Soft Computing Optimization of Industrial Robot Inverse Kinematics for Intelligent Manufacturing: A Statistical Benchmark of Metaheuristic Algorithms

Accurate and computationally efficient inverse kinematics is a fundamental requirement for industrial manipulators in intelligent manufacturing, where positioning, assembly, and handling demand reliable pose computation under nonlinear constraints. However, the inverse mapping of six-degree-of-freedom manipulators may admit multiple solutions, become ill-conditioned near singularities, or violate joint limits, while metaheuristic comparisons rely on heterogeneous protocols emphasizing best-run results. Herein, a statistical benchmark of five metaheuristics is presented for solving the inverse kinematics of a UR3 manipulator: Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Multi-Verse Optimizer (MVO), Non-Dominated Sorting Genetic Algorithm II (NSGA-II), Salp Swarm Algorithm (SSA), and Whale Optimization Algorithm (WOA). A master–slave architecture separates the stochastic search from a deterministic evaluator that computes an unweighted full-pose residual subject to joint bounds and a Jacobian-determinant screening penalty. Three poses represent reoriented, nominal-height, and near-singular upper-workspace conditions, each assessed over 1000 independent runs with fixed algorithm settings. Nonparametric tests are interpreted with Cliff’s Δ effect sizes. The results reveal distinct accuracy–time trade-offs: CMA-ES obtains the smallest best-run objective at modest runtimes, MVO yields the smallest mean objective but the longest runtimes, and WOA is the fastest with higher residuals. The findings support algorithm selection for offline inverse-kinematics computation and supervisory robotic planning.