Energy-Efficient Control of Collaborative Robots Through Intelligent Optimization Algorithms

Abstract

Abstract This research introduces a comprehensive framework for attaining energy-efficient control of collaborative robots via intelligent optimization techniques, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Reinforcement Learning (RL). This research commences with an intricate kinematic and dynamic modeling of the UR5e collaborative robot that, employs the Denavit-Hartenberg (DH) convention to formulate precise motion relationships and dynamic equations that incorporate actuator power, inertia, and gravitational influences. It creates a complete model of energy use that accounts for included actuators, sensors, control systems, and other parts. It also makes fair guesses about how efficient they would be and how they would work. The issue formulation centers on reducing overall energy consumption during job execution while preserving trajectory precision, motion fluidity, and compliance with dynamic and safety restrictions. A multi-objective optimization strategy is suggested to reconcile energy efficiency, job completion duration, and motion smoothness through the utilization of weighted cost functions. The UR5e robot did pick-and-place and assembly tasks on the ROS-Gazebo and MATLAB/Simulink platforms. These tasks were tested in both real world environments and in simulations. The results showed that PSO reduced energy by the most, at 29%, followed by RL with 25% and GA, with 22%. None of the three methods affected 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 significant effects 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 human-centric manufacturing initiatives by allowing robots to consume less energy, operate more reliably, and exhibit greater environmental sustainability. Future studies are also suggested the future use of AI-driven optimization, predictive maintenance and digital twin integration, which will allow robots to work together to control energy in real time.