智能移动式水果采摘机器人系统的研究
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摘要
水果采摘机器人对减轻果农劳动强度,保证果实及时采收,保证果品质量,提高我国果园自动化作业水平等方面具有非常重要的意义。本研究在国家“863计划一移动式果树采摘机器人关键技术研究”项目资助下,综合运用机械电子、图像处理、数据通信、计算机网络及智能控制等技术对智能移动式水果采摘机器人的关键技术进行了研究。主要研究内容及研究成果总结如下：
(1)在对标准矮化苹果果园进行实地调研的基础上,确定了采摘机器人基本设计参数。通过对现有四轮电动车底盘和履带底盘的分析比较,发现现有底盘很难满足设计要求。为此,自主设计了适用于果园作业的轻型履带式智能移动平台,并在智能移动平台上集成了采摘机械臂及其控制系统、末端执行器及其控制系统、智能移动平台驱动控制系统、DGPS、双目摄像机等设备,并以工业PC为主控制器构建了整个采摘机器人的运动控制系统。最终独立完成了水果采摘机器人样机的自主研制。
(2)搭建了水果采摘机器人的视觉系统,开发了果实识别和定位的图像处理软件。在RGB、归一化rgb、HIS、Lab、YIQ和Ⅰ1Ⅰ2Ⅰ3颜色空间下对多幅苹果、树叶、树枝的图像进行分析,通过比较多种色差模型的灰度图像,找出果实与枝叶图像差异较为明显的色差模型,并对可用色差模型的灰度图像进行了图像处理的实时性和实际分割效果的对比分析,结果表明,利用RGB颜色空间中的R-G色差分量并采用自适应阈值分割算法能获得理想的分割效果。最后对分割后的图像采用圆形Hough变换与质心标记相结合的方法检测果实中心点坐标,试验表明该方法的果实识别率比单用圆形Hough变换的果实识别率能够提高8.5%。
(3)提取了视觉导航和DGPS导航的控制参数,开发了基于机器视觉和DGPS的智能移动平台导航控制系统。在进行视觉导航参数提取时,采用直线Hough变换与最小二乘法相结合的方法检测导航路径,该方法能够融合Hough变换检测出的直线附近的近距离点集来拟合导航路径,试验表明该方法比单用Hough变换检测导航路径的误检率降低约30%。
(4)对采摘机械臂和智能移动平台进行了运动学分析和求解,提出了基于“最短行程”和“最低能耗”为准则的采摘机械臂最优逆解求取算法。另外,还对采摘机械臂和智能移动平台的轨迹规划进行了研究。
(5)搭建了水果采摘机器人远程视频监控系统。远程视频监控系统主要功能有以下几个方面：第一,能够实时观测到采摘机器人果园现场作业状况和周围工作环境；第二,当采摘机器人遇到突发状况时监控者可通过远程监控客户端向采摘机器人发出远程控制指令来实时控制各个运动模块动作,以保证采摘机器人作业的安全性；第三,采摘机器人能够对其自身周围环境进行监测,一旦有人或者障碍物接近机器人,采摘机器人主控制器便会在现场和向远程监控客户端同时发出警报。
(6)通过校内试验和果园综合试验对采摘机器人的作业性能进行了验证。试验表明：采摘机器人能够实现智能移动平台导航、采摘机械臂运动、末端执行器果实抓取及水果自动装箱等关键动作的智能协调控制,各种算法的实时性和鲁棒性能够满足采摘机器人的作业需要。
Fruit picking robot has very important significance to reduce labour intensity, to ensure harvesting in time and to pretect fruit quality. It also can increase automation levels in orchard operations. Funded by "863Project-study on key technologies for mobile fruit picking robot", key technologies for mobile fruit picking robot were studied by means of integrating with mechanics&electronics, image processing, data communication, computer networks, intelligent control system etc. The main contents and results of the research are summarized as below:
1. By the survey of the standard dwarf orchard, the basic design parameters of the picking robot were determined. Compared with the chassis of the four-wheeler electric vehicle and crawler, it was fund that the existing chassis are hard to meet the design requirements. Consequently, a light tracked intelligent mobile platform was designed independently which was found suitable for orchards operation. The platform is integrated with the picking arm and its control system, end-effecter along with its control system, the drive system of the mobile platform, DGPS and binocular camera etc. With industrial PC as the main controller, the entire picking robot motion control system was built and the prototype was independently developed.
2. The vision system of the fruit picking robot was built and the image processing software was developed to recognize and locate the fruits. Under the color space of RGB, normalized rgb, HIS, Lab, YIQ and Ⅰ1Ⅰ2Ⅰ3, numerous pictures of apple fruits, leaves and branches were analyzed. To find out the aberration model whose images of fruits, leaves and branches are obviously different, gray images of a variety of aberration model were extracted. The real time character and the effect of actual segmentation of processing of the gray images extracted from the aberration model were analyzed. The result showed that the ideal segmentation effect of the grays images of the R-G aberration model can be acquired with taking adaptive threshold segmentation algorithm. Eventually the method which combined circular Hough transform with mark of the center was adopted to detect the coordinate of the fruit central point. Compared with utilizing the circular Hough transform Alone, the algorithm proved that recognition rate of fruit can be increased by8.5%.
3. The collection of the navigation parameters based on machine vision and DGPS were completed. The software and interactive interface of the intelligent mobile navigation system was developed. While collecting the vision navigation parameters, linear Hough transform and least square method were combined to detect the navigation path. Research proved that the method was able to combine the close point set of the straight line detected by Hough transform to fit the navigation path. Compared with utilizing the Hough transform alone, The algorithm proved that the false detection rate was reduced by30%.
4. The kinematics analysis and the resolution of picking arm and intelligent mobile platform were carried out, the optimized arithmetic of the picking arm based on "the shortest travel" and "the least energy consume" are raised. Besides, the trajectory planning of the picking arm and intelligent mobile platform were researched in this paper as well.
5. The remote video monitoring system of fruit picking robot is built and the functions of this system are as follows:Firstly, it can inspect both situations of the picking robots' work states and the nearby environment; Secondly, for the purpose of controlling each module's action, the remote control command are sent to the picking robot via the remote monitoring client by the inspector when the robot meets the gusty situation; Thirdly, the picking robot can inspect the nearby environment by itself, once the robot approached by humans or obstacles, the alarm will be sent to both the site and the remote monitoring client by the main controller of the picking robot.
6. Through the experiments in campus and integrated tests in the orchard, the operation performance of the picking robot was validated.The results showed that the mobile platform's navigation, picking arm's movement, end-effecter's grasp and fruit automatic encasement can operate coordinately,and all the related algorithms satisfied real-time and reliability.
(1)在对标准矮化苹果果园进行实地调研的基础上,确定了采摘机器人基本设计参数。通过对现有四轮电动车底盘和履带底盘的分析比较,发现现有底盘很难满足设计要求。为此,自主设计了适用于果园作业的轻型履带式智能移动平台,并在智能移动平台上集成了采摘机械臂及其控制系统、末端执行器及其控制系统、智能移动平台驱动控制系统、DGPS、双目摄像机等设备,并以工业PC为主控制器构建了整个采摘机器人的运动控制系统。最终独立完成了水果采摘机器人样机的自主研制。
(2)搭建了水果采摘机器人的视觉系统,开发了果实识别和定位的图像处理软件。在RGB、归一化rgb、HIS、Lab、YIQ和Ⅰ1Ⅰ2Ⅰ3颜色空间下对多幅苹果、树叶、树枝的图像进行分析,通过比较多种色差模型的灰度图像,找出果实与枝叶图像差异较为明显的色差模型,并对可用色差模型的灰度图像进行了图像处理的实时性和实际分割效果的对比分析,结果表明,利用RGB颜色空间中的R-G色差分量并采用自适应阈值分割算法能获得理想的分割效果。最后对分割后的图像采用圆形Hough变换与质心标记相结合的方法检测果实中心点坐标,试验表明该方法的果实识别率比单用圆形Hough变换的果实识别率能够提高8.5%。
(3)提取了视觉导航和DGPS导航的控制参数,开发了基于机器视觉和DGPS的智能移动平台导航控制系统。在进行视觉导航参数提取时,采用直线Hough变换与最小二乘法相结合的方法检测导航路径,该方法能够融合Hough变换检测出的直线附近的近距离点集来拟合导航路径,试验表明该方法比单用Hough变换检测导航路径的误检率降低约30%。
(4)对采摘机械臂和智能移动平台进行了运动学分析和求解,提出了基于“最短行程”和“最低能耗”为准则的采摘机械臂最优逆解求取算法。另外,还对采摘机械臂和智能移动平台的轨迹规划进行了研究。
(5)搭建了水果采摘机器人远程视频监控系统。远程视频监控系统主要功能有以下几个方面：第一,能够实时观测到采摘机器人果园现场作业状况和周围工作环境；第二,当采摘机器人遇到突发状况时监控者可通过远程监控客户端向采摘机器人发出远程控制指令来实时控制各个运动模块动作,以保证采摘机器人作业的安全性；第三,采摘机器人能够对其自身周围环境进行监测,一旦有人或者障碍物接近机器人,采摘机器人主控制器便会在现场和向远程监控客户端同时发出警报。
(6)通过校内试验和果园综合试验对采摘机器人的作业性能进行了验证。试验表明：采摘机器人能够实现智能移动平台导航、采摘机械臂运动、末端执行器果实抓取及水果自动装箱等关键动作的智能协调控制,各种算法的实时性和鲁棒性能够满足采摘机器人的作业需要。
Fruit picking robot has very important significance to reduce labour intensity, to ensure harvesting in time and to pretect fruit quality. It also can increase automation levels in orchard operations. Funded by "863Project-study on key technologies for mobile fruit picking robot", key technologies for mobile fruit picking robot were studied by means of integrating with mechanics&electronics, image processing, data communication, computer networks, intelligent control system etc. The main contents and results of the research are summarized as below:
1. By the survey of the standard dwarf orchard, the basic design parameters of the picking robot were determined. Compared with the chassis of the four-wheeler electric vehicle and crawler, it was fund that the existing chassis are hard to meet the design requirements. Consequently, a light tracked intelligent mobile platform was designed independently which was found suitable for orchards operation. The platform is integrated with the picking arm and its control system, end-effecter along with its control system, the drive system of the mobile platform, DGPS and binocular camera etc. With industrial PC as the main controller, the entire picking robot motion control system was built and the prototype was independently developed.
2. The vision system of the fruit picking robot was built and the image processing software was developed to recognize and locate the fruits. Under the color space of RGB, normalized rgb, HIS, Lab, YIQ and Ⅰ1Ⅰ2Ⅰ3, numerous pictures of apple fruits, leaves and branches were analyzed. To find out the aberration model whose images of fruits, leaves and branches are obviously different, gray images of a variety of aberration model were extracted. The real time character and the effect of actual segmentation of processing of the gray images extracted from the aberration model were analyzed. The result showed that the ideal segmentation effect of the grays images of the R-G aberration model can be acquired with taking adaptive threshold segmentation algorithm. Eventually the method which combined circular Hough transform with mark of the center was adopted to detect the coordinate of the fruit central point. Compared with utilizing the circular Hough transform Alone, the algorithm proved that recognition rate of fruit can be increased by8.5%.
3. The collection of the navigation parameters based on machine vision and DGPS were completed. The software and interactive interface of the intelligent mobile navigation system was developed. While collecting the vision navigation parameters, linear Hough transform and least square method were combined to detect the navigation path. Research proved that the method was able to combine the close point set of the straight line detected by Hough transform to fit the navigation path. Compared with utilizing the Hough transform alone, The algorithm proved that the false detection rate was reduced by30%.
4. The kinematics analysis and the resolution of picking arm and intelligent mobile platform were carried out, the optimized arithmetic of the picking arm based on "the shortest travel" and "the least energy consume" are raised. Besides, the trajectory planning of the picking arm and intelligent mobile platform were researched in this paper as well.
5. The remote video monitoring system of fruit picking robot is built and the functions of this system are as follows:Firstly, it can inspect both situations of the picking robots' work states and the nearby environment; Secondly, for the purpose of controlling each module's action, the remote control command are sent to the picking robot via the remote monitoring client by the inspector when the robot meets the gusty situation; Thirdly, the picking robot can inspect the nearby environment by itself, once the robot approached by humans or obstacles, the alarm will be sent to both the site and the remote monitoring client by the main controller of the picking robot.
6. Through the experiments in campus and integrated tests in the orchard, the operation performance of the picking robot was validated.The results showed that the mobile platform's navigation, picking arm's movement, end-effecter's grasp and fruit automatic encasement can operate coordinately,and all the related algorithms satisfied real-time and reliability.
引文
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