权龙哲,李成林,冯正阳,刘佳伟.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):14-23.
QUAN Longzhe,LI Chenglin,FENG Zhengyang,LIU Jiawei.Algorithm of Works’ Decision for Three Arms Robot in Greenhouse Based on Control with Motion Sensing Technology[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(3):14-23.
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体感操控多臂棚室机器人作业决策规划算法研究   [下载全文]
Algorithm of Works’ Decision for Three Arms Robot in Greenhouse Based on Control with Motion Sensing Technology   [Download Pdf][in English]
投稿时间:2016-07-15  
DOI:10.6041/j.issn.1000-1298.2017.03.002
中文关键词:  多臂温室机器人  决策规划  体感技术  Kinect传感器
基金项目:黑龙江省普通高等学校青年创新人才培养计划项目(LR-356214)、国家自然科学基金项目(51405078)、黑龙江省博士后基金项目(LBH-Z13022)和东北农业大学“青年才俊”项目(518020)
作者单位
权龙哲 东北农业大学 
李成林 东北农业大学 
冯正阳 东北农业大学 
刘佳伟 东北农业大学 
中文摘要:针对目前棚室内机器人作业分析算法智能性不足、准确作业率较低,且一次巡航过程只能进行单一作业,存在使用效率不高的问题,提出了一种搭载在三臂棚室机器人上,基于体感操控作业的决策规划算法,用Kinect采集含操作人员位姿信息的深度图像,结合随机森林统计学习理论和基于高斯核函数的Mean shift算法,确定了代表人体位姿的20个关键骨骼点坐标,在此基础上提出了一种基于模式切换的三臂映射关系,将骨骼点信息映射到机器人工作空间,使人的两只手臂能自如的控制三臂机器人,在一次巡航中完成多种棚室作业;此外,还提出了一种结合骨骼追踪技术和YCbCr颜色空间的手势特征分割方法,实现了用手势控制机器人末端执行器作业。最后,搭建了用于测试体感决策算法的三臂机器人样机,进行了针对该决策算法的精确性试验,根据试验误差数据对肩部关节夹角采用离散化取值识别,解决了肩部关节识别误差,结果表明:测试者被捕捉到的关节处夹角和机器人对应关节夹角的最大映射误差为1.90°,上位机发送夹角值与机器人实际转动的夹角值最大误差为0.80°,在误差允许范围内,同时在该精度下完成一套采摘加喷施作业指令,平均耗时13.34s,且操作者还可通过体感操控训练进一步提高机器人作业性能,表明该算法具有准确性和实用性。
QUAN Longzhe  LI Chenglin  FENG Zhengyang  LIU Jiawei
Northeast Agricultural University,Northeast Agricultural University,Northeast Agricultural University and Northeast Agricultural University
Key Words:multi-arms robot in greenhouse  decision programming  motion sensing technology  Kinect sensor
Abstract:There are many problems in the current greenhouse and plant factory. It’s an effective solution to work by robots. However, because of the limit of the intelligent algorithm at present, the robot’s works are imprecise.And they can just complete single job in once cruise which cause the inefficient using. Aiming at the problem, an algorithm which based on the motion sensing of Kinect and used in three arms robot was proposed, by using the Kinect to collect the depth image, including the operating personnel and combining the Random forests of statistical learning theory with the mean shift algorithm based on the Gauss kernel function which acquired 20 skeletal joints that can standard the human motion. On this basis, a mapping relation was put forward in innovation of three arms based on the mode switching to achieve that the two arms of the man can freely control three arms of the robot and perform several works in greenhouse. In addition, a way of gesture features segmentation was proposed which based on skeletal tracking technology in Kinect and YCbCr color space, realizing the aim that using the action of the palm to control the robot’s end effectors. Finally, a prototype of three arms robot was built to test the decision algorithm of motion sensing and its accuracy. A discrete value was taken for the angle in shoulder joint to recognize the error data in experiment, so eliminated errors in the shoulder joints. The results showed that the maximum mapping error of joint angle was 1.90° between human and robot. The maximum error of the host computer sending angle and the real angle by robot was 0.80°, which was within the margin of error. Meanwhile, the average time of completing an order of picking and spraying was 13.34s, the picking time was 6.36s and the spraying time was 6.98s. And the performance of the robot can be boosted by training the manipulator. It was indicated that this algorithm had a great practicability and can work accurately.

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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