Two-Layer Path Planning for Welding Robots: Improved Ant Colony Optimization for Distance Optimization Coupled with Improved Genetic Algorithm for Time-Optimal Trajectory Generation

Welding robot path planning has drawn increasing attention due to its broad applications in intelligent manufacturing. This paper proposes an integrated optimization framework that combines an improved ant colony optimization (IACO) algorithm for shortest-distance planning and an improved genetic algorithm (IGA) for shortest-time fixed-point trajectory generation. In the distance-planning layer, IACO is enhanced with parameter optimization and a dynamic random-disturbance factor to overcome the premature convergence and limited global search capability of traditional ACO. This achieves a high-quality welding point sequence with reduced computational overhead. In the time-planning layer, cubic B-spline interpolation is adopted to ensure path smoothness, and IGA is designed with a penalty-augmented fitness function and sinusoidal adaptive crossover and mutation operators to minimize execution time while satisfying kinematic constraints. Simulation experiments on a 24-point welding task demonstrate that the proposed IACO converges within approximately 100 iterations and reduces the best-found path length to 822 cm, which is 12.9% shorter than that of traditional ACO. Furthermore, the IGA reduces the total welding task time from 129 s to 96.30 s, representing a 24.7% improvement in time efficiency. Compared with conventional PSO and A* algorithms, the proposed method saves 17.60 s and 11 s, respectively. Additional comparisons with state-of-the-art hybrid algorithms confirm that the IACO-IGA framework achieves superior performance in terms of path length, convergence speed, task duration, and joint motion smoothness, with maximum joint-angle deviation and angular-velocity fluctuation reduced to 3.73 rad and 2.22 rad/s. Simulation-based Gaussian-noise robustness simulations confirm that the hybrid IACO-IGA framework exhibits better anti-interference capability under sensor measurement noise. The results verify the effectiveness and practicability of the proposed method for complex multi-welding-point industrial scenarios (simulation-based theoretical results; physical robot validation remains future work).