A novel hybrid intelligent optimization strategy for multi-objective trajectory planning of robotic arms in precision assembly scenarios

For precision assembly tasks, the accuracy and efficiency of robotic arm trajectory planning directly impact product quality and production efficiency in manufacturing, making it a core technology driving industrial automation upgrades. This research endeavors to establish a sophisticated multi-objective trajectory planning model, specifically engineered to cater to the intricate demands of precision assembly scenarios. The model optimizes for “maximum efficiency, minimum energy consumption, and minimal impact,” quantifying time costs, energy expenditure, and the influence of mechanical impact on assembly precision during the process. To enhance the performance of traditional multi-objective particle swarm optimization (MOPSO), this study proposes an improved CEMOPSO algorithm. This approach enhances initial population diversity by incorporating Chebyshev mapping strategies, dynamically adjusts particle search directions through evolutionary elimination mechanisms, and optimizes constraint handling capabilities via a designed infeasibility evaluation function. Engineering experiments using pyrotechnic grain assembly as a typical scenario validate CEMOPSO’s practical application value. Implementing this algorithm increased robotic arm assembly efficiency by 15.2%, reduced energy consumption by 20.4%, and decreased impact by 26.4%. This demonstrates the effectiveness and engineering applicability of the theoretical methods developed in this study for complex precision assembly tasks.