Particle swarm optimization vs. whale optimization algorithm for robotic arm path planning: A controlled study on the KUKA KR4 R600

• Unified 6-DOF pipeline: quintic joint-space + APF with strict limits. • Like-for-like comparison of WOA vs PSO under identical budgets and weights. • Obstacle-free, APF, and optimized scenarios reported with full metrics. • WOA yields shortest obstacle-avoiding path (0.714 unit) with smooth dynamics. • PSO achieves 0.791 unit path; tunable parameters shape terminal dynamics. Path planning for robotic arms is essential for secure and efficient manipulation in congested industrial environments. This research assesses a three-phase pipeline comprising quintic trajectory creation, potential-field obstacle management, and metaheuristic optimisation, applied to a KUKA KR4 R600 while adhering to uniform kinematic restrictions and joint limits. In the absence of obstacles, the quintic baseline produces a total path length of 0.527 units. In an obstacle-laden environment, the collision-free path using potential fields measures 0.9101 units, but the application of the Whale Optimisation Algorithm (WOA) inside the same framework results in a reduced collision-free path of 0.714 units. To enhance the benchmarking of metaheuristic performance under uniform settings, Particle Swarm Optimisation (PSO) is integrated utilising the similar objective function (path length, smoothness, obstacle penalties), constraints, and kinematic model. PSO achieves a collision-free trajectory of 0.791 unit, with median stabilization following 100 iterations. The results quantify the trade-offs between the baseline, potential-field routing, and two metaheuristics for industrial 6-DOF manipulators, informing practical parameterization and implementation.