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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