ALV超声测障技术研究
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摘要
本文以陆地自主车(Autonomous Land Vehicle,简称ALV)为应用背景,从超声测距系统、高精度超声测距处理算法以及基于超声传感器的障碍定位和识别技术等三个方面展开了对超声测障技术的深入研究和实验验证。
本文首先设计和实现了基于单片机的多通道超声信号采集处理平台。通过上层控制指令,该平台能够完成不同发射方案、不同采样频率以及不同处理算法的测距处理,具有功能强大、便携性好和抗干扰能力强的优点。
在超声测距处理算法的研究方面,本文从理论上分析了调制解调技术的引入对测距系统处理增益的影响,根据数字相关法与匹配滤波器的等价性,使用包络相关解调算法实现了伪随机码数字相关测距,并给出了改进的两步相关快速测距算法的计算公式。此外,为进一步提高系统的处理增益和抗干扰能力,本文还引入了双极性M序列以实现PSK相干解调。
针对互相关函数峰值检测中的误差来源,本文提出了一种新的基于阈值的两次检测算法,在很大程度上解决了未知环境中阈值设定的难题,提高了超声测距的精度。
结合传感器阵列的设计,本文分析了不同情况下超声信息的融合方法,给出了基于多距离信息的障碍定位算法。通过对影响超声回波波形的主要因素进行深入分析,本文建立了超声回波信号模型,并提出了基于回波振幅和时长特征的障碍类型判别算法。
In this thesis, the obstacle detection technology based on ultrasonic sensors has been studied for Autonomous Land Vehicle (ALV) applications. The thesis consists of three closely related subjects: ultrasonic range-finding system, ultrasonic ranging processing method and localization and classification of obstacles.
At first, an ultrasonic range-finding system based on a single-chip microcomputer is designed and realized. According to different instructions from PC, the system can accomplish ranging work under different transmitting schemes, different sampling rates as well as different processing methods. The system shows high precision, portability and high resistance to interference through practices.
With deeply research of both the effect of the introduction of modulation and demodulation technology on processing gain of range-finding system and the equivalence of correlation detection and matching detection, this paper has realized the digital correlation detection arithmetic based on PN code via the envelope demodulation method. Also the calculate formulas for a fast matching detection approach based on two step correlation is concluded. Besides, for the further improvement of the processing gain, M sequence and polar envelopes are utilized to realize PSK demodulation.
In order to get the precise position of the echo, a novel peak detection method is proposed. The error due to the fluctuation of the peak pulse of the cross-correlation function could be eliminated effectively in this way, so the assignment problem of threshold in unknown environment can be resolved well.
Incorporating the design of sensor array, data fusion of multiple ultrasonic sensors in different situations is discussed, and also the obstacle localization method is presented. Finally, the echo model is built by analyzing the affecting factors of the waveform of echo, and as a result, a novel obstacle recognition method based on the echo amplitude and duration features is proposed.
本文首先设计和实现了基于单片机的多通道超声信号采集处理平台。通过上层控制指令,该平台能够完成不同发射方案、不同采样频率以及不同处理算法的测距处理,具有功能强大、便携性好和抗干扰能力强的优点。
在超声测距处理算法的研究方面,本文从理论上分析了调制解调技术的引入对测距系统处理增益的影响,根据数字相关法与匹配滤波器的等价性,使用包络相关解调算法实现了伪随机码数字相关测距,并给出了改进的两步相关快速测距算法的计算公式。此外,为进一步提高系统的处理增益和抗干扰能力,本文还引入了双极性M序列以实现PSK相干解调。
针对互相关函数峰值检测中的误差来源,本文提出了一种新的基于阈值的两次检测算法,在很大程度上解决了未知环境中阈值设定的难题,提高了超声测距的精度。
结合传感器阵列的设计,本文分析了不同情况下超声信息的融合方法,给出了基于多距离信息的障碍定位算法。通过对影响超声回波波形的主要因素进行深入分析,本文建立了超声回波信号模型,并提出了基于回波振幅和时长特征的障碍类型判别算法。
In this thesis, the obstacle detection technology based on ultrasonic sensors has been studied for Autonomous Land Vehicle (ALV) applications. The thesis consists of three closely related subjects: ultrasonic range-finding system, ultrasonic ranging processing method and localization and classification of obstacles.
At first, an ultrasonic range-finding system based on a single-chip microcomputer is designed and realized. According to different instructions from PC, the system can accomplish ranging work under different transmitting schemes, different sampling rates as well as different processing methods. The system shows high precision, portability and high resistance to interference through practices.
With deeply research of both the effect of the introduction of modulation and demodulation technology on processing gain of range-finding system and the equivalence of correlation detection and matching detection, this paper has realized the digital correlation detection arithmetic based on PN code via the envelope demodulation method. Also the calculate formulas for a fast matching detection approach based on two step correlation is concluded. Besides, for the further improvement of the processing gain, M sequence and polar envelopes are utilized to realize PSK demodulation.
In order to get the precise position of the echo, a novel peak detection method is proposed. The error due to the fluctuation of the peak pulse of the cross-correlation function could be eliminated effectively in this way, so the assignment problem of threshold in unknown environment can be resolved well.
Incorporating the design of sensor array, data fusion of multiple ultrasonic sensors in different situations is discussed, and also the obstacle localization method is presented. Finally, the echo model is built by analyzing the affecting factors of the waveform of echo, and as a result, a novel obstacle recognition method based on the echo amplitude and duration features is proposed.
引文
[1] 王京起,陈慧岩.陆地自主车辆研究概况.车辆与动力技术,2002(4):56-61.
[2] Charles M.Shoemaker,Jonathan A.Bornstein. The Demo Ⅲ UGV program:A testbed for autonomous navigation research. Proceedings of the 1998 IEEE ISIC/CIRA/ISAS Joint Conference,Gaithersburg,MD,Sept.14-17,1998:644-651.
[3] Maurer Markus,Behringer Reinhold and Dickmanns Dirk. VaMoRs-P:an advanced platform for visual autonomous road vehicle guidance. SPIE,1994,2352:239-248.
[4] 陈华华.视觉导航关键技术研究:立体视觉和路径规划:[博士学位论文].杭州:浙江大学,2005.
[5] 孙振平.自主驾驶汽车智能控制系统:[博士学位论文].长沙:国防科学技术大学, 2004.
[6] 刘文学,方漪.智能车辆测距技术.科技信息,2006(4):11-12.
[7] 党宏社等.汽车巡航控制用传感器.传感器技术,2002,21(1):1-3.
[8] 李鸿征,邱红卫.基于多传感器信息融合的车辆主动防碰撞控制系统.传感器世界,2002(5):28-30.
[9] 赵广涛,程荫杭.基于超声波传感器的测距系统设计.微计算机信息,2006,22(1-1): 129-130.
[10] 冯若.超声手册.南京:南京大学出版社,1999:16-91.
[11] 史亦韦.超声检测.北京:机械工业出版社,2005:4-35.
[12] 林书玉.超声换能器的原理及设计.北京:科学出版社,2004:2-11.
[13] 潘仲明.大量程超声波测距系统研究:[博士学位论文].长沙:国防科学技术大学, 2006.
[14] R.Jarvis. A laser time-of-flight range scanner for robotic vision. IEEE Trans. Pattern Analysis Mach.Intell.,Vol.PAMI–5,1983:505-512.
[15] M.Parrilla,J.J.Anaya and C.Fritsch. Digital signal processing techniques for high accuracy ultrasonic range measurements. IEEE Trans. Instruments and Measurement, 1991,40(4):759-763.
[16] 许天增,许克平等.超声传输特性和超声传感系统研究.厦门大学学报(自然科学版),2001(2):303-310.
[17] 杨劲松,王敏,黄心汉.超声波可变阈值测距装置.电子应用技术,1998(7):7-9.
[18] P.Webb,C.Wykes. Analysis of fast accurate low ambiguity beam forming for non λ/2ultrasonic arrays. Ultrasonics,2001,39:69-78.
[19] 翟国富,刘茂恺.一种实时高精度的机器人用超声波处理方法.应用声学,1996,15(1):35-45.
[20] 童峰,许水源,许天增.一种高精度超声波测距处理方法.厦门大学学报(自然科学版),1998,37(4):507-512.
[21] L.Kleeman,R.Kuc. Mobile robot sonar for target localization and classification. The International Journal of Robotics Research,1995,14(4):295-318.
[22] J.Klaus-Werner,B.Markus and M.Markus. Using pseudo-random codes for mobile robot sonar sensing. Third IFAC Symposium on Intelligent Autonomous Vehicles (IAV ’98),Madrid,1998:231-236.
[23] J.Ure?a,M.Mazo. Correlation detector based on a FPGA for ultrasonic sensors. Microprocessors and Microsystems,1999,23:25-33.
[24] 葛万成,吴凤萍.两步相关法高抗干扰超声波距离测量技术研究.仪器仪表学报,2002,23(3):253-256.
[25] 潘仲明,王跃科,杨俊.超声波扩频测距及其信道自适应均衡技术.国防科技大学学报,2002,24(6):95-98.
[26] 童峰,许天增.一种用于移动机器人导航的全向高精度超声测距处理方法.中南工业大学学报,2000,31:292-294.
[27] 童峰,许水源,许天增.基于遗传算法的超声信号自适应时延估计.应用声学,2000,19(4):26-30.
[28] 高鹰,谢胜利.一种变步长 LMS 自适应滤波算法及分析.电子学报,2001,29(8): 1094-1097.
[29] 邹晓红.小波分析在超声回波测量中的应用.传感器技术,2005,24(7):78-82.
[30] 刘喜昂,周志宇.基于多超声传感器的机器人安全避障技术.测控技术,2004,23(3):71-73.
[31] 陈春林,陈宗海,卓 睿.基于多超声波传感器的自主移动机器人探测系统.测控技术,2004,23(6):11-13.
[32] 李保国,宗光华.未知环境中移动机器人实时导航与避障的分层模糊控制.机器人,2005,27(6):481-485.
[33] 李彩虹等.基于 ANN 方式的超声波传感器数据融合.测控技术,1999,18(8):45-46.
[34] Olle Wijk,Henrik I. Triangulation-based fusion of sonar data with application in robot pose tracking. IEEE Trans. Robotics and Automation,2000,16(6):740-752.
[35] 娄万军,杨继宏,于鹏.履带式行走机器人的多超声波传感器信息融合方法.长春理工大学学报,2005,28(4):60-62.
[36] 叶涛等.运动学约束情况下的移动机器人导航控制研究.高技术通讯,2004(2):49-53.
[37] 戈新良,杨杰.基于多传感器融合的机器人障碍物检测和识别.计算机仿真,2004,22(3):168-170.
[38] Youngjoon Han,Hernsoo Hahn. Localization and classification of target surfaces using two pairs of ultrasonic sensors. Robotics and Autonomous Systems,2000,33:31-41.
[39] Gines Benét,Milagros Martínez. Differentiating walls from corners using the amplitude of ultrasonic echos. Robotics and Autonomous Systems,2005,50:31-41.
[40] Neil Harper,Phillip Mckerrow. Recognising plants with ultrasonic sensing for mobile robot navigation. Robotics and Autonomous Systems,2001,34:71-82.
[41] 王幸之,钟爱琴等.AT89 系列单片机原理与接口技术.北京:北京航空航天大学出版社,2004:20-21.
[42] MAXIM.CMOS high-speed 8-bit ADCs with multiplexer and reference MAX158.1996.
[43] 曾迎生,刘爱琴,朱红卫. 用于自主车绕障的超声传感器线阵. 高技术通讯,1995,5(4):9-12
[44] 杨高波,亓波.精通 MATLAB 7.0 混合编程.北京:电子工业出版社,2006:37-44.
[45] 葛万成.相关法高抗干扰超声波距离测量中的信号处理.同济大学学报,2002,30(1):71-76.
[46] 冯玉珉等. 通信系统原理.北京:北方交通大学出版社,2003:58-268.
[47] R.Kuc. A spatial criterion for sonar object detection. IEEE Trans. Pattern Analysis Mach.Intell.,Vol.PAMI,1990,12(7):686-690.
[48] 樊昌信等. 通信原理(第五版). 北京:国防工业出版社,2003:68-69.
[49] 姜建国等. 信号与系统分析基础. 北京:清华大学出版社,2001:24-50.
[50] 王秉钧等. 扩频通信. 天津:天津大学出版社,1993:109-115.
[2] Charles M.Shoemaker,Jonathan A.Bornstein. The Demo Ⅲ UGV program:A testbed for autonomous navigation research. Proceedings of the 1998 IEEE ISIC/CIRA/ISAS Joint Conference,Gaithersburg,MD,Sept.14-17,1998:644-651.
[3] Maurer Markus,Behringer Reinhold and Dickmanns Dirk. VaMoRs-P:an advanced platform for visual autonomous road vehicle guidance. SPIE,1994,2352:239-248.
[4] 陈华华.视觉导航关键技术研究:立体视觉和路径规划:[博士学位论文].杭州:浙江大学,2005.
[5] 孙振平.自主驾驶汽车智能控制系统:[博士学位论文].长沙:国防科学技术大学, 2004.
[6] 刘文学,方漪.智能车辆测距技术.科技信息,2006(4):11-12.
[7] 党宏社等.汽车巡航控制用传感器.传感器技术,2002,21(1):1-3.
[8] 李鸿征,邱红卫.基于多传感器信息融合的车辆主动防碰撞控制系统.传感器世界,2002(5):28-30.
[9] 赵广涛,程荫杭.基于超声波传感器的测距系统设计.微计算机信息,2006,22(1-1): 129-130.
[10] 冯若.超声手册.南京:南京大学出版社,1999:16-91.
[11] 史亦韦.超声检测.北京:机械工业出版社,2005:4-35.
[12] 林书玉.超声换能器的原理及设计.北京:科学出版社,2004:2-11.
[13] 潘仲明.大量程超声波测距系统研究:[博士学位论文].长沙:国防科学技术大学, 2006.
[14] R.Jarvis. A laser time-of-flight range scanner for robotic vision. IEEE Trans. Pattern Analysis Mach.Intell.,Vol.PAMI–5,1983:505-512.
[15] M.Parrilla,J.J.Anaya and C.Fritsch. Digital signal processing techniques for high accuracy ultrasonic range measurements. IEEE Trans. Instruments and Measurement, 1991,40(4):759-763.
[16] 许天增,许克平等.超声传输特性和超声传感系统研究.厦门大学学报(自然科学版),2001(2):303-310.
[17] 杨劲松,王敏,黄心汉.超声波可变阈值测距装置.电子应用技术,1998(7):7-9.
[18] P.Webb,C.Wykes. Analysis of fast accurate low ambiguity beam forming for non λ/2ultrasonic arrays. Ultrasonics,2001,39:69-78.
[19] 翟国富,刘茂恺.一种实时高精度的机器人用超声波处理方法.应用声学,1996,15(1):35-45.
[20] 童峰,许水源,许天增.一种高精度超声波测距处理方法.厦门大学学报(自然科学版),1998,37(4):507-512.
[21] L.Kleeman,R.Kuc. Mobile robot sonar for target localization and classification. The International Journal of Robotics Research,1995,14(4):295-318.
[22] J.Klaus-Werner,B.Markus and M.Markus. Using pseudo-random codes for mobile robot sonar sensing. Third IFAC Symposium on Intelligent Autonomous Vehicles (IAV ’98),Madrid,1998:231-236.
[23] J.Ure?a,M.Mazo. Correlation detector based on a FPGA for ultrasonic sensors. Microprocessors and Microsystems,1999,23:25-33.
[24] 葛万成,吴凤萍.两步相关法高抗干扰超声波距离测量技术研究.仪器仪表学报,2002,23(3):253-256.
[25] 潘仲明,王跃科,杨俊.超声波扩频测距及其信道自适应均衡技术.国防科技大学学报,2002,24(6):95-98.
[26] 童峰,许天增.一种用于移动机器人导航的全向高精度超声测距处理方法.中南工业大学学报,2000,31:292-294.
[27] 童峰,许水源,许天增.基于遗传算法的超声信号自适应时延估计.应用声学,2000,19(4):26-30.
[28] 高鹰,谢胜利.一种变步长 LMS 自适应滤波算法及分析.电子学报,2001,29(8): 1094-1097.
[29] 邹晓红.小波分析在超声回波测量中的应用.传感器技术,2005,24(7):78-82.
[30] 刘喜昂,周志宇.基于多超声传感器的机器人安全避障技术.测控技术,2004,23(3):71-73.
[31] 陈春林,陈宗海,卓 睿.基于多超声波传感器的自主移动机器人探测系统.测控技术,2004,23(6):11-13.
[32] 李保国,宗光华.未知环境中移动机器人实时导航与避障的分层模糊控制.机器人,2005,27(6):481-485.
[33] 李彩虹等.基于 ANN 方式的超声波传感器数据融合.测控技术,1999,18(8):45-46.
[34] Olle Wijk,Henrik I. Triangulation-based fusion of sonar data with application in robot pose tracking. IEEE Trans. Robotics and Automation,2000,16(6):740-752.
[35] 娄万军,杨继宏,于鹏.履带式行走机器人的多超声波传感器信息融合方法.长春理工大学学报,2005,28(4):60-62.
[36] 叶涛等.运动学约束情况下的移动机器人导航控制研究.高技术通讯,2004(2):49-53.
[37] 戈新良,杨杰.基于多传感器融合的机器人障碍物检测和识别.计算机仿真,2004,22(3):168-170.
[38] Youngjoon Han,Hernsoo Hahn. Localization and classification of target surfaces using two pairs of ultrasonic sensors. Robotics and Autonomous Systems,2000,33:31-41.
[39] Gines Benét,Milagros Martínez. Differentiating walls from corners using the amplitude of ultrasonic echos. Robotics and Autonomous Systems,2005,50:31-41.
[40] Neil Harper,Phillip Mckerrow. Recognising plants with ultrasonic sensing for mobile robot navigation. Robotics and Autonomous Systems,2001,34:71-82.
[41] 王幸之,钟爱琴等.AT89 系列单片机原理与接口技术.北京:北京航空航天大学出版社,2004:20-21.
[42] MAXIM.CMOS high-speed 8-bit ADCs with multiplexer and reference MAX158.1996.
[43] 曾迎生,刘爱琴,朱红卫. 用于自主车绕障的超声传感器线阵. 高技术通讯,1995,5(4):9-12
[44] 杨高波,亓波.精通 MATLAB 7.0 混合编程.北京:电子工业出版社,2006:37-44.
[45] 葛万成.相关法高抗干扰超声波距离测量中的信号处理.同济大学学报,2002,30(1):71-76.
[46] 冯玉珉等. 通信系统原理.北京:北方交通大学出版社,2003:58-268.
[47] R.Kuc. A spatial criterion for sonar object detection. IEEE Trans. Pattern Analysis Mach.Intell.,Vol.PAMI,1990,12(7):686-690.
[48] 樊昌信等. 通信原理(第五版). 北京:国防工业出版社,2003:68-69.
[49] 姜建国等. 信号与系统分析基础. 北京:清华大学出版社,2001:24-50.
[50] 王秉钧等. 扩频通信. 天津:天津大学出版社,1993:109-115.