Design of Precision Planetary Gear Reducer for Industrial Robot Joints Optimized Through the Combination of Genetic Algorithm and Ant Colony Algorithm

The demand for high-precision and high-efficiency transmissions in industrial robot joints has made the optimal design of precision planetary gear reducers a key research focus. To address the multi-objective constraints in optimizing transmission efficiency, load capacity, and thermal performance, a hybrid optimization method combining genetic algorithm (GA) and ant colony algorithm (ACA) is proposed. By integrating the global search capability of GA with the local optimization ability of ACA, a two-stage optimization model linking the fitness function to design variables is established. Experimental results demonstrate that this method significantly enhances the performance indicators of the reducer. In multi-condition tests, the transmission efficiency was increased to a maximum of 93.5%, the maximum gear temperature rise was reduced by 4.5°C, and torque fluctuation decreased by 21.7%. The convergence speed of the hybrid algorithm was 16.7%-31.8% faster than that of single algorithms, exhibiting superior computational efficiency. Sensitivity analysis verified the significant impact of modulus, tooth width, and pressure angle on transmission efficiency and stability. Reliability assessment results indicate that the optimized scheme maintains high stability across multiple runs, with result deviations controlled within 0.010. Therefore, the hybrid optimization design based on GA and ACA exhibits significant advantages in improving reducer performance and reliability optimization, providing an effective reference for the engineering design of transmissions in industrial robots.