The fuzzy improved DWA autonomous charging algorithm is proposed for the problems of high hardware cost, complex docking process and low docking efficiency of robot autonomous charging technology. A horizontal attitude adjustment function and a vertical attitude adjustment function have been added to the azimuth evaluation function of the traditional DWA algorithm. During the robot electrode docking process, the robot uses a fuzzy control algorithm to adjust the weight coefficients of the horizontal posture adjustment function and the weight coefficients of the vertical posture adjustment function according to the horizontal distance between itself and the charging pile, in order to control the robot's direct navigation to the charging pile.
To solve the problem of obstacle avoidance cars find the shortest path without collision between two locations. Firstly, the method of building a raster map is used in MATLAB to simulate the working environment of the trolley. Secondly, a hybrid algorithm combining particle swarm algorithm and genetic algorithm is proposed, and it is applied to the path optimization of the trolley. The hybrid algorithm combines the traditional particle swarm algorithm and the genetic algorithm, retains the speed and position iteration mode of the particle swarm algorithm, and integrates the cross and mutation operations in the genetic algorithm. The results show that compared with the traditional particle swarm algorithm and genetic algorithm, the hybrid algorithm can enhance the ability to jump out of local extremes in the process of population search, improve the convergence progress of the algorithm, and ensure that the obstacle avoidance trolley can find a more suitable moving path between the starting point and the target point.
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