Optimizing the path of seedling transplanting with multi-end effectors by using an improved greedy annealing algorithm

Abstract

Automated transplanters perform the repetitive task of replugging bad or missing cells with healthy seedlings in greenhouses. The work efficiency of transplanters can be improved by optimizing the transplanting paths of manipulators. In this study, an improved greedy simulated annealing algorithm (IGSA) based on MTSP model was proposed for path optimization. Aimed at improving the computation speed of the algorithm, a new improved 2-Opt algorithm based on the metropolis criterion was utilized. Moreover, the greedy algorithm (GRA) and the greedy genetic algorithm (GGA) were modified to adapt to the multi-end effectors’ replugging operation. The performance of IGSA and those of the GGA, GRA, and common sequence method (CSM) in path planning for replugging transplanting were compared in terms of their optimization effects and computation times. Then, manipulators with different numbers of end effectors (four, five, and six end effectors) were used to verify the increased effect of the path planning. Under the verification test conditions, the comparative average optimization ratios of GRA, GGA, and IGSA compared to the CSM were 4.87 %, 10.64 %, and 20.07 %, respectively. The performance ranking of the methods with short average paths was in the order of IGSA, GGA, GRA, and CSM. Compared with the average path shortening ratio of the four end effectors, those of the five and six end effectors were 11.44 % and 18.68 %, respectively. The average computation times of CSM, GRA, GGA, and IGSA were 0.002, 0.007, 6.94, and 3.49 s in MATLAB (R2019a), respectively. All of them can meet the real-time operation requirements except for GGA. In contrast to GGA, GRA, and CSM, the proposed IGGA performed effectively in the path planning of the seedlings’ replug transplanting owing to its comprehensive performance derived from its path optimization ratio and low cost in terms of computation time.