Optimization of Seedlings Transplanting Strategy Based on Greedy Algorithm

Tong, Junhua; Jiang, Huanyu; Xu, Xiaolong; Chao, Fang

doi:10.1007/978-3-642-25661-5_5

Cited by 2 publications

(3 citation statements)

References 5 publications

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“…A total of 32 (specification: 4 × 8), 50 (specification: 5 × 10), 72 (specification: 6 × 12), and 128 (specification: 8 × 16) holes are frequently used in the North-West of China. It is known from reference [21] that the randomness of the positions of M holes in the S tray and N healthy seedlings in the T tray determine that there are paths available for replanting seedlings. In the case of 50 holes, if inferior quality seedlings account for 5-20% of the total, M = 5 in the S tray and the healthy seedlings N = 45 in the S tray, there are alternative transplanting paths.…”

Section: Path Planning Methodsmentioning

confidence: 99%

“…GA has advantages in computation time, but it does not always find the optimal solution. The greedy genetic algorithm (GGA), developed by combining GRA and GA, has been used to good effect for the thinning and transplanting of plug tray seedlings, with improved path optimization and computation time [15][16][17][18][19][20][21]. Ant colony algorithm (ACA) is a common algorithm to solve combinatorial optimization problems, and its optimal path is determined by the pheromone concentration released by bionic ants.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing the Path of Plug Tray Seedling Transplanting by Using the Improved A* Algorithm

Wang

Liu

et al. 2022

Agriculture

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In greenhouse nurseries, one of the important tasks of the automatic transplanter is replanting missing or bad seedling holes with healthy seedlings. This requires the transplanter to spend significant time moving between the supply trays and target trays during replanting. The diversity and complexity of the transplanting routes affect transplanter efficiency. Path planning method can find a better path for the manipulator and improve the efficiency of transplantation. The A* algorithm (A*), which is one of the optimal path search algorithms, is often used in practical applications of path planning. In this paper, the heuristic function of the A* is optimized by the ant colony algorithm (ACA), and an improved A* algorithm (Imp-A*) is obtained. Simulation tests and transplanting trials of Imp-A*, A*, ACA, Dijkstra (DA), and common sequence method (CSM) were carried out using 32-, 50-, 72-, and 128-hole plug trays. The results show that Imp-A* inherits the advantages of A* and ACA in terms of path planning length and computation time. Compared to A*, ACA, DA, and CSM, the transplanting time for Imp-A* was reduced by 2.4%, 12.84%, 11.63%, and 14.27%, respectively. In just six trays of transplanting tasks, Imp-A* saves 60.91 s compared to CSM, with an average time saving of 10.15 s per tray. The combination optimization algorithm has similar application prospects in agriculture.

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Section: Path Planning Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing the Path of Plug Tray Seedling Transplanting by Using the Improved A* Algorithm

Wang

Liu

et al. 2022

Agriculture

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show abstract

“…The machine can transplant through the three direction movement. And transplanting paths were simple plan, but did not involve motion function research [4]; In 2008, Feng Shijia et al designed a three--translational parallel transplanting robot. Transplanting movement were simple planned by acceleration, constant velocity and deceleration three steps.…”

Section: Introductionmentioning

confidence: 99%

Transplanting Path Planning and Motion Functions Research of the High-Speed Tray Seedling Transplanting Robot

Yan

et al. 2013

AMR

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High-speed tray seedling transplanting robot was developed which used two-DOF parallel mechanism as transplanting mechanism, and pneumatic manipulator as the end-effector, so it can achieve high speed of transplanting. According to transplanting seedlings from high-density plug to the low-density plug, a type of transplanting path was put forward which consists of rise, translation and decline, in this paper, it was called three-stage trajectory. Analysis the movement characteristic of each stage, make sure the stability of transplanting, then the sine motion law, quintic polynomial motion law and septic polynomial motion law were put forward as the Transplanting movement function according the analysis of movement characteristic. Compared the transplanting efficiency and stability of the three motion law through their velocity, acceleration and saltus, finally, quintic polynomial motion law was preferred as transplanting robot motion law. Transplanting experiment was taken on the developed prototype, the transplanting frequency was more than 60 per minute and the qualified rate can reach 92.71%.

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Optimization of Seedlings Transplanting Strategy Based on Greedy Algorithm

Cited by 2 publications

References 5 publications

Optimizing the Path of Plug Tray Seedling Transplanting by Using the Improved A* Algorithm

Optimizing the Path of Plug Tray Seedling Transplanting by Using the Improved A* Algorithm

Transplanting Path Planning and Motion Functions Research of the High-Speed Tray Seedling Transplanting Robot

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