A Fast Path Planning Method of Seedling Tray Replanting Based on Improved Particle Swarm Optimization

Abstract

In the factory nursery, qualified seedlings can be used to replant unqualified seedlings or missing seedlings in the seedling tray through automatic transplanters. Due to the random positions of unqualified and missing seedlings, the end effector of the automatic replanting machine spends substantial time shuttling between the supply tray and the target tray to complete the replanting task. Therefore, we proposed a fast path planning method based on improved particle swarm optimization and compared it with the fixed sequence method and genetic algorithm in experiments with different replanting numbers in different tray types. The experiment shows that the improved particle swarm optimization algorithm and genetic algorithm can shorten the length of the replantation path by about 20% compared with the fixed sequence method, and the running time of the improved particle swarm optimization algorithm is 57.63% less than the genetic algorithm on average. The replanting path optimization method based on improved particle swarm optimization designed in this research can significantly optimize the length and time of the replanting path of the seedling tray, improve the efficiency of the replanting operation, and meet the real-time requirements.

Keywords

• plug seedling replanting
• fixed sequence method
• genetic algorithm
• particle swarm optimization
• path planning