基于DSP的机器人视觉系统的设计与实现
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摘要
智能机器人在实际应用中首先需要解决的就是机器人对外界环境信息的感知问题,之后才能完成指定的任务。由于视觉传感器能够获得最大量的环境信息,因此高效稳定的机器人视觉系统是移动机器人不可或缺的重要部分。针对机器人视觉系统的图像处理算法以及软硬件设计展开研究,取得了一定的成果。
对视觉图像处理算法进行了研究,包括图像预处理、图像分割、运动目标检测等等。通过仿真对比各方法的优缺点,选择其中较好的方法对其编程实现。采用摄像机针孔模型进行目标定位,避免了定位时大量的矩阵运算,提高了运算速度。针对传统阈值分割的不足,将模糊神经网络应用于图像分割,提出了一种新型的基于模糊神经网络组的彩色图像可变阈值分割方法。该方法有效地克服了传统固定阈值分割中颜色阈值受外界光线变化影响较大的问题,对不同光照下的颜色阈值具有自适应调整功能,提高了图像分割精度。采用快速补偿学习算法对神经网络组的各个子网络进行训练,在普通神经网络的基础上进一步提高网络的收敛速度,缩短了计算时间,保证了分割的实时性。通过仿真实验,验证了该方法的有效性。
对机器人视觉系统的软硬件进行设计。硬件电路采用高性能的嵌入式DSP作为核心处理器,既可完成机器人视觉系统中大量的数据运算,又能大大提高视觉系统的便捷性。设计了硬件电路的各个模块,包括视频编解码模块、存储器模块、通信模块以及电源模块等等。完成了视觉系统的软件设计。以CCS为开发环境,运用DSP/BIOS操作系统,完成了程序装载功能,并对软件系统进行初始化。
最后对该机器人视觉系统进行了实验研究。软件实现了一系列的图像处理算法,包括图像增强、图像滤波、图像分割以及运动目标检测等,并实现了目标定位跟踪应用软件和运动目标速度检测应用软件。两者具有较强的实用性和可拓展性。通过实验证明了其有效性。
The first problem need to be solved by the intelligent robot when in practice is the perpception to the external environment. Only after this could it complete the specific tasks. The visual sensor can get the most significant environmental information, so highly efficient and stable mobile robot vision system is an indispensable part of the robot. For the robot vision system, study is carried out about the image processing algorithms and the software and hardware designs, and a certain achievements are acquired.
Researches have been done to the visiual image processing algorithms, including image pre-processing, image segmentation, moving target detection, etc. Comparing the advantages and disadvantages of the various methods through simulations, the better ways are chosen and realized by program. The pin-hole camera model is used for target positioning, which avoids a large number of matrix operations, and increases computing speed. For the deficiencies of the traditional threshold segmentation, the fuzzy neural network is applied on image segmentation, and a new method with variable threshold based on fuzzy neural network group is proposed for the color image segmentation. The method effectively overcomes the problem of great impact of the external changing light on color image segmentation, and has a self-adjustment function for the color thresholds under different light environments, which increases the accuracy of image segmentation. The sub-networks are trained respectively by rapid compensation learning algorithm. It further improves the convergence rate of the whole network on the basis of general neural network, and shortens the calculation time. The validity of the algorithm is verified through the simulation experiments.
The software and hardware of the robot vision system is designed. The hardware adopts a high-performance embedded DSP as the core processor, which could not only complete a large amount of data computation, but also greatly enhance the ease of the vision system. Various hardware modules of the system are designed, including video codec and decode modules, memory modules, power module, communication module, and so on. The software design of the vision system is completed. Program loading and software initialization is arrived using DSP/BIOS operating system under the CCS development environment.
Ultimately, the robot vision system is studied. A series of image processing algorithms are realized by programming, including image enhancement, image filtering, image segmentation and moving target detection. What’s more, two experiments are carried out, one of which is target location and tracking, and another is speed detection of moving target. Both of them are practical and extensible. The validity is shown through the experiments.
对视觉图像处理算法进行了研究,包括图像预处理、图像分割、运动目标检测等等。通过仿真对比各方法的优缺点,选择其中较好的方法对其编程实现。采用摄像机针孔模型进行目标定位,避免了定位时大量的矩阵运算,提高了运算速度。针对传统阈值分割的不足,将模糊神经网络应用于图像分割,提出了一种新型的基于模糊神经网络组的彩色图像可变阈值分割方法。该方法有效地克服了传统固定阈值分割中颜色阈值受外界光线变化影响较大的问题,对不同光照下的颜色阈值具有自适应调整功能,提高了图像分割精度。采用快速补偿学习算法对神经网络组的各个子网络进行训练,在普通神经网络的基础上进一步提高网络的收敛速度,缩短了计算时间,保证了分割的实时性。通过仿真实验,验证了该方法的有效性。
对机器人视觉系统的软硬件进行设计。硬件电路采用高性能的嵌入式DSP作为核心处理器,既可完成机器人视觉系统中大量的数据运算,又能大大提高视觉系统的便捷性。设计了硬件电路的各个模块,包括视频编解码模块、存储器模块、通信模块以及电源模块等等。完成了视觉系统的软件设计。以CCS为开发环境,运用DSP/BIOS操作系统,完成了程序装载功能,并对软件系统进行初始化。
最后对该机器人视觉系统进行了实验研究。软件实现了一系列的图像处理算法,包括图像增强、图像滤波、图像分割以及运动目标检测等,并实现了目标定位跟踪应用软件和运动目标速度检测应用软件。两者具有较强的实用性和可拓展性。通过实验证明了其有效性。
The first problem need to be solved by the intelligent robot when in practice is the perpception to the external environment. Only after this could it complete the specific tasks. The visual sensor can get the most significant environmental information, so highly efficient and stable mobile robot vision system is an indispensable part of the robot. For the robot vision system, study is carried out about the image processing algorithms and the software and hardware designs, and a certain achievements are acquired.
Researches have been done to the visiual image processing algorithms, including image pre-processing, image segmentation, moving target detection, etc. Comparing the advantages and disadvantages of the various methods through simulations, the better ways are chosen and realized by program. The pin-hole camera model is used for target positioning, which avoids a large number of matrix operations, and increases computing speed. For the deficiencies of the traditional threshold segmentation, the fuzzy neural network is applied on image segmentation, and a new method with variable threshold based on fuzzy neural network group is proposed for the color image segmentation. The method effectively overcomes the problem of great impact of the external changing light on color image segmentation, and has a self-adjustment function for the color thresholds under different light environments, which increases the accuracy of image segmentation. The sub-networks are trained respectively by rapid compensation learning algorithm. It further improves the convergence rate of the whole network on the basis of general neural network, and shortens the calculation time. The validity of the algorithm is verified through the simulation experiments.
The software and hardware of the robot vision system is designed. The hardware adopts a high-performance embedded DSP as the core processor, which could not only complete a large amount of data computation, but also greatly enhance the ease of the vision system. Various hardware modules of the system are designed, including video codec and decode modules, memory modules, power module, communication module, and so on. The software design of the vision system is completed. Program loading and software initialization is arrived using DSP/BIOS operating system under the CCS development environment.
Ultimately, the robot vision system is studied. A series of image processing algorithms are realized by programming, including image enhancement, image filtering, image segmentation and moving target detection. What’s more, two experiments are carried out, one of which is target location and tracking, and another is speed detection of moving target. Both of them are practical and extensible. The validity is shown through the experiments.
引文
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2. Horn B K P.Robot Vision[M].Cambridge,Massachusetts:MTT Press,1986
3. Chen H T,Jiang P and Wang Y J.Shape glass cutting direct drive robot[C].Proc. Int. Conf. On ManufacturingAutomation,Hongkong,1997:146—151
4.李磊,叶涛,谭民.移动机器人技术研究现状与未来[J].机器人技术与应用.2002,24(5),475—480
5. T Nagata,K Konishi,H Zha. Cooperative manipulator based on genetic algorithms using contact information[J]. Proc Int Conf on Intelligent Robots and Systems,Pittsburgh, Pennsylvania,1995,400—405
6. H.D.Cheng,C.H.Chen,H.H.Chiu.Image segmentation using fuzzy homogeneity criterion[J].Information Sciences,1997,98(1):237—262
7. Philippe Schmid.Segmentation of digitized dermatoscopic images by two dimensional color clustering[J].IEEE Trans.On Medical Imaging,1999,18(2):164—171
8. K.S. Sim, C.P. Tso, Y.Y. Tan. Recursive sub-image histogram equalization applied to gray scale images[J].Pattern Recognition Letters,2007,28(10):1209—1221
9. Nikoletta Bassiou, Constantine Kotropoulos. Color image histogram equalization by absolute discounting back-off[J]. Computer Vision and Image Understanding,2007, 107(1-2):108—122
10. Abdullah Toprak, Inan Guler. Impulse noise reduction in medical images with the use of switch mode fuzzy adaptive median filter[J]. Digital Signal Processing,2007,17(4): 711—723
11. Tzu-Chao Lin. Progressive decision-based mean type filter for image noise suppression [J]. Computer Standards & Interfaces, 2008,30(3):106—114
12.刘涵,刘丁,李琦.一种遗传—模糊神经网络图像滤波器[J].仪器仪表学报, 2004,25(3):310—312,320
13. Chung-Chia Kang, Wen-June Wang. A novel edge detection method based on the maximizing objective function[J]. Pattern Recognition,2007,40(2),609—618
14. Ety Navon,Ofer Miller,Amir Averbuch. Color image segmentation based on adaptive local thresholds[J]. Image and Vision Computing,2005,23(1): 69—85
15.席志红.全方位视觉技术及其在智能移动机器人等领域的应用研究[D].[博士学位论文].哈尔滨:哈尔滨工程大学,2006
16.张广军.机器视觉(第一版)[M].北京:科学出版社,2005
17.蔡自兴.机器人学[M].北京:清华大学出版社,2000
18.王麟琨,徐德,谭民.机器人视觉伺服研究进展[J].机器人,2004,26(3):277—282
19.林靖,陈辉堂.机器人视觉伺服系统的研究[J],控制理论与应用,2000,17(4):476—481
20. Tarek Hamel,Robert Mahony. Image based visual servo control for a class of aerial robotic systems[J]. Automatica,2007,43(11):1975—1983
21.张毅,罗元,郑太雄.移动机器人技术及其应用[M].北京:电子工业出版社,2007
22.黄晶,赵臣,周明明.基于快速彩色空间变换的足球机器人目标搜索[J].哈尔滨工业大学学报,2003,35(9):1036—1039
23. Russo F.An image enhancement technique combining sharpening and noise reduction[J]. IEEE Transactions on Instrumentation and Measurement,2002,51(4): 824—828
24.井上诚喜,八木伸行等.实用数字图像处理[M] .北京:科学出版社,2003
25. Tsung-Nan Lin, Kai-Jie Chan. Adaptive-hierarchical filtering approach for noise removal [J]. Displays,2008,29(3): 209—213
26. Samuel Morillas,Valentin Gregori.A fast impulsive noise color image filter using fuzzy metrics[J].Real-Time Imaging,2005,11(5-6):417—428
27. Chang-Shing Lee,Chin-Yuan Hsu,Yau-Hwang Kuo.Intelligent fuzzy image filter for impulse noise removal[C].Proceedings of the 2002 IEEE International Conference on,2002,1:431—436
28.王明佳,张旭光,韩广良等.自适应权值滤波消除图像椒盐噪声的方法[J].光学精密工程,2007,15,5:779—783
29.邹艳碧,高鹰.自适应滤波算法综述[J].广州大学学报(自然科学版),2002,2(1):44—50
30. Ren Shuyan,Pu Zhaobang,Zhuang Zhitao.Image filter arithmetic based on fuzzy neural network[J].Chinese Journal of Scientific Instrument,2006,27(6):801—804
31. C.H.Messom,G.S.Gupta,H.L.Sng.Distributed Real-time Image Processing for a Dual Camera System[C].CIRAS 2001,Singapore,2001:53—59
32. Philippe Schmid.Segmentation of digitized dermatoscopic images by two dimensional color clustering[J].IEEE Trans.On Medical Imaging,1999,18(2):164—171
33.张发存,赵晓红,王忠等.区域生长法图像分割的数据并行方法研究[J].计算机工程,2004,30(17):14—16
34. Tolga Bektas,Osman Oguz,Iradj Ouveysi.A Lagrangean relaxation and decomposition algorithm for the video placement and routing problem[J].European Journal of Operational Research,2007,182(1):455—465
35. Michael H F.Optimizing edge detectors for robust automatic threshold selection:coping with edge curvature and noise[J].Graphical models and image processing,1998, 60(5): 385—401
36. C.H.Messom,S.Demidenko,K.Subramaniam et al.Size/Position Identification in real-time image processing using run length encoding[C].IEEE Instrumentation and Measurement Technology Conference Anchorage,2002,21—23
37. James Bruce,Manuela Veloso.Fast and accurate vision-based pattern detection and identification[C].In Proceedings of ICRA'03,the 2003 IEEE International Conference on Robotics and Automation,Taiwan,May 2003
38. Mehmet Sezgin,Bulent Sankur.Survey over image thresholding techniques and quantitative performance evaluation[J].Journal of Electronic Imaging,2004,13(1):146—165
39. Wren C,Azarayejani A,Darrell T et al.Pfinder:Real-time tracking of the human body[J]. IEEE Transactions PAMI,1997,19(7):780—785
40.杨必武,郭晓松.用于识别一类缺陷字符的过筛连通—模板匹配法[J].计算机工程,2002,28(9):88—89
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