Path Planning of Library Management Robot Based on PDO-ACO Algorithm

A path planning method based on Particle Differential Optimization-Ant Colony Optimization (PDO-ACO) algorithm for library management robots is proposed in the study, aiming to solve the problem of low efficiency of current path planning methods in complex environments. The method combines the negative feedback improved ant colony algorithm and particle difference optimization algorithm to enhance the accuracy, stability and environmental adaptability of path planning. The study innovatively introduces the negative feedback mechanism to improve the ant colony algorithm, which effectively avoids the problem that the traditional algorithm is prone to fall into the local optimum, and combines with the particle difference optimization algorithm to be applied to the path planning of the library management robot, which demonstrates high adaptability and robustness. The experimental results show that the method outperforms other common path planning methods in terms of error rate and redundant path rate, and the average path lengths are 54.507m and 57.456m, respectively, and the convergence speed and accuracy are improved by 57% and 45.5%, respectively. The method shows high accuracy, stability and robustness in the actual library management environment, which can effectively avoid the risks in path planning, reduce the management cost, and provide a new technical solution for the construction of intelligent library system.