Omnidirectional AGV path planning based on simulated annealing genetic algorithm

To solve the problems of traditional Genetic Algorithm (GA) in path planning of Automated Guided Vehi-cle (AGV) such as easy to fall into local Optimum, slow convergence and non-shortest path length, an improved GA combining artificial potential field method and simulated annealing idea was proposed. Combined with artificial Potential field method, a guided initial population generation strategy was designed to improve the initialization speed of the algorithm. Then, constraint conditions such as the size of the rotation Angle and the numher of unnecessary turns were added into the fitness function to improve the smoothness of the path. Based on simulated annealing algorithm, the selection operator was improved to enhance the global search ability. Edit distance was introduced to screen individuals before crossover to prevent invalid crossover, and the delete operator was added to solve the Problem of redundant nodes and get a shorter path. The experimental Simulation results showed that the improved algorithm had shorter path length and better convergence effect, and effectively prevented the algorithm from falling into local Optimum. After the ROS test platform verification, the search path is more advantageous, which proved the ef-fectiveness and feasibility of the improved algorithm to a certain extent. © 2024 CIMS. All rights reserved.