Greedy Algorithm & Fixed Sequence Methods Comparison for Seedlings Lower Density Transplanting Path Planning
Junhua Tong, Gaohong Yu, Chuanyu Wu · 2016
Abstract. Automated transplanter do the repetitive task of seedlings lower density transplanting in greenhouses. It resolves the labor shortage, and makes seedling in well consistency. Shorten end-effector‘s transplanting path length between seedling trays could improve the working efficiency. In this paper, greedy algorithm was used to optimize the transplanting path which was planned by the fixed sequence method. 10 trays with random generation vacancy hole were used to simulate the path planning by 4 fixed sequence methods and 4 greedy algorithm methods. The results show greedy algorithm methods could shorten the path exceeding 19.4% length comparing with fixed sequence methods in totally. For seedling trays with less than 88% seedling emergence rate, the fixed sequence method with scanning by column and following nearby principle get shorter path length. For the majority of seedling trays with more than 88% seedling emergence rate, the greedy algorithm method with scanning by column can get optimization path planning. The result is useful to improve the transplanting efficiency.