Path Optimization of Welding Robot Based on Improved Ant Colony Algorithm
In response to the slow convergence speed and susceptibility to local optima issues in the tradi-tional ant colony optimization(ACO)algorithm for welding robot path planning,a modified algorithm called dynamic weighted ant colony algorithm(DWAG)is proposed.DWAG enhances the information pheromone update process based on dynamic weight strategy and sorting factor strategy,accelerating the convergence speed during the solving process.By incorporating crossover and mutation operations from the genetic algorithm,the search space in the solving process is expanded.Lastly,DWAG is validated through 20 simulations at a certain workstation for welding the rear floor assembly in a white car body.The simula-tion results demonstrate that compared to ACO,DWAG achieves shorter welding paths,faster convergence speed,and better stability in optimizing performance during problem-solving.
welding robotpath planningimproved ant colony algorithmsimulation
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NSTL
万方数据
维普
