Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization

The implementation of Industry 5.0 necessitates a decrease in the energy consumption industrial robots. This research investigates optimization for optimal motion planning dual-arm robot. objective function minimization problem is stated based on execution time and total robot arm configurations its workspace pick-and-place operation. Firstly, PID controller being used to achieve parameters. parameters are then fine-tuned using metaheuristic algorithms such as Genetic Algorithms Particle Swarm Optimization methods create more precise trajectory, resulting an energy-efficient configuration. results different were compared with both algorithms, which shows better compatibility terms efficiency. feasibility demonstrated by conducting experiments robot, named duAro. In efficiency, show that motions can save than single-arm Furthermore, combining configuration approaches saves when controllers alone.