Trajectory optimization for 6 DOF robotic arm using WOA, GA, and novel WGA techniques

Abstract

• Optimized trajectory for a 6 DOF KUKA arm using inverse kinematics. • Applied WOA, GA, and hybrid WGA for efficient robotic trajectory optimization. • Minimized time, and energy under multi-objective constraints. • Validated algorithms on four paths, ensuring efficiency and reliability. • WGA outperformed GA & WOA, enhancing energy efficiency and speed Optimizing the trajectory of robotic arms with a high degree of freedom (DOF) is a significant challenge due to the complexity of the design space and the need to balance competing objectives such as time efficiency and energy consumption. This study addresses the increasing demand for industrial robotic systems capable of performing tasks with enhanced precision, reduced operational costs, and improved sustainability. To this end, we propose the Whale Genetic Algorithm (WGA), a novel hybrid optimization technique that combines the global exploration strengths of the Whale Optimization Algorithm (WOA) with the local refinement capabilities of the Genetic Algorithm (GA). The WGA is designed to optimize robotic arm trajectories by minimizing reachability time and energy consumption while adhering to kinematic and operational constraints. As a case study, the WGA was applied to a 6-DOF KUKA KR 4 R600 robotic arm, with performance evaluated across four virtual missions involving multiple target points. The results demonstrated that the WGA outperformed standalone WOA and GA methods, achieving up to 44 % faster reachability times and 15 % lower energy consumption while maintaining operational feasibility. Additionally, the WGA exhibited superior convergence efficiency, showcasing its robustness in solving complex trajectory planning problems. Experimental verification was conducted in a laboratory environment using the KUKA KR 4 R600 robotic arm to validate the theoretical results. The experimental findings closely aligned with the simulation predictions, demonstrating minimal deviations in execution time (6 %-12 %) and energy consumption (5 %-10 %). These findings underline the WGA's potential to significantly advance the efficiency and sustainability of robotic systems, contributing to the development of more optimized and energy-conscious industrial automation solutions.