立体苗盘管理机器人的机械臂参数优化与试验
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摘要
为使立体苗盘管理机器人的机械臂能够在植物工厂狭窄的作业环境下,灵活、高效地完成目标工作空间的所有搬运和喷洒动作任务需求,同时尽量减小机械臂的操纵空间和结构尺寸,采用理论与试验相结合的方法对机械臂参数进行了优化设计。首先采用D-H法建立了机器人的运动学模型,然后通过工作空间分析确定出优化参数的工作空间约束条件。在此基础上,以"距离最短"和"结构紧凑"为性能指标建立目标优化函数,并利用遗传算法求解出最优的大臂杆长648 mm、中臂杆长472 mm和小臂杆长396 mm,最优机械臂关节转角极限值为96°、68°和126°。最后进行机器人样机的搬运和喷洒运动规划试验,并借助高速摄像系统标记机械臂末端运动轨迹坐标。试验结果表明:优化后的机械臂能够到达目标工作空间的所有极限位置及其他特征位置点,最大绝对定位误差为9.8 mm,最大相对定位误差为0.98%,在允许的误差范围内,能够满足机械臂工作空间对目标工作空间的有效包容。
With the rapid development of modern agricultural technology, plant factory has become the most advanced development stage of facility agricultural. At present, the majority of work tasks in plant factory completed by manpower are labor-intensive and low efficient, therefore, the agricultural intelligent equipment system has become a hot spot in the development of plant factory. In view of the task demand of the carrying and spraying of the three-dimesional seedling tray, the three-dimesional seedling tray management robot was developed. In order to make the manipulator of three-dimesional seedling tray management robot complete all carrying and spraying tasks flexibly and efficiently, meanwhile to reduce operating space and structure size of manipulator, parameters of the manipulator were optimized by the method of theory and experiment. Firstly, in order to determine the relationship between the end coordinate of the manipulator's connecting rod and the base coordinate system, the kinematic model of the robot system was established by D-H method, which was important theoretical basis for the workspace analysis. Then the workspace of manipulator was constructed by graphic method, and the workspace constraint conditions were determined according to the condition that manipulator workspace accommodated target workspace. Based on that, the objective function was established according to shortest distance and compact structure, and genetic algorithm was used to solve the objective function. The optimal rod lengths(big arm, medium arm, small arm) of the manipulator were 648, 472, and 396 mm, and the limit values of the optimal joint angle were 96○, 68○, and 126○. The workspace and the target workspace of the robot were depicted in the MATLAB(Matrix Laboratory) software platform according to the optimal solution of the manipulator parameters, the kinematics equation of the robot and the range of the manipulator's parameters. The simulation result showed that the target workspace was between the inner limiting envelope interfaceand the outer limiting envelope interface of the manipulator, which verified the manipulator's ability to cover the target workspace, and the rationality of the theoretical optimization for the parameters of the manipulator was proved. Finally, in order to further validate whether the manipulator could complete all the action tasks of the target workspace, the robot prototype and the three-dimesional seedling tray experimental platform were built in the laboratory, and the motion planning test of carrying and spraying of the robot system prototype was carried out. The carrying test was planned as follows: According to the target workspace size and the theoretical position coordinate value, the manipulator was controlled to move vertically upward from the lowermost(lower limit) to the topmost(upper limit) of the target workspace, this group of actions were repeated 100 times, and seedling tray was always placed horizontally during carrying. The carrying test mainly verified the manipulator's ability to cover the target workspace in the vertical direction. Spraying test steps were as follows: 1) The initial spraying height value was 100 mm; 2) Divide the seedling disk plane into m×n grids, and each grid point represented the spray position point, m=10, n=20; 3) The target path point group consisted of all the spray points at the current height, and the manipulator was controlled to pass through the target path point group sequentially; 4) The spraying height value was increased by 20 mm; 5) Repeat step 2), 3) and 4) until the spraying height value was equal to 1 020 mm. The spraying test mainly verified the manipulator's ability to cover the target workspace in the horizontal direction. The high-speed video camera system was used to mark trajectory coordinates of manipulator in the motion planning test of carrying and spraying(high-speed camera was KODAK's color CCD(charge coupled devices) camera, a resolution of 512×480 pixels, frame rate of 125 frames/s). Test results showed that the optimized manipulator could reach all limiting positions and other characteristic positions of target workspace, and the maximum relative positioning error was 0.98% which was within error range and could meet the accuracy requirements for manipulator containing the target workspace effectively; what was more, it was proved that the optimal parameters of manipulator were reasonable. Parameters optimization and experiment of three-dimesional seedling tray management robot could provide the reference for trajectory planning and motion control.
With the rapid development of modern agricultural technology, plant factory has become the most advanced development stage of facility agricultural. At present, the majority of work tasks in plant factory completed by manpower are labor-intensive and low efficient, therefore, the agricultural intelligent equipment system has become a hot spot in the development of plant factory. In view of the task demand of the carrying and spraying of the three-dimesional seedling tray, the three-dimesional seedling tray management robot was developed. In order to make the manipulator of three-dimesional seedling tray management robot complete all carrying and spraying tasks flexibly and efficiently, meanwhile to reduce operating space and structure size of manipulator, parameters of the manipulator were optimized by the method of theory and experiment. Firstly, in order to determine the relationship between the end coordinate of the manipulator's connecting rod and the base coordinate system, the kinematic model of the robot system was established by D-H method, which was important theoretical basis for the workspace analysis. Then the workspace of manipulator was constructed by graphic method, and the workspace constraint conditions were determined according to the condition that manipulator workspace accommodated target workspace. Based on that, the objective function was established according to shortest distance and compact structure, and genetic algorithm was used to solve the objective function. The optimal rod lengths(big arm, medium arm, small arm) of the manipulator were 648, 472, and 396 mm, and the limit values of the optimal joint angle were 96○, 68○, and 126○. The workspace and the target workspace of the robot were depicted in the MATLAB(Matrix Laboratory) software platform according to the optimal solution of the manipulator parameters, the kinematics equation of the robot and the range of the manipulator's parameters. The simulation result showed that the target workspace was between the inner limiting envelope interfaceand the outer limiting envelope interface of the manipulator, which verified the manipulator's ability to cover the target workspace, and the rationality of the theoretical optimization for the parameters of the manipulator was proved. Finally, in order to further validate whether the manipulator could complete all the action tasks of the target workspace, the robot prototype and the three-dimesional seedling tray experimental platform were built in the laboratory, and the motion planning test of carrying and spraying of the robot system prototype was carried out. The carrying test was planned as follows: According to the target workspace size and the theoretical position coordinate value, the manipulator was controlled to move vertically upward from the lowermost(lower limit) to the topmost(upper limit) of the target workspace, this group of actions were repeated 100 times, and seedling tray was always placed horizontally during carrying. The carrying test mainly verified the manipulator's ability to cover the target workspace in the vertical direction. Spraying test steps were as follows: 1) The initial spraying height value was 100 mm; 2) Divide the seedling disk plane into m×n grids, and each grid point represented the spray position point, m=10, n=20; 3) The target path point group consisted of all the spray points at the current height, and the manipulator was controlled to pass through the target path point group sequentially; 4) The spraying height value was increased by 20 mm; 5) Repeat step 2), 3) and 4) until the spraying height value was equal to 1 020 mm. The spraying test mainly verified the manipulator's ability to cover the target workspace in the horizontal direction. The high-speed video camera system was used to mark trajectory coordinates of manipulator in the motion planning test of carrying and spraying(high-speed camera was KODAK's color CCD(charge coupled devices) camera, a resolution of 512×480 pixels, frame rate of 125 frames/s). Test results showed that the optimized manipulator could reach all limiting positions and other characteristic positions of target workspace, and the maximum relative positioning error was 0.98% which was within error range and could meet the accuracy requirements for manipulator containing the target workspace effectively; what was more, it was proved that the optimal parameters of manipulator were reasonable. Parameters optimization and experiment of three-dimesional seedling tray management robot could provide the reference for trajectory planning and motion control.
引文
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[2]仝宇欣.设施农业的技术革命--人工光植物工厂[J].科技导报,2014,32(10):84-84.
[3]Kozai T.Resource use efficiency of closed plant production system with artificial light:Concept,estimation and application to plant factory[J].Proceedings of the Japan Academy,Series B,Physical and Biological Sciences,2013,89(10):447-461.
[4]Kozai T.Plant Factory with Artificial Light[M].Tokyo:Ohmsha Ltd.,2012.
[5]辜松,杨艳丽,张跃峰,等.荷兰蔬菜种苗生产装备系统发展现状及对中国的启示[J].农业工程学报,2013,29(14):185-194.Gu Song,Yang Yanli,Zhang Yuefeng,et al.Development status of automated equipment systems for greenhouse vegetable seedlings production in Netherlands and its inspiration for China[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2013,29(14):185-194.(in Chinese with English abstract)
[6]权龙哲,张冬冬,查绍辉,等.三臂多功能棚室农业机器人的运动学分析及试验[J].农业工程学报,2015,31(13):32-38.Quan Longzhe,Zhang Dongdong,Zha Shaohui,et al.Kinematics analysis and experiment of multifunctional agricultural robot in greenhouse with three arms[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2015,31(13):32-38.(in Chinese with English abstract)
[7]张俊雄,曹峥勇,耿长兴,等.温室精准对靶喷雾机器人研制[J].农业工程学报,2009,25(增刊2):70-73.Zhang Junxiong,Cao Zhengyong,Geng Changxing,et al.Research on precision target spray robot in greenhouse[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2009,25(Supp.2):70-73.(in Chinese with English abstract)
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[10]刘继展,刘炜,毛罕平,等.面向立柱栽培的机器人移栽苗序与路径分析[J].农业工程学报,2014,30(5):28-35.Liu Jizhan,Liu Wei,Mao Hanping,et al.Preparation and path analysis of robot transplantation for column cultivation[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2014,30(5):28-35(in Chinese with English abstract)
[11]权龙哲,申静朝,奚德君,等.狭闭空间内苗盘物流化搬运机器人运动规划与试验[J].农业机械学报,2016,47(1):51-59.Quan Longzhe,Shen Jingzhao,Xi Dejun,et al.Motion planning and test of robot for seedling tray handling in narrow space[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(1):51-59.(in Chinese with English abstract)
[12]权龙哲,李成林,冯正阳,等.体感操控多臂棚室机器人作业决策规划算法研究[J].农业机械学报,2017,48(3):1-12.Quan Longzhe,LI Chenglin,Feng Zhengyang,et al.Study on decision-making algorithm for robot operation in somersault control[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(3):1-12.(in Chinese with English abstract)
[13]王燕,杨庆华,鲍官军,等.关节型果蔬采摘机械臂优化设计与试验[J].农业机械学报,2011,42(7):191-195.Wang Yan,Yang Qinghua,Bao Guanjun,et al.Ptimization design and experiment of fruit vegetable picking manipulator[J].Transactions of the Chinese Society for Agricultural Machinery,2011,42(7):191-195.(in Chinese with English abstract)
[14]宋健,孙学岩,张铁中,等.开放式茄子采摘机器人设计与试验[J].农业机械学报,2009,40(1):143-147.Song Jian,Sun Xueyan,Zhang Tiezhong,et al.Design and experiment of opening picking robot for eggplant[J].Transactions of the Chinese Society of Agricultural Machinery,2009,40(1):143-147.(in Chinese with English abstract)
[15]李伟,李吉,张俊雄,等.苹果采摘机器人机械臂优化设计及仿真[J].北京工业大学学报,2009,35(6):721-726.Li Wei,Li Ji,Zhang Junxiong,et al.Optimization design and simulation of the apple-picking-robot arm[J].Journal of Beijing University of Technology,2009,35(6):721-726.(in Chinese with English abstract)
[16]樊炳辉,逄振旭.一种机器人大臂结构的优化设计[J].机器人,1995,17(6):325-331.Fan Binghui,Pang Zhenxu.Optimization design of a robot arm structure[J].Robot,1995,17(6):325-331.(in Chinese with English abstract)
[17]何春燕,何允纪,浦纪寿.HP99型堆垛机器人结构参数的优化设计[J].江苏理工大学学报:自然科学版,2000,21(3):42-45.He Chunyan,He Yunji,Pu Jishou.Optimization of physical dimension of HP99 stacker robot[J].Journal of Jiangsu University of Science and Technology:Natural Science,2000,21(3):42-45.(in Chinese with English abstract)
[18]丁渊明,王宣银.串联机械臂结构优化方法[J].浙江大学学报:工学版,2010,44(12):2360-2364.Ding Yuanming,Wang Xuanyin.Optimization method of serial manipulator structure[J].Journal of Zhejiang University:Engineering Science,2010,44(12):2360-2364.(in Chinese with English abstract)
[19]Lan P,Liu M,Lu N,et al.Optimal design of a novel high speed and high precision 3-DOF manipulator[C]//IEEEInternational Conference on Mechatronics,2005:689-694.
[20]Liu H,Huang T,Mei J,et al.Kinematic design of a 5-DOFhybrid robot with large workspace/limb-stroke ratio[J].Journal of Mechanical Design,2007,129(5):530-537.
[21]Hwang Y K,Yoon J W,Ryu J H.The optimum design of a6-DOF parallel manipulator with large orientation workspace[C]//IEEE International Conference on Robotics&Automation,IEEE,2007:163-168.
[22]蔡自兴.机器人学[M].北京:清华大学出版社,2009.
[23]高文斌,王洪光,姜勇,等.基于距离误差的机器人运动学参数标定方法[J].机器人,2013(5):600-606.Gao Wenbin,Wang Hongguang,Jiang Yong,et al.Method for Kinematic Parameter Calibration of Robot Based on Distance Error[J].Robot,2013(5):600-606.(in Chinese with English abstract)
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