Path planning for 6 DOF robotic arm based on novel whale genetic algorithm compared to fifth polynomial planning

Path planning for robotic manipulators is a critical challenge in robotics and automation, especially in confined or congested situations where seamless and collision-free paths are important. It enables the robot to calculate ideal, collision-free trajectories while accounting for its mechanical limitations and environment, ensuring smooth motion, reducing unnecessary movement, and facilitating precise positioning for operations such as assembly, welding, or surgery. Efficient path planning improves safety by circumventing barriers and offers adaptation to dynamic settings, rendering robotic arms more dependable and adaptable in industrial and service applications. Nevertheless, conventional trajectory generation techniques frequently yield possible yet suboptimal pathways that undermine smoothness, path length, or execution efficiency. This work introduces a novel hybrid metaheuristic, the Whale–Genetic Algorithm (WGA), designed to address these issues for the KUKA KR4 R600 robot manipulator. The WGA integrates the exploratory prowess of the Whale Optimization Algorithm (WOA) with the exploitative efficiency of Genetic Algorithms (GA), yielding enhanced convergence and solution quality. The WGA is benchmarked against a quintic polynomial trajectory, which acts as a baseline algorithm for assessment. Simulation results indicate that WGA attains a 41.85% decrease in path length compared to the baseline, while generating smoother trajectories and preserving viable joint configurations. These findings indicate that the proposed WGA framework is a promising offline trajectory-optimization method for improving path-length efficiency and motion smoothness under the tested simulation conditions. Further validation using physical robotic platforms, multiple task configurations, and obstacle-inclusive environments is required before confirming its industrial applicability.