基于神经网络的自主吸尘机器人混合感知系统设计及避障规划
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摘要
感知系统是机器人智能化的基础，它好比机器人的“眼睛”，廉价高效的混合感知系统是本文设计的目标。本系统采用了基于BP神经网络的智能避障算法作为吸尘机器人的“大脑”，其中感知系统得到的障碍物信息作为神经网络的输入。神经网络的输出作为行走电机的驱动信息，在该驱动信息的作用下机器人完成避障策略。在由DSP算法处理系统和单片机控制系统组成的机器人实验平台上进行了实验，用实验验证了避障算法的有效性。
本文由四章组成，各章的主要内容如下。第一章介绍了国内外自主吸尘机器人的研究现状，并且介绍了经典的移动机器人和主要的吸尘机器人的感知系统，在此基础上提出了本文的研究内容。第二章在对超声波传感器和红外传感器特性分析的基础上，给出了具有高可靠性和高抗干扰能力的混合感知系统的设计。基于该感知系统，得到了大量关于超声波传感器和红外传感器特性的实验数据。第三章具体介绍了基于神经网络的吸尘机器人智能避障算法的实现过程，用FoxPro建立训练样本的数据库，用VC编程实现避障算法，并且将算法移植到DSP系统中。利用MATLAB提供的神经网络工具箱对BP神经网络进行仿真设计，从理论上验证了算法的有效性。并且在对BP神经网络主要性能参数分析的基础上得出了包括隐层神经元个数、学习速率和目标误差等参数的优化组合。第四章完成了由DSP和单片机构成的机器人实验平台的具体硬件设计，包括DSP最小系统设计、DSP与单片机的HPI通信、DSP与液晶通过CPLD实现时序匹配以及单片机控制系统等。
The sensor system, which is the "eyes" of the robot, is the foundation of robots' intelligence. So, to design a hybrid sensor system with perfect performance and low cost is the aim of this thesis. And the intelligent obstacle-avoiding algorithm based on BP neural network is designed as the "brain". All these are realized on the experimental platform of an autonomous cleaning robot.
The obstacle information which is apperceived by the sensor system acts as input of the BP neural network, the output of the network acts as commands which drive the motors and the robot implement the obstacle avoiding behaviors according to the commands. The efficiency of the obstacle avoidance algorithm is validated with the experiments which are carried out on the robot experimental platform, which includes the DSP system for the algorithm processing and the MCU control system.
This thesis includes four chapters, and the contents are as follows. The first chapter introduces the research status of the autonomous cleaning robot at home and abroad. Some classical sensor systems of the mobile robots and the cleaning robots are also introduced in this chapter. At the end, the main research content of this thesis is presented.
In chapter 2, the hybrid sensor system with high reliability and perfect anti-jamming capability is designed considering the characteristic of sonar sensors and infrared sensors. And much experimental data about the characteristic of the designed sensor system is obtained and analyzed.
The third chapter introduces the realization of the intelligent obstacle avoidance algorithm based on BP neural network. The designed BP neural network is simulated with MATLAB neural network toolbox, as well as the efficiency of the algorithm discussed in theory. And the optimal configuration of parameters is obtained through analyzing the performance parameters of the network.
In chapter 4, the architecture and implementation of the hardware system is addressed. The platform includes the minimum application system for DSP, the MCU control system, the HPI communication between DSP and MCU, and the time-sequence matching between DSP and LCD by using CPLD.
本文由四章组成，各章的主要内容如下。第一章介绍了国内外自主吸尘机器人的研究现状，并且介绍了经典的移动机器人和主要的吸尘机器人的感知系统，在此基础上提出了本文的研究内容。第二章在对超声波传感器和红外传感器特性分析的基础上，给出了具有高可靠性和高抗干扰能力的混合感知系统的设计。基于该感知系统，得到了大量关于超声波传感器和红外传感器特性的实验数据。第三章具体介绍了基于神经网络的吸尘机器人智能避障算法的实现过程，用FoxPro建立训练样本的数据库，用VC编程实现避障算法，并且将算法移植到DSP系统中。利用MATLAB提供的神经网络工具箱对BP神经网络进行仿真设计，从理论上验证了算法的有效性。并且在对BP神经网络主要性能参数分析的基础上得出了包括隐层神经元个数、学习速率和目标误差等参数的优化组合。第四章完成了由DSP和单片机构成的机器人实验平台的具体硬件设计，包括DSP最小系统设计、DSP与单片机的HPI通信、DSP与液晶通过CPLD实现时序匹配以及单片机控制系统等。
The sensor system, which is the "eyes" of the robot, is the foundation of robots' intelligence. So, to design a hybrid sensor system with perfect performance and low cost is the aim of this thesis. And the intelligent obstacle-avoiding algorithm based on BP neural network is designed as the "brain". All these are realized on the experimental platform of an autonomous cleaning robot.
The obstacle information which is apperceived by the sensor system acts as input of the BP neural network, the output of the network acts as commands which drive the motors and the robot implement the obstacle avoiding behaviors according to the commands. The efficiency of the obstacle avoidance algorithm is validated with the experiments which are carried out on the robot experimental platform, which includes the DSP system for the algorithm processing and the MCU control system.
This thesis includes four chapters, and the contents are as follows. The first chapter introduces the research status of the autonomous cleaning robot at home and abroad. Some classical sensor systems of the mobile robots and the cleaning robots are also introduced in this chapter. At the end, the main research content of this thesis is presented.
In chapter 2, the hybrid sensor system with high reliability and perfect anti-jamming capability is designed considering the characteristic of sonar sensors and infrared sensors. And much experimental data about the characteristic of the designed sensor system is obtained and analyzed.
The third chapter introduces the realization of the intelligent obstacle avoidance algorithm based on BP neural network. The designed BP neural network is simulated with MATLAB neural network toolbox, as well as the efficiency of the algorithm discussed in theory. And the optimal configuration of parameters is obtained through analyzing the performance parameters of the network.
In chapter 4, the architecture and implementation of the hardware system is addressed. The platform includes the minimum application system for DSP, the MCU control system, the HPI communication between DSP and MCU, and the time-sequence matching between DSP and LCD by using CPLD.
引文
1 朱世强，刘瑜，庞作伟，金波．自主吸尘机器人的研究现状．机器人．2004，Vol．26(6)：569～574．
2 刘瑜．自主吸尘机器人[D]．杭州：浙江大学出版社，2006．
3 吴秋轩，曹广益．家用服务型吸尘机器人的发展与现状．电气传动自动化．2004，Vol．25(6)．
4 胡跃明，丁维中，吴忻生．吸尘机器人的研究现状与展望．机器人技术与应用．2002，第一期．
5 www.electrolux.com.TrilobiteZA2_860001EN.pdf.
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10 马翔，朱世强，吴海彬．智能吸尘器的开发及设计[J]．电子技术应用，2000，8．
11 X. Ma, S. Zhu, J. Fu. Study on Intelligent Dust-collecting Robot[A]. Proceedings of the Fifth International Conference on Fluid Power Transmission and Control, Hangzhou, China PR: International Academic Publishers, 2001, 392-396.
12 Liu Yu, Shiqiang Zhu, Bo Jin, Shenshen Feng, Huafeng Gong. Sensory Navigation of Autonomous Cleaning Robots. Proceedings of the fifth world congress on intelligent control and automation. 2004, 5:4793～4796 (EI Compendex).
13 田春颖，刘瑜，冯申坤，朱世强．基于栅格地图的移动机器人完全遍历算法：矩形分解法．机械工程学报．2004，Vol．40(10)：56～61(EI Compendex)．
14 朱世强，刘瑜，庞作伟，金波．自主吸尘机器人的研究现状．机器人．2004，Vol．26(6)：569～574．
15 冯申珅，龚华锋，朱世强，刘瑜．智能吸尘机器人控制系统的设计．机电工程．2004，Vol．21(4)：4～7．
16 Songguo Liu, Shiqiang Zhu, Yu Liu, Zuowei Pang, Fengshen Zhao. Autonomous Cleaning Robot Working in Unstructured Environment. Proceedings of the Sixth International Conference on Fluid Power Transmission and Control(ICFP'2005). 2005:831-835(ISTP).
17 徐勇，朱世强，刘瑜．基于神经网络的自主吸尘机器人混合视觉研究[J]．电子技术应用，2006，(11)．
18 Lanthier, M., D.Nussbaum, and A.Sheng. Improving vision-based maps by using sonar and infrared data[J]. Robotics and Applications,2004(8).
19 Jonathan Connell, Paul Viola. Cooperative Control of a Semi-Autonomous Mobile Robot. in Proc. 1990 IEEE Int. Conf. on Robotics and Automation, Cincinnati, OH, May 13-18, pp. 1118-1121.
20 庞作伟，朱世强，刘瑜．吸尘机器人视觉系统设计．机电工程．2004，Vol．21(6)：9～12．
21 庞作伟．基于DSP的自主吸尘机器人立体视觉系统研究．浙江大学硕士学位论文．2005．
22 方帅，胡英，徐心和．集控式足球机器人视觉子系统的关键技术[J]东北大学学报(自然科学版)，2003,(11)．
23 李开生，张慧慧，费仁元，宗光华．定位传感器及其融合技术综述[J]．计算机自动测量与控制，2001，(04)．
24 方建军，何广平．智能机器人．北京：化学工业出版社，2004．
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27 陈永甫．红外探测与控制电路．北京：人民邮电出版社，2004．
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31 龚华锋，冯申坤，朱世强，刘瑜，金波．对自主吸尘机器人多路超声波下窜绕信号的研究[J]．机床与液压，2002，(7)：43-46．
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2 刘瑜．自主吸尘机器人[D]．杭州：浙江大学出版社，2006．
3 吴秋轩，曹广益．家用服务型吸尘机器人的发展与现状．电气传动自动化．2004，Vol．25(6)．
4 胡跃明，丁维中，吴忻生．吸尘机器人的研究现状与展望．机器人技术与应用．2002，第一期．
5 www.electrolux.com.TrilobiteZA2_860001EN.pdf.
6 http://www.iRobot.com.
7 http://www.lge.com.
8 http://www.robocleaner.de.
9 吴海彬，朱世强，马翔．自主吸尘机器人在非结构化环境下的壁障与路径规划研究[J]．机器人，2000，22(7)．
10 马翔，朱世强，吴海彬．智能吸尘器的开发及设计[J]．电子技术应用，2000，8．
11 X. Ma, S. Zhu, J. Fu. Study on Intelligent Dust-collecting Robot[A]. Proceedings of the Fifth International Conference on Fluid Power Transmission and Control, Hangzhou, China PR: International Academic Publishers, 2001, 392-396.
12 Liu Yu, Shiqiang Zhu, Bo Jin, Shenshen Feng, Huafeng Gong. Sensory Navigation of Autonomous Cleaning Robots. Proceedings of the fifth world congress on intelligent control and automation. 2004, 5:4793～4796 (EI Compendex).
13 田春颖，刘瑜，冯申坤，朱世强．基于栅格地图的移动机器人完全遍历算法：矩形分解法．机械工程学报．2004，Vol．40(10)：56～61(EI Compendex)．
14 朱世强，刘瑜，庞作伟，金波．自主吸尘机器人的研究现状．机器人．2004，Vol．26(6)：569～574．
15 冯申珅，龚华锋，朱世强，刘瑜．智能吸尘机器人控制系统的设计．机电工程．2004，Vol．21(4)：4～7．
16 Songguo Liu, Shiqiang Zhu, Yu Liu, Zuowei Pang, Fengshen Zhao. Autonomous Cleaning Robot Working in Unstructured Environment. Proceedings of the Sixth International Conference on Fluid Power Transmission and Control(ICFP'2005). 2005:831-835(ISTP).
17 徐勇，朱世强，刘瑜．基于神经网络的自主吸尘机器人混合视觉研究[J]．电子技术应用，2006，(11)．
18 Lanthier, M., D.Nussbaum, and A.Sheng. Improving vision-based maps by using sonar and infrared data[J]. Robotics and Applications,2004(8).
19 Jonathan Connell, Paul Viola. Cooperative Control of a Semi-Autonomous Mobile Robot. in Proc. 1990 IEEE Int. Conf. on Robotics and Automation, Cincinnati, OH, May 13-18, pp. 1118-1121.
20 庞作伟，朱世强，刘瑜．吸尘机器人视觉系统设计．机电工程．2004，Vol．21(6)：9～12．
21 庞作伟．基于DSP的自主吸尘机器人立体视觉系统研究．浙江大学硕士学位论文．2005．
22 方帅，胡英，徐心和．集控式足球机器人视觉子系统的关键技术[J]东北大学学报(自然科学版)，2003,(11)．
23 李开生，张慧慧，费仁元，宗光华．定位传感器及其融合技术综述[J]．计算机自动测量与控制，2001，(04)．
24 方建军，何广平．智能机器人．北京：化学工业出版社，2004．
25 陈永甫．红外辐射红外器件及典型应用．电子工业出版社．北京，2004．
26 肖景和，赵键．红外线、热释电与超声波遥控电路．北京：人民邮电出版社，2003．
27 陈永甫．红外探测与控制电路．北京：人民邮电出版社，2004．
28 National Semiconductor Inc. LM567/LM567C Tone Decoder Datasheet, 1999.
29 Air Ultrasonic Ceramic Transducers,400ST/R160 datasheet.
30 佛朗哥．S．(Franco，Sergio)著，刘树棠，朱茂林，容玫译．基于运算放大器和模拟集成电路的电路设计．西安：西安交通大学出版社，2005．
31 龚华锋，冯申坤，朱世强，刘瑜，金波．对自主吸尘机器人多路超声波下窜绕信号的研究[J]．机床与液压，2002，(7)：43-46．
32 M. Flynn, "Combining sonar and infrared sensors for mobilerobot navigation," Int. J. Robot. Res., pp. 5-14, Dec. 1988.
33 Kam M,Zhu M G, Kalata P. Sensor fusion for mobile robot navigation[J]. Proceedings of the IEEE,1997,85(1):108～119.
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