中文摘要：

温室育苗需要通过补苗移栽作业用健康钵苗替换穴盘内未发芽或劣质的钵苗,保证钵苗的质量。自动补苗移栽机可利用机器视觉获取穴盘苗健康信息,控制末端执行器抓取钵苗进行补苗作业,移栽效率高。穴盘内需补苗孔穴的位置具有随机性,对补栽路径进行规划,可进一步提高补栽效率。本文综合贪心算法和遗传算法的特性提出一种贪心遗传算法,在分段步长取8,优化代数取100时,可实现稀疏和密集穴盘的补栽路径优化,具有鲁棒性。贪心遗传算法所规划补苗路径长度与全遗传算法接近,均值差在443 mm以内;相比优化前的固定顺序法,贪心遗传算法路径长度可缩短33.8%～41.3%,缩短长度随空穴数量增加而加长;贪心遗传算法与全遗传算法规划补栽路径耗时分别为1.81 s和5.59 s。对比可知,贪心遗传算法更有利于自动移栽机输送单元和移栽单元间的动作衔接,可进一步提高自动移栽机效率。

英文摘要：

Replugging tasks make seedling in well consistency in greenhouses. Healthy seedlings are used to replace the ungerminated or poor growth seedlings. This task is labor intensive by traditional manual method. And automated transplanters do the replugging task in high efficiency and good quality. According to the seedlings healthy information which is detected by machine vision, end-effector grasping healthy seedlings does the repetitive replugging task. The position of vacancy holes in plug tray are randomly. Optimizing the seedling grasping sequence can decrease the transplanting path which can improve working efficiency. A greedy genetic algorithm （GGA） was proposed for replugging tour planning which combined the character of greedy algorithm （GAS） and genetic algorithm （ GA）. The algorithm was robustness. The GGA was suitable for sparse and dense trays＇ path optimization when segmentation step value and hereditary algebra were 8 and 100, respectively. The average path deviation of GGA and GA was 443 ram. And their effectiveness was better than that of GAS. Compared with fixed sequence method （FS） , the range of optimization amplitude for GGA was 33.8% -41.3%. GA and GGA could finish the optimization operation in 1.81 s and 5.59 s, respectively. The results showed that GGA was more suitable for the action requirement between delivery unit and transplanting unit. The working efficiency of automated transplanter was further improved.