Multi-Strategy Enhanced Grey Wolf Optimizer with Non-Uniform Mutation for Industrial Robot Path Planning in Complex Manufacturing Environments

To address the core drawbacks of traditional robot path planning algorithms in complex constrained environments, including local optima trapping, imbalance between global exploration and local exploitation, and difficulty in coordinating path feasibility with overall performance, this paper proposed a path planning method based on multi-strategy fused Non-Uniform Mutation Improved Grey Wolf Optimization (NUMIGWO). The algorithm integrated four core strategies: piecewise chaotic mapping for population initialization to improve solution space traversal uniformity; a nonlinear decreasing dynamic weight mechanism to adaptively switch between global exploration in early iterations and fine local exploitation in later stages; an adaptive Cauchy- Gaussian hybrid mutation with four parameters (r, α, β, γ) to enable both large-span local optima escaping and fine-grained search; and a path feasibility detection and repair strategy to explicitly embed environmental constraints for reliable obstacle avoidance. Verified by six standard test functions, NUMIGWO significantly outperformed Traditional Grey Wolf Optimization (TGWO) and Particle Swarm Optimization(PSO) in terms of optimal value, mean value, standard deviation and local optima escaping capability. In the application of an automotive engine block workshop, the optimal parameter combination was obtained through L25(5 4 ) orthogonal experiments. The proposed method outperformed TGWO, A*, and RRT in comprehensive performance. Compared with the above algorithms, NUMIGWO reduced the path length by 6.02%, 1.34%, and 20.55% and decreased the average turning angle by 7.29%, 43.56%, and 64.42%, respectively. It achieved a minimum safety distance of 5.64 m and a larger average obstacle distance than A* and RRT, which effectively improved the overall practicability and reliability of robot path planning for intelligent manufacturing applications.