Aiming at disadvantages of particle swarm optimization in the path planning of mobile robots, such as low convergence accuracy and easy maturity, this paper proposes an improved particle swarm optimization algorithm based on differential evolution. First, the concept of corporate governance is introduced, adding adaptive adjustment weights and acceleration coefficients to improve the traditional particle swarm optimization and increase the algorithm convergence speed. Then, in order to improve the performance of the differential evolution algorithm, the size of the mutation is controlled by adding adaptive parameters. Moreover, a “high-intensity training” mode is developed to use the improved differential evolution algorithm to intensively train the global optimal position of the particle swarm optimization, which can improve the search precision of the algorithm. Finally, the mathematical model for robot path planning is devised as a two-objective optimization with two indices, i.e., the path length and the degree of danger to optimize the path planning. The proposed algorithm is applied to different experiments for path planning simulation tests. The results demonstrate the feasibility and effectiveness of it in solving a mobile robot path-planning problem.

Aiming at disadvantages of particle swarm optimization in the path planning of mobile robots, such as low convergence accuracy and easy maturity, this paper proposes an improved particle swarm optimization algorithm based on differential evolution. First, the concept of corporate governance is introduced, adding adaptive adjustment weights and acceleration coefficients to improve the traditional particle swarm optimization and increase the algorithm convergence speed. Then, in order to improve the performance of differential evolution algorithm, the size of the mutation is controlled by adding adaptive parameters. Moreover, a "high-intensity training" mode is developed to use the improved differential evolution algorithm to intensively train the global optimal position of the particle swarm optimization, which can improve the search precision of the algorithm. Finally, the mathematical model for robot path planning is devised as a two-objective optimization with two indices, i.e., the path length and the degree of danger to optimize the path planning. The proposed algorithm is applied to different experiments for path planning simulation tests. The results demonstrate the feasibility and effectiveness of it in solving mobile robot path planning problem.

Robotic grasping has been widely used in various industries. How to meet the requirements of grasping accuracy and grasping speed at the same time is a challenging problem in real-time grasping tasks. In this paper, aiming at the real-time grasping task in retail warehousing, a lightweight grasping pose estimation model for retail warehousing is proposed. The model first uses the Focus module to perform lossless double downsampling, and learns each feature map of the upper layer through the dilated convolution block to expand the receptive field; then, the R-Resblock structure is improved to perform multi-scale feature fusion, and a lightweight RFB-SE module is designed to enrich feature information and reduce the number of parameters. Finally, after upsampling and restoring the image, the grasping quality, grasping angle, and grasping width of the target are regressed to obtain the optimal grasping pose of the target item. Experiments are carried out in the Cornell dataset, Jacquard dataset, and simulation environment respectively. The experimental results show that the method has a grasping accuracy of 97.8% and a grasping speed of 78FPS on the Cornell dataset. The success rate is 91.5%, and the grasping task in a retail warehouse environment is simulated in grasping simulation experiments.