When a single mobile robot is used in SLAM construction, the low efficiency of SLAM construction will occur due to the small number of mobile robots. When multi-mobile robots are used in SLAM construction, aiming at the two-dimensional raster local environment map generated by multiple mobile robots, this paper proposes an improved real-time fusion method of two-dimensional raster map based on feature point matching. The improved algorithm PROSAC is adopted to replace RANSAC algorithm and improve algorithm efficiency to eliminate mismatched feature points. Improve the efficiency of map fusion. First, two mobile robots were used to construct the local environment map by using the Gmapping mapping algorithm respectively. Then, the constructed two-dimensional raster map was processed in gray scale, and the feature points in the raster map after gray scale processing were extracted, filtered and matched, and finally the global map fusion was completed. Through gazebo simulation experiment, local maps constructed by multiple mobile robots were fused to verify the feasibility and effectiveness of the proposed method.
Taking a certain optical mold as the research object, addressing the issues of prolonged cooling time and warping deformation of the product due to uneven cooling that affects optical performance, a conformal cooling channel based on 3D printing technology was designed and simulated with Fluent fluid analysis software. By comparing the simulation results of traditional drilled channels, single-channel conformal channels, and multi-micro parallel conformal channels, it was found that the multi-micro parallel conformal channels can significantly improve the cooling efficiency of the injection molding process and reduce the temperature difference on the molding surface of the mold, where the molding cycle was shortened by 23.3%, and the temperature difference on the molding surface of the mold during the injection molding process was reduced to within 1.2℃.
Aiming at the problem of complex and time-consuming PID parameter tuning of Brushless DC reduction motor. A PID parameter tuning method based on improved particle algorithm is proposed. Firstly, the principle of orbiting each particle is proposed, which improves the inertia weight of traditional particle swarm optimization algorithm; Then, the particle swarm mutation method is proposed to ensure that there will be no local optimization in the iterative process. The transfer function of the motor is measured by experimental method, and then the modeling and simulation analysis are carried out using MATLAB. The experimental and simulation results show that the improved particle swarm optimization algorithm will not fall into local optimization compared with the ordinary particle swarm optimization algorithm. Compared with the parameters obtained by the PID parameter tuning and the parameters obtained by the Zn and AC tuning methods, the rising time is less, the time required for stabilization is shorter, and the deviation after stabilization is smaller. Using improved particle algorithm for PID parameter tuning can improve the accuracy of PID control and reduce the complexity and timeconsuming problem of PID parameter tuning.
Aiming at the problem of low mapping efficiency of Simultaneous Localization And Mapping (SLAM) algorithm for single robot, a real-time fusion scheme for multi-robot raster maps based on improved map_meiging package is designed, and the PROSAC algorithm with improved RANSAC algorithm is used to eliminate mismatched feature points and improve the efficiency of map fusion. Firstly, the two single robots construct local maps based on the Gmapping algorithm, and then extract the feature points of the raster map after grayscale processing, match and complete the map fusion after purification, which does not need to predict the initial pose of the mobile robot in advance. Finally, test experiments are carried out in the Gazebo simulation environment to verify the effectiveness, real-time and robustness of the method.
A two-mobile robot collaborative autonomous exploration method is proposed for map exploration and target search in unknown environments. The method first uses a boundary exploration strategy based on the RRT algorithm to achieve autonomous movement of the first mobile robot, while using the Gmapping-SLAM algorithm to complete the map construction, then the second robot subscribes to the completed map based on the ROS distributed communication method, and then sets the target location for it. The robot uses the A* algorithm in combination with the TEB algorithm to plan the path and navigate to the target location. Finally, the practicality and effectiveness of the exploration strategy is verified through simulation experiments
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