基于两轴直线驱动的仿生视觉平台研究
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摘要
根据自主视觉导引机器人(如无人驾驶、自动导航、机器手作业、空间探测等)在复杂场景下因颠簸、振动、坡度变化、离焦和目标快速移动造成成像不理想甚至失去对目标的跟踪问题,本文提出了一种自主机器人仿生视觉平台,该平台仿照人眼运动机理,通过两轴直线电机驱动模型可对目标物体进行随动跟踪。
本文首先介绍了视觉平台的整体设计思路,并提出了整套视觉平台的机械运动结构设计。在此基础上,全文分别从数据输入阶段(模式识别与目标跟踪),中间处理阶段(位姿调整策略)以及运动输出阶段(直线电机运动控制)加以展开,详细讨论了各阶段中的处理流程及系统结构。
在模式识别与目标跟踪阶段,本文首先通过常用的图像预处理方法获取质量较好的图像信息,并通过图像锐化,图像分割,边缘追踪等一系列处理过程获取目标的特征信息;之后利用kalman滤波器完成对目标物体的跟踪预测。在此过程中,本文比较了不同处理算法的优劣,并在matlab中进行了大量的实验仿真。
通过对目标运动预测以及特征匹配,目标在视野坐标系下的位置信息将作为输入传递到位姿调整策略中,本文进而对视觉平台进行了运动学建模,建立了目标位置与电机位移的对应关系。最后,本文分析了直线电机的运动控制过程,并分别采用速度环和位置环对输出情况进行了仿真。
除此以外,本文还设立了一些干扰场景来检验视觉平台的工作性能,从仿真结果来看,直线电机高响应性特点不仅可以达到预期的追踪效果,而且有能力应对外界的干扰。本研究为自主机器人智能追踪系统提供关键基础部件的设计原理和方法。
This paper intends to present a stable imaging platform for the visual system of autonomous robot, aiming at improving the non-optimal quality of image which is caused by tremor, vibration, slope alteration as well as rapid movement of the target during the moving process in intricate circumstances. According to the mechanism of human optical system, a bionic imaging platform driven by multi-axis linear motors is given, in which the function of swift tracing is integrated.
This paper gives both the general design and the construction of the visual platform at the beginning, and then it expands to describe three important processing stages in details including data input stage(pattern recognition and target tracking),intermediate stage(attitude adjustment strategy) as well as motion output stage(motion control of linear motor).
During the phase of pattern recognition and target tracking, some preprocessing methods are used to achieve high-quality images, and then the characters of target can be extracted from those images by image sharpening, image segmentation and edge tracing. After the procedures mentioned above, we utilize kalman filter to predict and trace target and furthermore verify the validity of these processes in matlab.
According to the result of recognition and prediction, the target positions can be transferred as inputs to the attitude adjustment strategy, generating the relations between target position and motor displacement by kinematics models of the platform. And finally, this paper analyzes the motion control process of linear motors and completes the simulation of velocity loop and position loop.
Moreover, we set up some interference to verify the performance of this visual platform. From the simulation, it is obvious to see that a good tracking effect and capacities of anti-interference can be available due to the high response of linear motor.
This research also contributes fundamental approaches to the framework designs of the visual system of autonomous robot.
本文首先介绍了视觉平台的整体设计思路,并提出了整套视觉平台的机械运动结构设计。在此基础上,全文分别从数据输入阶段(模式识别与目标跟踪),中间处理阶段(位姿调整策略)以及运动输出阶段(直线电机运动控制)加以展开,详细讨论了各阶段中的处理流程及系统结构。
在模式识别与目标跟踪阶段,本文首先通过常用的图像预处理方法获取质量较好的图像信息,并通过图像锐化,图像分割,边缘追踪等一系列处理过程获取目标的特征信息;之后利用kalman滤波器完成对目标物体的跟踪预测。在此过程中,本文比较了不同处理算法的优劣,并在matlab中进行了大量的实验仿真。
通过对目标运动预测以及特征匹配,目标在视野坐标系下的位置信息将作为输入传递到位姿调整策略中,本文进而对视觉平台进行了运动学建模,建立了目标位置与电机位移的对应关系。最后,本文分析了直线电机的运动控制过程,并分别采用速度环和位置环对输出情况进行了仿真。
除此以外,本文还设立了一些干扰场景来检验视觉平台的工作性能,从仿真结果来看,直线电机高响应性特点不仅可以达到预期的追踪效果,而且有能力应对外界的干扰。本研究为自主机器人智能追踪系统提供关键基础部件的设计原理和方法。
This paper intends to present a stable imaging platform for the visual system of autonomous robot, aiming at improving the non-optimal quality of image which is caused by tremor, vibration, slope alteration as well as rapid movement of the target during the moving process in intricate circumstances. According to the mechanism of human optical system, a bionic imaging platform driven by multi-axis linear motors is given, in which the function of swift tracing is integrated.
This paper gives both the general design and the construction of the visual platform at the beginning, and then it expands to describe three important processing stages in details including data input stage(pattern recognition and target tracking),intermediate stage(attitude adjustment strategy) as well as motion output stage(motion control of linear motor).
During the phase of pattern recognition and target tracking, some preprocessing methods are used to achieve high-quality images, and then the characters of target can be extracted from those images by image sharpening, image segmentation and edge tracing. After the procedures mentioned above, we utilize kalman filter to predict and trace target and furthermore verify the validity of these processes in matlab.
According to the result of recognition and prediction, the target positions can be transferred as inputs to the attitude adjustment strategy, generating the relations between target position and motor displacement by kinematics models of the platform. And finally, this paper analyzes the motion control process of linear motors and completes the simulation of velocity loop and position loop.
Moreover, we set up some interference to verify the performance of this visual platform. From the simulation, it is obvious to see that a good tracking effect and capacities of anti-interference can be available due to the high response of linear motor.
This research also contributes fundamental approaches to the framework designs of the visual system of autonomous robot.
引文
[1].邹海荣,龚振邦,罗均.仿生眼研究现状与发展趋势.机器人2005.09 25(6):469-474
[2].李恒宇,罗均,夏冰玉.机器人仿生双眼的运动控制系统建模与仿真.电子机械工程2008.08 24(2): 60-64
[3]. Christian Siagian, Member, IEEE, and Laurent Itti, Member, IEEE Biologically Inspired Mobile Robot Vision Localization IEEE TRANSACTIONS ON ROBOTICS, 25(4), 324-326,2009
[4].王宣银向桂山富晓杰.并联气动人工肌肉驱动的仿生眼睛. ZL:200720106951.2 2007.3
[5].丛爽刘宜.多轴协调运动中的交叉耦合控制.机械设计与制造. 2006,10:166-168.
[6].李团结苏理张琰.直线电机驱动的全向滚动球形机器人的设计与分析.机械设计与研究.2006,08 37(11): 46-48,52
[7].郭吉丰,傅平.多自由度球形超声波电机的研究进展.电工电能新技术.2005.04 42(1):65-68
[8]. Ofir Avni, Francesco Borrelli, Gadi Katzir, Ehud Rivlin and Hector Rotstein. Using Dynamic Optimization for Reproducing the Chameleon Visual System. Proceedings of the 45th IEEE Conference on Decision & Control.Manchester Grand Hyatt Hotel San Diego, CA, USA, 26(3):13-15, 2006
[9]. Ofir Avni, Francesco Borrelli, Gad Katzir, Ehud Rivlin and Hector Rotstein. Scanning the Environment with Two Independent Cameras - Biologically Motivated Approach. Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems 24(2) 9 - 15, 2006, Beijing, China
[10].杜鑫峰,熊蓉,褚健.仿人足球机器人视觉系统快速识别与精确定位.浙江工业大学学报2009. 43(11):1975-1981
[11].李志成,谢娟,周先国.一种改进的移动机器人视觉跟踪算法研究.佳木斯大学学报(自然科学版)2009 27(2):203-205
[12]. Lin Rui, Du Zhijiang, Sun Lining Moving Object Tracking based on Mobile Robot Vision. Proeeedings ofthe 2009 IEEE International Con ference on Meehatronies and Automation 21(6):9–12, Changehun, China
[13].陈应松肖世德林静等自主导航移动机器人视觉系统的一种标定方法.计算机工程与应用.2009,45(23):190-192 199
[14]. Tao Wang, Nanning. Zheng, Kuizhi Mei. A Visual Brain Chip Based on Selective Attention for Robot Vision Application. Third IEEE International Conference on SpaceMission Challenges for Information Technology 22(1):4-8 2000.
[15]. Stephan Hussmann, Thorsten Liepert Three-Dimensional TOF Robot Vision System. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 58(1):135-139 2009.
[16].赵洪刚,欧阳富等.对空间机构自由度计算公式的探索[J].北华大学学报2003.10 2(2): 439-442.
[17].石玉秋,孙炜,孙洪淋.一种三关节机器人视觉伺服系统研究.装备制造技术. 2006.02 25(2):12-15
[18].李国栋,周友行,王延伟,邓胜达,唐稳庄.基于无标定视觉伺服机器人的运动物体跟踪.机械研究与应用. 2007.02 17(3):50-52
[19].张国亮,王捷,刘宏.大范围视觉伺服方法在空间机器人上的应用.西安交通大学学报. 2009.01:85-89
[20].叶云岳著.直线电机原理与应用.北京:机械工业出版社,2000.
[21]. Sun Hua ; Yuehong Dai. Fuzzy PID Control and Simulation Experiment on Permanent Magnet Linear Synchronous Motors. Electrical and Control Engineering (ICECE), 2010 International Conference.12(11):1047-1049,2010
[22].叶云岳,陆凯元.直线电机的PID控制与模糊控制.电工技术学报. 2001.06 7(8): 11-15.
[23].翁秀华;郭庆鼎;刘德君.基于模糊自适应PID的双直线电机同步驱动系统控制.组合机床自动化加工技术. 2004.09 15(3):28-29
[24].余成波著.数字图像处理及MATLAB实现.重庆:重庆大学出版社,2003
[25]. Chin-Chen Chang; Ju-Yuan Hsiao; Chih-Ping Hsieh. An Adaptive Median Filter for Image Denoising. Intelligent Information Technology Application, 2008. IITA '08. Second International Symposium.12(7):346-350,2008.
[26]. Li Zhongshen. Design and Analysis of Improved Butterworth Low Pass Filter. Electronic Measurement and Instruments, 2007. ICEMI '07. 8th International Conference.11(1):729-732,2007.
[27]. Sapna Varshney, S. ; Rajpal, N. ; Purwar, R. Comparative study of image segmentation techniques and object matching using segmentation. Methods and Models in Computer Science, 2009. ICM2CS 2009. Proceeding of International Conference. 1-6,2009.
[28].高成敏.计算图像左右边界算法.福建电脑2008.08 12(6): 11-14
[29]. Konstantinides, K. ; Bhaskaran, V. ; Beretta, G. Image sharpening in the JPEG domain. Image Processing, IEEE Transactions.15(9):874-878,1999
[30]. Shiping Zhu ; Xi Xia ; Qingrong Zhang ; Belloulata, K. An Image SegmentationAlgorithm in Image Processing Based on Threshold Segmentation. Signal-Image Technologies and Internet-Based System, 2007. SITIS '07. Third International IEEE Conference. 32(7):673-678, 2007.
[31]. Nae-Joung Kwak ; Dong-Jin Kwon ; Young-Gil Kim ; Jae-Hyeong Ahn. Color image segmentation using edge and adaptive threshold value based on the image characteristics. Intelligent Signal Processing and Communication Systems, 2004. ISPACS 2004. Proceedings of 2004 International Symposium. 555-558, 2004.
[32]. Wenshuo Gao ; Xiaoguang Zhang ; Lei Yang ; Huizhong Liu. An improved Sobel edge detection. Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on.18(4):67-72, 2010
[33]. Bing Wang ; ShaoSheng Fan. An Improved CANNY Edge Detection Algorithm. Computer Science and Engineering, 2009. WCSE '09. Second International Workshop.497-500,2010
[34].王福生,齐国清.二值图像中目标物体轮廓的边界跟踪算法.大连海事大学学报2006.02 32(1): 62-67
[35].郭健,刘斌,赖海燕.基于二值图像的目标边界跟踪算法及应用.红外技术2005.09 27(5): 416-418
[36]. Talu, M.F. ; Turkoglu, I. A novel object recognition method based on improved edge tracing for binary images. Application of Information and Communication Technologies, 2009. AICT 2009. International Conference. 1-5, 2009
[37].丁一.低对比度下的目标跟踪算法研究及系统实现.北京工业大学硕士学位论文. 2007.06 p35-39.
[38].王宇,程耀瑜.基于Kalman滤波原理的运动目标跟踪.信息技术. 2008.10 28(5): 48-50,54
[39].关晓惠,周志敏,钱亚冠. Kalman滤波在视频监控中的应用.浙江水利水电学院学报2007.06 19(2): 78-80
[40]. Jong-Yun Kim ; Tae-Yong Kim. Soccer Ball Tracking Using Dynamic Kalman Filter with Velocity Control. Computer Graphics, Imaging and Visualization, 2009. CGIV '09. Sixth International Conference.22(3):367-374,2009.10
[41]. St-Pierre, M. ; Gingras, D. Comparison between the unscented Kalman filter and the extended Kalman filter for the position estimation module of an integrated navigation information system. Intelligent Vehicles Symposium, 2004 IEEE.831-835,2004.10
[42]. Blanco, E. ; Ondel, O. ; Llor, A. Using linear interpolation and Kalman prediction in Pattern Recognition: Application to an induction machine. Diagnostics for ElectricMachines, Power Electronics and Drives, 2005. SDEMPED 2005. 5th IEEE International Symposium 17(8):1-6,2005
[2].李恒宇,罗均,夏冰玉.机器人仿生双眼的运动控制系统建模与仿真.电子机械工程2008.08 24(2): 60-64
[3]. Christian Siagian, Member, IEEE, and Laurent Itti, Member, IEEE Biologically Inspired Mobile Robot Vision Localization IEEE TRANSACTIONS ON ROBOTICS, 25(4), 324-326,2009
[4].王宣银向桂山富晓杰.并联气动人工肌肉驱动的仿生眼睛. ZL:200720106951.2 2007.3
[5].丛爽刘宜.多轴协调运动中的交叉耦合控制.机械设计与制造. 2006,10:166-168.
[6].李团结苏理张琰.直线电机驱动的全向滚动球形机器人的设计与分析.机械设计与研究.2006,08 37(11): 46-48,52
[7].郭吉丰,傅平.多自由度球形超声波电机的研究进展.电工电能新技术.2005.04 42(1):65-68
[8]. Ofir Avni, Francesco Borrelli, Gadi Katzir, Ehud Rivlin and Hector Rotstein. Using Dynamic Optimization for Reproducing the Chameleon Visual System. Proceedings of the 45th IEEE Conference on Decision & Control.Manchester Grand Hyatt Hotel San Diego, CA, USA, 26(3):13-15, 2006
[9]. Ofir Avni, Francesco Borrelli, Gad Katzir, Ehud Rivlin and Hector Rotstein. Scanning the Environment with Two Independent Cameras - Biologically Motivated Approach. Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems 24(2) 9 - 15, 2006, Beijing, China
[10].杜鑫峰,熊蓉,褚健.仿人足球机器人视觉系统快速识别与精确定位.浙江工业大学学报2009. 43(11):1975-1981
[11].李志成,谢娟,周先国.一种改进的移动机器人视觉跟踪算法研究.佳木斯大学学报(自然科学版)2009 27(2):203-205
[12]. Lin Rui, Du Zhijiang, Sun Lining Moving Object Tracking based on Mobile Robot Vision. Proeeedings ofthe 2009 IEEE International Con ference on Meehatronies and Automation 21(6):9–12, Changehun, China
[13].陈应松肖世德林静等自主导航移动机器人视觉系统的一种标定方法.计算机工程与应用.2009,45(23):190-192 199
[14]. Tao Wang, Nanning. Zheng, Kuizhi Mei. A Visual Brain Chip Based on Selective Attention for Robot Vision Application. Third IEEE International Conference on SpaceMission Challenges for Information Technology 22(1):4-8 2000.
[15]. Stephan Hussmann, Thorsten Liepert Three-Dimensional TOF Robot Vision System. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 58(1):135-139 2009.
[16].赵洪刚,欧阳富等.对空间机构自由度计算公式的探索[J].北华大学学报2003.10 2(2): 439-442.
[17].石玉秋,孙炜,孙洪淋.一种三关节机器人视觉伺服系统研究.装备制造技术. 2006.02 25(2):12-15
[18].李国栋,周友行,王延伟,邓胜达,唐稳庄.基于无标定视觉伺服机器人的运动物体跟踪.机械研究与应用. 2007.02 17(3):50-52
[19].张国亮,王捷,刘宏.大范围视觉伺服方法在空间机器人上的应用.西安交通大学学报. 2009.01:85-89
[20].叶云岳著.直线电机原理与应用.北京:机械工业出版社,2000.
[21]. Sun Hua ; Yuehong Dai. Fuzzy PID Control and Simulation Experiment on Permanent Magnet Linear Synchronous Motors. Electrical and Control Engineering (ICECE), 2010 International Conference.12(11):1047-1049,2010
[22].叶云岳,陆凯元.直线电机的PID控制与模糊控制.电工技术学报. 2001.06 7(8): 11-15.
[23].翁秀华;郭庆鼎;刘德君.基于模糊自适应PID的双直线电机同步驱动系统控制.组合机床自动化加工技术. 2004.09 15(3):28-29
[24].余成波著.数字图像处理及MATLAB实现.重庆:重庆大学出版社,2003
[25]. Chin-Chen Chang; Ju-Yuan Hsiao; Chih-Ping Hsieh. An Adaptive Median Filter for Image Denoising. Intelligent Information Technology Application, 2008. IITA '08. Second International Symposium.12(7):346-350,2008.
[26]. Li Zhongshen. Design and Analysis of Improved Butterworth Low Pass Filter. Electronic Measurement and Instruments, 2007. ICEMI '07. 8th International Conference.11(1):729-732,2007.
[27]. Sapna Varshney, S. ; Rajpal, N. ; Purwar, R. Comparative study of image segmentation techniques and object matching using segmentation. Methods and Models in Computer Science, 2009. ICM2CS 2009. Proceeding of International Conference. 1-6,2009.
[28].高成敏.计算图像左右边界算法.福建电脑2008.08 12(6): 11-14
[29]. Konstantinides, K. ; Bhaskaran, V. ; Beretta, G. Image sharpening in the JPEG domain. Image Processing, IEEE Transactions.15(9):874-878,1999
[30]. Shiping Zhu ; Xi Xia ; Qingrong Zhang ; Belloulata, K. An Image SegmentationAlgorithm in Image Processing Based on Threshold Segmentation. Signal-Image Technologies and Internet-Based System, 2007. SITIS '07. Third International IEEE Conference. 32(7):673-678, 2007.
[31]. Nae-Joung Kwak ; Dong-Jin Kwon ; Young-Gil Kim ; Jae-Hyeong Ahn. Color image segmentation using edge and adaptive threshold value based on the image characteristics. Intelligent Signal Processing and Communication Systems, 2004. ISPACS 2004. Proceedings of 2004 International Symposium. 555-558, 2004.
[32]. Wenshuo Gao ; Xiaoguang Zhang ; Lei Yang ; Huizhong Liu. An improved Sobel edge detection. Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on.18(4):67-72, 2010
[33]. Bing Wang ; ShaoSheng Fan. An Improved CANNY Edge Detection Algorithm. Computer Science and Engineering, 2009. WCSE '09. Second International Workshop.497-500,2010
[34].王福生,齐国清.二值图像中目标物体轮廓的边界跟踪算法.大连海事大学学报2006.02 32(1): 62-67
[35].郭健,刘斌,赖海燕.基于二值图像的目标边界跟踪算法及应用.红外技术2005.09 27(5): 416-418
[36]. Talu, M.F. ; Turkoglu, I. A novel object recognition method based on improved edge tracing for binary images. Application of Information and Communication Technologies, 2009. AICT 2009. International Conference. 1-5, 2009
[37].丁一.低对比度下的目标跟踪算法研究及系统实现.北京工业大学硕士学位论文. 2007.06 p35-39.
[38].王宇,程耀瑜.基于Kalman滤波原理的运动目标跟踪.信息技术. 2008.10 28(5): 48-50,54
[39].关晓惠,周志敏,钱亚冠. Kalman滤波在视频监控中的应用.浙江水利水电学院学报2007.06 19(2): 78-80
[40]. Jong-Yun Kim ; Tae-Yong Kim. Soccer Ball Tracking Using Dynamic Kalman Filter with Velocity Control. Computer Graphics, Imaging and Visualization, 2009. CGIV '09. Sixth International Conference.22(3):367-374,2009.10
[41]. St-Pierre, M. ; Gingras, D. Comparison between the unscented Kalman filter and the extended Kalman filter for the position estimation module of an integrated navigation information system. Intelligent Vehicles Symposium, 2004 IEEE.831-835,2004.10
[42]. Blanco, E. ; Ondel, O. ; Llor, A. Using linear interpolation and Kalman prediction in Pattern Recognition: Application to an induction machine. Diagnostics for ElectricMachines, Power Electronics and Drives, 2005. SDEMPED 2005. 5th IEEE International Symposium 17(8):1-6,2005