Optimizing the Performance Evaluation of Robotic Arms with the Aid of Particle Swarm Optimization

Abstract

In this modern world, robotic evaluation plays a most important role. In secure distance, this leads the humans to execute insecure task. To acquire an effective result, the system which makes the human task easier should be taken care of and the holdup behind the system should be eradicated. Only static parameters are considered and such parameters are not enough to obtain optimized value in existing work. For consecutively attaining optimized value in our previous work, we focused on both static and dynamic parameters in the robotic arm gearbox model. Now, a genetic algorithm is utilized and the result obtained is greater than the existing work. On the other hand, to attain an effective result the genetic algorithm itself is not enough since it takes massive time for computation process and the result obtained in this computation is not as much closer to the true value. By eliminating all those aforementioned issues, a proper algorithm needs to be utilized in order to achieve an efficient result than the existing and our previous works. In this paper, we anticipated to suggest a Particle Swarm Optimization technique that reduce the computation time as well as make the output result as much closer to the true value (i.e.,) experimentally obtained value.