Computation of inverse kinematics of redundant manipulator using particle swarm optimization algorithm and its combination with artificial neural networks

Abstract

Nowadays, the world of the industry is not working without the robotics’ manipulators. Logically, robotic manipulators are very old topics and lots of researches have been done on them. However, with implementing recent methodologies on them, many contributions have been achieved, which even become a part of human’s life. The objective of this research is to achieve more precise position for end effector of rescue robotic manipulator in real time within reasonable calculation time. In this regard, all the inverse kinematics formulation of the 2, 3, and 4-links manipulator are derived, and be solved by Particle Swarm Optimization (PSO) method. It should be mentioned that this optimization method will be used to solve the inverse kinematics problem in real-time mode which consequently making the results more accurate. The presented PSO method is implemented on a rigid 3-link manipulator with three rigid revolute joints. It is pointed out that the application of the robot manipulator in this paper is used as a rescue robot for the first time, which has stronger environmental adaptability and real-time performance. It is shown in this study, the precision of PSO method is about 10-3.6 (0.0002511) within 50 iterations which is a lot better than previous research (i.e. an ANN application did the same study and used the same platform only can generate a result with the error of 0.045589 in 1000 epochs). However, as the PSO method is real-time, the presented PSO method will consume a lot more time than the other methods such as ANNs. For instance, the PSO method took 2 minutes and 10 seconds to solve an inverse kinematics problem comparing with 36.10 seconds that solved by an ANNs. Therefore, author developed a combination approach that mixing the PSO method and ANN technique, namely PSO-ANN, to solve rescue robotic manipulator in real time, which can generate high accuracy results in a short testing time. Due to the aforementioned description, the contribution of this study is to reach to more precise methodology with less error due to the previous studies and also presenting the methodology that can solve all the issues in regards of the run-time of the real-time methods.