Energy-Efficient Control of Collaborative Robots through Intelligent Optimization Algorithms

Abstract

This research presents a comprehensive framework for achieving energy-efficient control of collaborative robots through intelligent optimization techniques, including particle swarm optimization, genetic algorithm, and reinforcement learning.  The goal is to produce accurate momentum relations and dynamic equations using the Denavit-Hartenberg convention. Research begins with complex kinematic and dynamic modeling of 5E collaborative robots including actuator forces, inertial and gravitational effects. He created a complete model of energy consumption that included actuators, sensors, control systems and other parts. Its accurate predictions about how efficient they would be and how they would work.  Point formulation focuses on minimizing total energy consumption during job execution while maintaining accuracy in trajectory, fluidity of motion, and compliance with dynamic and safety constraints. The UR5e robot performed pick-and-place and assembly tasks on the ROS-Gazebo and Matlab/Simulink platforms.  The results indicate that PSO has the highest energy reduction of 29%, followed by RL at 25% and GA at 22%. All three methods did not change the duration or accuracy of the work. RL made motion smoother by being able to learn and adapt. Sensitivity analysis showed that the size of the population, the constraints on iterations, and the learning rate all had a big effect on how well optimization worked. The main contributions of this study are the creation of a complete energy modeling framework, the development of multi-objective optimization methods, and the testing of the system in real-world robotic tasks. These findings show that intelligent optimization can enhance green manufacturing and Industry 5.0 initiatives by enabling robots to consume less energy, operate more reliably, and exhibit greater environmental sustainability. In the future, it will consider using AI-driven optimization, predictive maintenance and digital twin integration together. This will allow robots to work together to control energy in real time.