童俊华,蒋焕煜,武传宇.基于贪心算法的温室钵苗稀植移栽路径优化[J].农业机械学报,2016,47(3):8-13.
Tong Junhua,Jiang Huanyu,Wu Chuanyu.Optimization of Seedlings Lower Density Transplanting Path Based on Greedy Algorithm[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(3):8-13.
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基于贪心算法的温室钵苗稀植移栽路径优化   [下载全文]
Optimization of Seedlings Lower Density Transplanting Path Based on Greedy Algorithm   [Download Pdf][in English]
投稿时间:2015-07-22  
DOI:10.6041/j.issn.1000-1298.2016.03.002
中文关键词:  温室  穴盘苗  稀植移栽  路径优化  贪心算法
基金项目:国家自然科学基金项目(51275457)、浙江省自然科学基金项目(LQ16E050006)、浙江理工大学科研启动基金项目(14022211-Y)、〖JP〗机械工程浙江省高校重中之重学科和浙江理工大学重点实验室优秀青年人才培养基金项目(ZSTUME01B11)和杭州市科技发展计划项目(20140432B15)
作者单位
童俊华 浙江理工大学
浙江省种植装备技术重点实验室 
蒋焕煜 浙江大学 
武传宇 浙江理工大学
浙江省种植装备技术重点实验室 
中文摘要:温室育苗中,钵苗需从高密度穴盘向低密度穴盘移栽以获得生长空间。温室钵苗自动移栽机获取穴盘中钵苗健康信息并对健康钵苗进行稀植移栽,代替传统人工作业,且效率高、质量好。钵苗稀植移栽路径包括移栽机末端执行器从原点出发,将高密度盘内的健康钵苗逐一抓取移栽至低密度盘,直到完成回到出发点。钵苗取栽位置的先后秩序决定了稀植路径的长短,遍历搜索算法规划路径计算量巨大,无法满足移栽实时性要求。本文基于贪心算法对常规的4种固定顺序路径规划方案分别优化,共组成8种路径规划方案,分别对稀疏和密集穴盘稀植路径进行规划,比较分析优化算法的有效性。结果表明按列扫描的2种贪心优化方案比固定顺序方案要优,规划路径长度与穴盘缺苗数量成正比趋势。最优化方案GAS3对密集穴盘稀植规划路径,相比固定顺序方案的优化幅度达10.6%,算法平均耗时0.84s。穴盘缺苗数对路径缩短优化效果有显著影响,缺苗数增加后优化幅度有所降低。贪心优化方案使稀植移栽路径得到优化,也满足作业实时性要求,提高了钵苗移栽效率。
Tong Junhua  Jiang Huanyu  Wu Chuanyu
Zhejiang Sci-Tech University;Zhejiang Provincial Key Laboratory of Transplanting Equipment and Technology,Zhejiang University and Zhejiang Sci-Tech University;Zhejiang Provincial Key Laboratory of Transplanting Equipment and Technology
Key Words:greenhouses  seedlings  lower density transplanting  path optimization  greedy algorithm
Abstract:Seedlings need to be transplanted from high density into lower density trays for more space to grow in the greenhouse. Automatic transplanter could detect seedlings health state and do the tasks of lower density transplanting. It can work in high efficiency and good quality comparing with the traditional manual work. The lower density transplanting path includes end effector leaving from original point, grasping health seedlings from high density to lower density trays one by one, and going back to the start at last. The distances of transplanting path were decided by seedlings grasping order. Traversing search algorithm consumed large calculation to plan this transplanting path which does not meet the real time requirement. In this paper, four schemes of fixed sequences were optimized by using the greedy algorithm separately, and eight schemes were formed totally. The path planning methods were used in the sparse and dense trays to verify the effectiveness of the greedy algorithm scheme. Comparing with the longest mean value of fixed sequence schemes for high density trays path planning, optimal scheme of GAS3 could get a better result with more than 10.6% amplitude optimization. The average calculation time of the optimization scheme was 0.84s. Significance test showed the increase of vacancy holes reduced the effectiveness of length shorten. As a whole, the greedy algorithm scheme optimizes the lower density transplanting path, meets the real time work requirement, and improves the transplanting work efficiency.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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