Computation of Inverse Kinematics of Redundant Manipulator Using Particle Swarm Optimization Algorithm and Its Combination with Artificial Neural Networks

Abstract

The industry heavily relies on robotic manipulators at present. Despite their age, recent methodologies have significantly advanced their functionality, integrating them into daily life. Rescue robots are popular. However, their precision and reaction speed are the main issues for real-world rescuing. This research aims to enhance the precision of rescue robotic manipulators’ end-effectors in real time. It achieves this by deriving and solving inverse kinematics formulations for 2-, 3-, and 4-link manipulators using Particle Swarm Optimization (PSO). The PSO method proves highly accurate, outperforming previous studies utilizing Artificial Neural Networks (ANNs). While PSO requires more time than ANNs, a hybrid approach, PSO-ANN, balances accuracy and speed, offering real-time solutions with minimal errors, and contributing to a precise methodology for real-time robotic manipulator operations.