (Neural Networks) [21], ABC (Artificial Bee Colony) [22], and ACS (Ant Colony System) [23]. The main idea involved in using intelligent algorithms for solving inverse kinematics is to transform the problem into minimizing or maximizing a fitness function with an iterative strategy.…”