| Smart warehousing is a key link between production and consumption in the smart manufacturing process.The use of smart work robots,especially the collaborative application of multiple robots,can realize smart warehousing automation and efficient operation.The multi-robot operation scheduling of intelligent warehousing needs to consider the power situation of the robot,so as to reduce the impact of charging timing and charging time on the system operation efficiency;At the same time,repeated charging and discharging will cause the battery to deteriorate continuously and increase the battery replacement cost.Therefore,the influence of robot energy limitation needs to be considered in the scheduling of multirobot jobs in intelligent warehousing.On the other hand,the existing multi-robot scheduling generally uses the swarm intelligence optimization algorithm,but there are generally problems of long solution time and poor optimization effect.The existing algorithm needs to be improved to adapt to the complex scheduling model solution.Considering the difficulty of solving the model,an improved sparrow search algorithm is proposed to solve the multi-robot scheduling model.The main research contents of this paper are as follows:First,study the impact of robot energy limitation on the intelligent warehouse operation system,analyze the multi-robot charging and discharging characteristics and battery consumption characteristics,and use the charging and discharging efficiency and battery consumption as energy constraints to minimize the maximum operating time and the total battery consumption cost of the system.To optimize the objective,a multi-robot scheduling model considering energy constraints is constructed.Secondly,based on the analysis of the principle of the sparrow search algorithm,improvements are made from three perspectives: population initialization,convergence speed and perturbation strategy.Among them,the Cubic chaotic map is used to make the initial population distribution more uniform,the nonlinear sine and cosine algorithm is used to speed up the convergence speed,and the firefly perturbation strategy combined with the Gaussian factor is introduced to improve the convergence accuracy.The performance of the proposed algorithm and other swarm intelligence algorithms is compared using the benchmark function,and the adaptability of the improved sparrow search algorithm to different types of functions and the superiority of the optimization ability are verified.Finally,taking the special material rack for automobile assembly workshop online as a case background,the multi-robot scheduling model of this paper is used to optimize the robot scheduling process of the material rack online,and the improved sparrow search algorithm is used to solve the model.The seven indicators before and after optimization are compared and analyzed to verify the effectiveness of the model and algorithm in this paper to solve the actual intelligent warehouse robot scheduling problem. |
