基于多传感器的室内移动机器人环境感知关键技术研究
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摘要
室内移动机器人具有广阔的应用前景,可以应用于服务、娱乐、安检等各个方面。室内环境一般属于比较复杂的动态环境,机器人在这样的环境下运行时,对动态环境的感知与响应能力提出了更高的要求,其中最主要的是对运动物体的跟踪和避障。另外,在家庭等动态场合下运动,机器人不可避免地受到移动物体的碰撞和用户的触碰,对这类环境作用的感知与响应是机器人实现自我保护并与环境保持和谐的基础。对室内危险气体泄漏和火情的探测则是安检类机器人的一项重要功能,属于移动机器人对危险环境的感知范畴。这三项环境感知技术对于室内移动机器人在各方面的应用是至关重要的。
国内外对这三个环境感知问题的相关方面也开展了一定的研究,但是在具体应用中尚有不足。论文在国家863计划的支持下,针对室内移动机器人运行所涉及到的上述三个环境感知问题,从实际应用出发,系统地开展了理论和实验研究。
首先研制了HR-I室内移动机器人系统,作为机器人环境感知问题的研究平台。根据移动机器人运动灵活、支撑稳定、结构简单的要求,对支撑结构进行了尺寸优化。通过模块化设计,在软、硬件上集成了运动环境感知、危险环境感知和运动状态感知需要的多种传感器。为了提高机器人的多任务管理能力和灵活的响应能力,在上、下位机控制结构的基础上,基于混合式体系结构的框架建立机器人的控制系统。
针对快速变化的动态环境,采用高效、准确的激光测距仪实现运动环境感知。在建立传感器感知模型的基础上,参照栅格法提出了动态极坐标图环境建模方法。将环境物体描述为直线和弧特征,在数据分割的基础上,利用递归迭代直线拟合方法分离出线段特征。为了简化环境描述,将障碍物简化为以三个极坐标点表示,并将密集障碍物合并成群,进一步简化了障碍环境的描述。动态极坐标图环境模型不但减少了数据的存储、处理工作量,而且提高了机器人基于行为控制的反应能力。
对动态障碍物的跟踪是实现良好避障的基础。针对机器人与运动障碍的相对运动变化较大的情况,提出了考虑运动补偿的动态障碍物跟踪方法,通过建立机器人的位姿更新模型及其协方差更新模型,将机器人的运动状态引入到基于扩展卡尔曼滤波理论的障碍跟踪模型中,实现对运动目标当前状态的估计和运动趋势的预测。为了提高避障的灵活性,采用分层避障策略,并在预测碰撞时间和位置的基础上提出了动态障碍物的避障策略。
针对在动态环境下,机器人不可避免地受到碰撞和敲打的情况,采用二维加速度计实现对碰撞的感知。为了分析机器人的碰撞振动特点,首先研究了机器人在静态受到碰撞时引起的振动信号特性。通过对振动信号序列合力的大小和方向分析,提出了基于规则的碰撞方向确定方法。根据碰撞方向检测能力和机器人的响应要求提出了分区响应策略,对碰撞采取避让和顺应反应方法。然后,针对机器人运动时,自身的速度变化、机体振动和路面倾斜状态都会产生加速度信号,与碰撞信号相混合问题,设计了动态碰撞检测窗口,在时域内实现对动态碰撞振动信号的实时识别和提取,并基于Motor Schema行为融合理论建立了兼顾障碍和运动目标的动态碰撞响应策略。
机器人在气体危险环境中巡检时,危险源搜索一直是个比较困难的问题。在分析了气体传感器的响应滞后现象、建立传感器感知模型的基础上,提出了基于视觉和气体传感器的危险点直接搜索策略和基于模糊逻辑的危险区域逐步搜索策略,提高了搜索效率,克服了传感器响应滞后及单纯气羽搜索的不足。逐步搜索策略充分利用气体扩散和危险气源特征的先验知识,对于随机分布的危险源,通过模糊逻辑判断可疑区域的危险性,根据危险程度逐步搜索,靠近危险源。对于火灾危险,利用火灾燃烧时释放出各种燃烧气体并改变环境温度的特点,机器人基于气体传感器和温度传感器,实现了对火情的搜索和早期报警。为了实现对火情危险程度的评估,提出了基于支持向量机的评估模型,与神经网络模型相比,具有更强的分级和推广能力。
通过仿真和实验研究,表明了理论方法的有效性,使室内移动机器人能够较好地实现相应环境的感知。
The indoor mobile robots have broad application prospect, which can be used in service, entertainment, safe checking and so on. Indoor surroundings belong to complicated dynamic environment, which needs robot a higher surroundings sensing ability and responding ability, and the ability of tracking and avoiding moving object is most important. When running on dynamic environment such as home, it is inevitable for robot to be knocked by moving object or touched by a person, and the sensing and responding to bump is the basis for self-protection and being harmonious with environment. It is an important function for danger checking robot to detect the danger gas and fire indoor, which needs good performance of robot on danger environment sensing. The three environment sensing technologies are critical for indoor mobile robots when work on different condition.
The relating aspects of the three environment sensing problem have been studied by researchers, but they are still not sufficient for specific application. The dissertation carries out theoretical and experimental research on the three problems for indoor mobile robot supported by National 863 Program.
The HR-I indoor mobile robot system is designed for environment sensing studying, and the structure is optimized according to the demands of motion flexibility, support stability and structure simplification. The robot is designed with modular conception, on which many kinds of sensors are integrated to realize motion surroundings sensing, dangerous environment sensing and motion control. The control system is set up with hybrid architecture on the base of up and down computer control structure to improve the task managing ability and motion reacting ability.
The motion environment sensing is mainly through Laser Range Finder, the sensing module is set up and the dynamic polar coordinate map model is presented referring to the grid map method based on the rolling window principle. The objects can be described with line and arc feature in the model. Line feature can be extracted with Recursive Line Fitting Method after the laser data is divided into segments to represent different objects. The obstacles are further described with three polar points, and the crowded obstacles will be merged into groups to reduce environment data.
The tracking of moving obstacles is the basis of good obstacle avoiding performance. In order to realize obstacle tracking with moving platform, the position and posture estimating model of robot and its covariance model are set up based on odometer. Based on these models the moving obstacle tracking model is derived according to the Kalman Filter theory, which can estimate the current state and predict the future state of obstacle more accurately. To improve the obstacle avoiding flexibility the layered obstacle avoiding policy is adopted. For moving obstacle, an avoiding policy including waiting, bypassing and quickly going through is presented based on the prediction of collision time and position.
The two-dimension accelerometer is adopted to perceive bump. The vibration signal caused by bump when robot is static is studied first to analyze the features of bump signal. According to the features of vibration signal resultant force and its direction, a rule based bump direction determing method is presented. Since the bumping direction is not very precise, a bump reacting policy according to different zone around robot is presented, which induce the robot make avoiding reaction or adjustment reaction. When robot is running, the velocity variation, the body vibration and road inclination can all induce acceleration signal, which will mix with bump signal. To recognize and extract the bump signal, a bump signal detection window is designed, which can work on time domain in real time. A dynamic bump responding approach is set up based on Motor Schema theory to consider obstacle and motion goal at the same time.
In the process of danger environment detection, the gas source searching by robot is always a difficult problem. The responding and recovery delay of gas sensors are analyzed through experiment, and then the sensing model is also set up. For the environment that the danger sources are known, a Danger Site Directly Searching Policy is presented based on vision and gas sensors. For randomly distribute danger sources, the robot can judge the risk of suspected region through fuzzy logic by making use of the prior knowledge about gas distribution and source characters, and searching the region with higher risk grade step by step to find the source. Simulation shows the validity of the searching policy. Since gases can be released and the surrounding temperature will increase when there is a fire, the CO, CO2, CH4 and temperature sensors are used to detect fire, and a fire intensity estimating model is set up based on Support Vector Machine theory to judge the fire level. The contrast to neural network shows better classification and generalization ability.
The simulation and experiment show the validity of theory approach, the robot can make good sensing to its environment and respond to it with better performance.
国内外对这三个环境感知问题的相关方面也开展了一定的研究,但是在具体应用中尚有不足。论文在国家863计划的支持下,针对室内移动机器人运行所涉及到的上述三个环境感知问题,从实际应用出发,系统地开展了理论和实验研究。
首先研制了HR-I室内移动机器人系统,作为机器人环境感知问题的研究平台。根据移动机器人运动灵活、支撑稳定、结构简单的要求,对支撑结构进行了尺寸优化。通过模块化设计,在软、硬件上集成了运动环境感知、危险环境感知和运动状态感知需要的多种传感器。为了提高机器人的多任务管理能力和灵活的响应能力,在上、下位机控制结构的基础上,基于混合式体系结构的框架建立机器人的控制系统。
针对快速变化的动态环境,采用高效、准确的激光测距仪实现运动环境感知。在建立传感器感知模型的基础上,参照栅格法提出了动态极坐标图环境建模方法。将环境物体描述为直线和弧特征,在数据分割的基础上,利用递归迭代直线拟合方法分离出线段特征。为了简化环境描述,将障碍物简化为以三个极坐标点表示,并将密集障碍物合并成群,进一步简化了障碍环境的描述。动态极坐标图环境模型不但减少了数据的存储、处理工作量,而且提高了机器人基于行为控制的反应能力。
对动态障碍物的跟踪是实现良好避障的基础。针对机器人与运动障碍的相对运动变化较大的情况,提出了考虑运动补偿的动态障碍物跟踪方法,通过建立机器人的位姿更新模型及其协方差更新模型,将机器人的运动状态引入到基于扩展卡尔曼滤波理论的障碍跟踪模型中,实现对运动目标当前状态的估计和运动趋势的预测。为了提高避障的灵活性,采用分层避障策略,并在预测碰撞时间和位置的基础上提出了动态障碍物的避障策略。
针对在动态环境下,机器人不可避免地受到碰撞和敲打的情况,采用二维加速度计实现对碰撞的感知。为了分析机器人的碰撞振动特点,首先研究了机器人在静态受到碰撞时引起的振动信号特性。通过对振动信号序列合力的大小和方向分析,提出了基于规则的碰撞方向确定方法。根据碰撞方向检测能力和机器人的响应要求提出了分区响应策略,对碰撞采取避让和顺应反应方法。然后,针对机器人运动时,自身的速度变化、机体振动和路面倾斜状态都会产生加速度信号,与碰撞信号相混合问题,设计了动态碰撞检测窗口,在时域内实现对动态碰撞振动信号的实时识别和提取,并基于Motor Schema行为融合理论建立了兼顾障碍和运动目标的动态碰撞响应策略。
机器人在气体危险环境中巡检时,危险源搜索一直是个比较困难的问题。在分析了气体传感器的响应滞后现象、建立传感器感知模型的基础上,提出了基于视觉和气体传感器的危险点直接搜索策略和基于模糊逻辑的危险区域逐步搜索策略,提高了搜索效率,克服了传感器响应滞后及单纯气羽搜索的不足。逐步搜索策略充分利用气体扩散和危险气源特征的先验知识,对于随机分布的危险源,通过模糊逻辑判断可疑区域的危险性,根据危险程度逐步搜索,靠近危险源。对于火灾危险,利用火灾燃烧时释放出各种燃烧气体并改变环境温度的特点,机器人基于气体传感器和温度传感器,实现了对火情的搜索和早期报警。为了实现对火情危险程度的评估,提出了基于支持向量机的评估模型,与神经网络模型相比,具有更强的分级和推广能力。
通过仿真和实验研究,表明了理论方法的有效性,使室内移动机器人能够较好地实现相应环境的感知。
The indoor mobile robots have broad application prospect, which can be used in service, entertainment, safe checking and so on. Indoor surroundings belong to complicated dynamic environment, which needs robot a higher surroundings sensing ability and responding ability, and the ability of tracking and avoiding moving object is most important. When running on dynamic environment such as home, it is inevitable for robot to be knocked by moving object or touched by a person, and the sensing and responding to bump is the basis for self-protection and being harmonious with environment. It is an important function for danger checking robot to detect the danger gas and fire indoor, which needs good performance of robot on danger environment sensing. The three environment sensing technologies are critical for indoor mobile robots when work on different condition.
The relating aspects of the three environment sensing problem have been studied by researchers, but they are still not sufficient for specific application. The dissertation carries out theoretical and experimental research on the three problems for indoor mobile robot supported by National 863 Program.
The HR-I indoor mobile robot system is designed for environment sensing studying, and the structure is optimized according to the demands of motion flexibility, support stability and structure simplification. The robot is designed with modular conception, on which many kinds of sensors are integrated to realize motion surroundings sensing, dangerous environment sensing and motion control. The control system is set up with hybrid architecture on the base of up and down computer control structure to improve the task managing ability and motion reacting ability.
The motion environment sensing is mainly through Laser Range Finder, the sensing module is set up and the dynamic polar coordinate map model is presented referring to the grid map method based on the rolling window principle. The objects can be described with line and arc feature in the model. Line feature can be extracted with Recursive Line Fitting Method after the laser data is divided into segments to represent different objects. The obstacles are further described with three polar points, and the crowded obstacles will be merged into groups to reduce environment data.
The tracking of moving obstacles is the basis of good obstacle avoiding performance. In order to realize obstacle tracking with moving platform, the position and posture estimating model of robot and its covariance model are set up based on odometer. Based on these models the moving obstacle tracking model is derived according to the Kalman Filter theory, which can estimate the current state and predict the future state of obstacle more accurately. To improve the obstacle avoiding flexibility the layered obstacle avoiding policy is adopted. For moving obstacle, an avoiding policy including waiting, bypassing and quickly going through is presented based on the prediction of collision time and position.
The two-dimension accelerometer is adopted to perceive bump. The vibration signal caused by bump when robot is static is studied first to analyze the features of bump signal. According to the features of vibration signal resultant force and its direction, a rule based bump direction determing method is presented. Since the bumping direction is not very precise, a bump reacting policy according to different zone around robot is presented, which induce the robot make avoiding reaction or adjustment reaction. When robot is running, the velocity variation, the body vibration and road inclination can all induce acceleration signal, which will mix with bump signal. To recognize and extract the bump signal, a bump signal detection window is designed, which can work on time domain in real time. A dynamic bump responding approach is set up based on Motor Schema theory to consider obstacle and motion goal at the same time.
In the process of danger environment detection, the gas source searching by robot is always a difficult problem. The responding and recovery delay of gas sensors are analyzed through experiment, and then the sensing model is also set up. For the environment that the danger sources are known, a Danger Site Directly Searching Policy is presented based on vision and gas sensors. For randomly distribute danger sources, the robot can judge the risk of suspected region through fuzzy logic by making use of the prior knowledge about gas distribution and source characters, and searching the region with higher risk grade step by step to find the source. Simulation shows the validity of the searching policy. Since gases can be released and the surrounding temperature will increase when there is a fire, the CO, CO2, CH4 and temperature sensors are used to detect fire, and a fire intensity estimating model is set up based on Support Vector Machine theory to judge the fire level. The contrast to neural network shows better classification and generalization ability.
The simulation and experiment show the validity of theory approach, the robot can make good sensing to its environment and respond to it with better performance.
引文
1 A. Lilienthal, D. Reimann, A. Zell. Gas source tracing with a mobile robot using an adapted moth strategy. Proceedings of Autonomous Mobile System. 2003: 150-160
2 H. Ishida, H. Tanaka, H. Taniguchi, T. Moriiizumi. Mobile robot navigation using vision and olfaction to search for a gas/odor source. Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2004: 313-318
3 E. A. Topp, D. Kragic, P. Jensfelt and H. I. Christensen. An interactive interface for service robots. Proceedings of International Conference on Robotics and Automation. 2004: 3469-3474
4 B. Choi, S. Lee, H. R. Choi and S. Kang. Development of anthropomorphic robot hand with tactile sensor. SKKU Hand II. IEEE/RSJ International Conference on Intelligent Robots and Systems. 2006: 3779-3784
5 Z. Bien, M. J. Chung, P. H. Chang. Integration of a rehabilitation robotic system (KARES II) with human-friendly man-machine interaction units. Autonomous Robots. 2004, 16(2): 165-191
6余静,游志胜.自动目标识别与跟踪技术研究综述.计算机应用研究2005, 1: 12-15
7吉书鹏,张桂林,丁晓青.地面复杂场景图像相关跟踪算法研究.激光与红外. 2002, 32(6): 428-430
8任仙怡,廖云涛,张桂林.一种新的相关跟踪方法研究.中国图像图形学报. 2002, 7(6): 553-557
9雍杨,王敬儒,张启衡.基于塔型结构的快速相关跟踪算法.光电工程. 2003, 30(6): 11-14
10苏虹,蔡自兴.未知环境下移动机器人运动目标跟踪技术研究进展.华中科技大学学报. 2004, 32: 24-27
11朱志宇,姜长生,张冰.多传感器多机动目标跟踪方法研究进展.现代防御技术. 2005, 33(5): 60-66
12张殿友,张友益.快速机动目标跟踪算法研究.舰船电子对抗. 2007, 30(1): 75-80
13李景熹,王树宗,王航宇,宋振伟.基于多模型和辅助粒子滤波的机动目标跟踪算法研究.武汉理工大学学报. 2007, 4: 35-38
14李延秋,沈毅,刘志言.基于粒子滤波器的多机动目标跟踪贝叶斯滤波算法研究.战术导弹技术. 2005, 2:51-54
15何炎祥,范收平,基于神经网络和人工势场的滚动规划.计算机工程与应用. 2005, 41(31): 66-68
16 Elfes A. Sonar-based real-world mapping and navigation. IEEE Journal of Robotics and Automation. 1987, 3(3): 249-265
17蔡自兴,郑敏捷,邹小兵.基于激光雷达的移动机器人实时避障策略.中南大学学报. 2006, 4(37): 324-329
18徐璐,陈阳舟,居鹤华.基于动态行为控制的移动机器人自主避障.计算机工程. 2007, 33(14): 180-182
19曹成才,姚进,王强.基于几何法的移动机器人路径规划.计算机应用研究. 2005, 12: 82-86
20石鸿雁,孙昌志.非结构环境下移动机器人的运动规划.机器人. 2004, 26(1): 27-31
21沈晓晶,潘俊民.基于自适应Kalman预测器的运动估计算法.计算机仿真. 2004, 21(10): 73-78
22李彩虹,李贻斌,范晨.移动机器人动态避障算法.山东大学学报. 2007, 37(5): 60-64
23陈少斌,蒋静坪.基于神经网络和粒子群优化算法的移动机器人动态避障路径规划.系统仿真技术. 2006, 2(4): 192-197
24鞠浩,牛合明,张建勋.基于遗传算法的家用保安机器人路径规划方法.高技术通讯. 2007,17(9): 924-928
25 http://www.activrobots.com/ACCESSORIES/bumper.html
26 K. FUJIWARA, F. KANEHIRO, S. KAJITA and K. YOKOI. The first human-size humanoid that can fall over safely and stand-up again. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. 2003: 1920-1926
27 J. Palacín, T. Palleja, I. Valga?ón, R. Pernia and J. Roca. Measuring coverage performances of a floor cleaning mobile robot using a vision system. Proceedings of International Conference on Robotics and Automation. 2005: 4247-4252
28 G. Campion and V. Hayward. The pantograph Mk-II: a haptic instrument. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. 2005: 723-728
29 Y. Koda and T. Maeno. Grasping force control in master-slave system with partial slip sensor. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. 2006: 4641-4646
30 W. T. Ang, P. K. Khosla, and C. N. Riviere. Design of all-accelerometer inertial measurement unit for tremor sensing in hand-held microsurgical instrument. Proceedings of International Conference on Robotics and Automation. 2003: 1781-1786
31 D. Sadhukhan, Autonomous ground vehicle terrain classification using internal sensors. Master’s thesis. Florida State University. 2004
32 C. Brooks, K. Iagnemma, and S. Dubowsky. Vibration-based terrain analysis for mobile robots. Proceedings of International Conference on Robotics and Automation. 2005: 3426-3431
33 P. D. Welch. The use of Fast Fourier Transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans. Audio and Electroacoustics. 1967, 15(6): 70-73
34 C. Weiss, H. Fr?hlich and A. Zell. Vibration-based terrain classification using support vector machines. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. 2006: 4429-4434
35 D. Sadhukhan and C. Moore. Online terrain estimation using internal sensors. Proceeding Florida Conference on Recent Advances in Robotics. 2003:359-364
36 M. R. Wandel, A. Lilienthal, T. Duckett. Gas distribution in unventilated indoor environments inspected by a mobile robot. Proceedings of 2003 International Conference on Advanced Robot. 2003: 507-512
37 A. T. Hayes, A. Martinoli, R. M. Goodman. Distributed odor source localization. IEEE Sensors, Special Issue on Artificial Olfaction. 2002, 2(3): 260-271
38 D. Zarzhitsky, D. F. Spears, W. M. Spears. Distributed robotics approach to chemical plume tracing. Proceedings of 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2005: 2974-2979
39 T. Lochmatter, X. Raemy, A. Martinoli. Odor source localization with mobile robot. Bulletin of the Swiss Society for Automatic Control. 2007,46: 11-14
40 W. Jatmiko, Y. Ikemoto, T. Matsuno. Distributed odor source localization in dynamic environment. Sensors. 2005, 5: 254-257
41 H. Ishida, T. Nakamoto, T. Mpriizumi. Remote sensing of gas/odor source localization and concentartion using mobile system. Sensors and Actuators. 1998, 49: 52-57
42 A. Lilienthal, T. Duckett. A stereo electronic nose for a mobile inspection robot. Proceedings of the First International Workshop on Robotic Sensing. 2003:1-6
43 A. Lilienthal, T. Duckett. Experimental analysis of smelling Braitenberg Vehicles. Proceedings of 2003 International Conference on Advanced Robot. 2003: 375-380
44 B. Porat and A. Nehorai. Localizing vapor-emitting sources by moving sensors. IEEE Trans. Signal Processing. 1996, 44(4): 1018-1021
45 G. Sandini, G. Lucarini, M. Varoli. Gradient driven self-organising system. Proceedings of 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems. 1993: 429-432
46 A. Dhariwal, G. S. Sukhatme and A. A. G. Requicha. Bacterium-inspired robots for environmental monitoring. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. 2004: 1436-1443
47 L. Marques, A.T. De Almeida. Electronic nose-based odor source localization. Proceedings of the 6th International Workshop on Advanced Motion Control. 2000: 36-40
48 T. Nakamoto, H. Ishida, T. Moriizumi. A Sensing system for odor Plumes. Analytical Chemical News and Features. 1999,1: 531-537
49 A. J. Lilienthal, M. R. Wandel, U. Weimar. Experiences using gas sensors on an autonomous mobile robot. Proceedings of EUROBOT 2001, 4th European Workshop on Advanced Mobile Robots. 2001: 4005-4010
50 L. Marques, A. Martins and A. T. de Almeida. Environmental monitoring with mobile robots. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. 2005: 1140-1145
51 H. Ishida, K. Suetsugu, T. Nakamoto, T. Moriizumi. Study of autonomousmobile sensing system for localization of odor source using gas sensors and anemometric sensors. Sens. Actuators A. 1994, 45:153-157
52 H. Ishida, Y. Kagawa, T. Nakamoto, T. Moriizumi. Odorsource localization in the clean room by an autonomous mobile sensing system. Sens. Actuators B. 1996, 33:115-121
53 R. A. Russell, D. Thiel, R. Deveza, A. Mackay-Sim. A robotic system to locate hazardous chemical leaks. Proceedings of the 2006 IEEE International Conference on Robotics and Automation. 1995: 556-561
54 A. T. Hayes, A. Martinoli, R. M. Goodman. Distributed odor source localization. IEEE Sensors. 2002, 2:261-271
55 A. Lilienthal, T. Duckett. Creating gas concentration gridmaps with a mobile robot. Proceedings of 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2003: 118-123
56 D. Martinez and L. Perrinet. Cooperation between vision and olfaction in a koala robot. Report on the 2002 Workshop on Neuromorphic Engineering. 2002:51-53
57 A. Loutfi, S. Coradeschi, L. Karlsson, M. Broxvall. Using an electronic nose on multi-sensing mobile robot. Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2004: 337-342
58 H. Ishida, H. Tanaka, H. Taniguchi. Mobile robot navigation using vision and olfaction to search for a gas/odor source. Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2004: 313-318
59 G. Kowadlo, D. Rawlinson, R. A. Russell, R. Jarvis. Bi-modal search using complementary sensing(olfaction/vision) for odour source localization. Proceedings of the 2006 IEEE International Conference on Robotics and Automation. 2006: 2041-2046
60 W. Jatmiko, K. Sekiyama and T. Fukuda. A mobile robots PSO-based for odor source localization in dynamic advection-diffusion environment. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. 2006: 4527-4532
61张兢,路彦和,雷刚.数据融合技术在火灾自动探测中的应用研究.计算机测量与控制. 2006, 14(5): 610-612
62赵建华,喻益超,李庄.火灾气体辨识的人工神经网络方法研究.消防科学与技术. 2006, 25(5): 675-678
63崔东艳,徐凤荣,任华彬.火灾探测信号的智能处理算法.传感器与为系统. 2006, 25(9): 69-72
64杨效余,钱玮.模糊神经网络在火灾报警系统中的应用.自动化仪表. 2006, 1: 40-43
65 G. N. Saridis. Toward the realization of intelligent controls. IEEE Proceeding. 1979, 67(8): 1115-1133
66 R. Brooks. A robust layered control system for a mobile robot. IEEE Journal of robotics and automation (now IEEE Transactions on Robotics and Automation) . 1986, 1(1): 1-10
67 R. C. Arkin. Towards cosmopolitan robots: intelligent navigation in extended man-made environments. Ph.D. Thesis, University of Massachusetts. 1987
68 R. R. Murphy, R. C. Arkin. SFX: an architecture for action-oriented sensor fusion. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. 1992: 1079-1086
69 J. H. Connell. SSS: a hybrid architecture applied to robot navigation. Proceedings of IEEE International Conference on Robotics and Automation. 1992, 2: 719-724
70 J. D. Rosenblatt. A distributed architecture for mobile navigation. Robotics Institute, 1997
71 M. Piaggio. A non-hierarchical hybrid architecture for intelligent robots. ATAL’98. Paris, France, 1998
72蔡自兴,周翔,李枚毅,雷鸣.基于功能/行为集成的自主式移动机器人进化控制体系结构.机器人. 2002, 22(3):170-175
73 R. James, J. Ivra, B. Grady. The unified modeling language reference manual. Boston: Addison Wesley, 1999
74徐小良,汪乐宇,周泓.有限状态机的一种实现框架.工程设计学报.2003, 10(5):251-255
75 R. Arkin. Motor schema based mobile robot navigation. International Journal of Robotics Research. 1989, 8(4): 92-112
76梅风翔.非完整系统力学基础.北京工业学院出版社. 1985:28-30
77 H. Seraji. A unified approach to motion control of mobile manipulators. The International Journal of robotics research. 1998, 17(2): 107-118
78邹小兵,蔡自兴.非完整移动机器人道路跟踪控制器设计及应用.控制与决策. 2004,19(3): 319-322
79 C. Ye, J. Borenstein.Characterization of a 2-D Laser Scanner for Mobile Robot Obstacle negotiation.IEEE Conf: on Robotics and Automation, 2002:2512-2518
80庄严,王伟,王珂等.移动机器人基于激光测距和单目视觉的室内同时定位和地图构建.自动化学报. 2005,31(6):925-933
81杨明,王宏,何克忠.基于激光雷达的移动机器人环境建模与避障.清华大学学报. 2000, 40(7): 112-116
82庄严,徐晓东,王伟.移动机器人几何-拓扑混合地图的构建及子定位研究.控制与决策. 2005, 20(7): 815-820
83石朝侠,洪炳镕,周彤等.大规模环境下的拓扑地图创建与导航.机器人. 2007, 29(5): 433-438
84 R. C. LUO, M. G. KAY, Multi-sensor Integration an Fusion in Intelligent Systems, IEEE trans.On SMC, 1989, 19(5):901-931
85王卫华,陈卫东,席裕庚.基于不确定信息的移动机器人地图创建研究进展.机器人,2001,23(6),563-568
86 D. W.Cho. Certainty grid representation for robot navigation by Bayesian method, Robotica, 1990, 8:159-165
87 M. Weigl, B. Siemiatkowska, K. A. Sikorski, A. Borkowski.Grid based mapping for autonomous mobile robot . Robotics and Autonomous Systems,1993, 11:13-21
88胡小明,吴耿锋,樊建.基于时间栅格法和免疫算法的机器人动态路径规划.微型电脑应用,2005,21(2):1-2
89 T. P. Samuel, L. R. Stergios, W. B. Joel. Weighted line fitting algorithms for mobile robot map building and efficient data representation. Proceedings of IEEE International Conference on Robotics and Automation. 2003: 121-128
90 J. S. Gutmann. Robust Navigation autonomous mobile System. PhD thesis, Albert-Ludwigs-Universit¨at Freiburg, 2000.
91席裕庚,张纯刚.一类动态不确定环境下机器人的滚动路径规划.自动化学报,2002,28(2):161-175
92 A. Ram, R. C. Arkin, K. Moorman, R. J. Clark. Case-based reactive navigation:A method for on-line selection and adaptation of reactiverobotic control parameters, IEEE trans , On SMC-PARTB :CYBERNETICS, 1997, 27(3):376-393
93 Zhaoqing Ma, Zengren Yuan.Real-time Navigation and Obstacle Avoidance Based on Grids Method for Fast Mobile Robot.Engng Applic Artif Intell, 1995, 8(1):91-95
94 K. C. J. Dietmayer, J. Sparbert, D. Streller. Model based object classification and object tracking in traffic scenes. IEEE Intelligent Vehicle Symposium. 2001: 25-30
95 T. Einsele. Localization in indoor environments using a panoramic laser range finder. Ph.D. dissertation, Technical University of M¨unchen, September 2001
96 Xavier, J. Pacheco, M. Castro, D. Ruano. Fast line, arc/circle and leg detection from laser scan data in a player driver. IEEE International Conference on Robotics and Automation. 2005: 3941-3946
97洪伟,田彦涛等.智能机器人系统中局部环境特征的提取.机器人,2003,25(3):264-269
98 J. H. Lee, T. Tsubouchi, K. Yamamoto, S. Egawa. People tracking using a robot in motion with laser range finder. Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2006: 2936-2941
99万琴,王耀南.基于卡尔曼滤波器的运动目标检测与跟踪.湖南大学学报. 2007, 34(3): 36-40
100 M. Kohler. Using the kalman filter to track human interactive motion- modelling and initialization of the kalman filter for translational motion. Informatik VII, University of Dortmund, TR 629, 1997
101 A. Mendes, L. Conde, U. Nunes. Multi-target Detection and Tracking with a Laserscanner. IEEE Intelligent Vehicles Symposium. 2004: 796-801
102李人厚译.自主移动机器人导论.西安交通大学出版社. 2006
103张恒,雷志辉,丁晓华.一种改进的中值滤波算法.中国图象图形学报. 2004, 9(4): 408-411
104刘武发,蒋蓁,龚振邦.基于MEMS加速度传感器的双轴倾角计及其应用.传感器技术. 2005, 24(3): 86-88
105飞思科技产品研发中心.神经网络理论与MATLAB7实现.电子工业出版社. 2006: 100-127
106 V. Vapnik. Statistical learning theory. Wiley. New York, 1998.
107李盼池,许少华.支持向量在模式识别中的核函数特性分析.计算机工程与设计.2005, 26(2):302-304
108 C. W. Hsu, C. J. Lin. A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks. 2002, 13(2): 415-425
2 H. Ishida, H. Tanaka, H. Taniguchi, T. Moriiizumi. Mobile robot navigation using vision and olfaction to search for a gas/odor source. Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2004: 313-318
3 E. A. Topp, D. Kragic, P. Jensfelt and H. I. Christensen. An interactive interface for service robots. Proceedings of International Conference on Robotics and Automation. 2004: 3469-3474
4 B. Choi, S. Lee, H. R. Choi and S. Kang. Development of anthropomorphic robot hand with tactile sensor. SKKU Hand II. IEEE/RSJ International Conference on Intelligent Robots and Systems. 2006: 3779-3784
5 Z. Bien, M. J. Chung, P. H. Chang. Integration of a rehabilitation robotic system (KARES II) with human-friendly man-machine interaction units. Autonomous Robots. 2004, 16(2): 165-191
6余静,游志胜.自动目标识别与跟踪技术研究综述.计算机应用研究2005, 1: 12-15
7吉书鹏,张桂林,丁晓青.地面复杂场景图像相关跟踪算法研究.激光与红外. 2002, 32(6): 428-430
8任仙怡,廖云涛,张桂林.一种新的相关跟踪方法研究.中国图像图形学报. 2002, 7(6): 553-557
9雍杨,王敬儒,张启衡.基于塔型结构的快速相关跟踪算法.光电工程. 2003, 30(6): 11-14
10苏虹,蔡自兴.未知环境下移动机器人运动目标跟踪技术研究进展.华中科技大学学报. 2004, 32: 24-27
11朱志宇,姜长生,张冰.多传感器多机动目标跟踪方法研究进展.现代防御技术. 2005, 33(5): 60-66
12张殿友,张友益.快速机动目标跟踪算法研究.舰船电子对抗. 2007, 30(1): 75-80
13李景熹,王树宗,王航宇,宋振伟.基于多模型和辅助粒子滤波的机动目标跟踪算法研究.武汉理工大学学报. 2007, 4: 35-38
14李延秋,沈毅,刘志言.基于粒子滤波器的多机动目标跟踪贝叶斯滤波算法研究.战术导弹技术. 2005, 2:51-54
15何炎祥,范收平,基于神经网络和人工势场的滚动规划.计算机工程与应用. 2005, 41(31): 66-68
16 Elfes A. Sonar-based real-world mapping and navigation. IEEE Journal of Robotics and Automation. 1987, 3(3): 249-265
17蔡自兴,郑敏捷,邹小兵.基于激光雷达的移动机器人实时避障策略.中南大学学报. 2006, 4(37): 324-329
18徐璐,陈阳舟,居鹤华.基于动态行为控制的移动机器人自主避障.计算机工程. 2007, 33(14): 180-182
19曹成才,姚进,王强.基于几何法的移动机器人路径规划.计算机应用研究. 2005, 12: 82-86
20石鸿雁,孙昌志.非结构环境下移动机器人的运动规划.机器人. 2004, 26(1): 27-31
21沈晓晶,潘俊民.基于自适应Kalman预测器的运动估计算法.计算机仿真. 2004, 21(10): 73-78
22李彩虹,李贻斌,范晨.移动机器人动态避障算法.山东大学学报. 2007, 37(5): 60-64
23陈少斌,蒋静坪.基于神经网络和粒子群优化算法的移动机器人动态避障路径规划.系统仿真技术. 2006, 2(4): 192-197
24鞠浩,牛合明,张建勋.基于遗传算法的家用保安机器人路径规划方法.高技术通讯. 2007,17(9): 924-928
25 http://www.activrobots.com/ACCESSORIES/bumper.html
26 K. FUJIWARA, F. KANEHIRO, S. KAJITA and K. YOKOI. The first human-size humanoid that can fall over safely and stand-up again. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. 2003: 1920-1926
27 J. Palacín, T. Palleja, I. Valga?ón, R. Pernia and J. Roca. Measuring coverage performances of a floor cleaning mobile robot using a vision system. Proceedings of International Conference on Robotics and Automation. 2005: 4247-4252
28 G. Campion and V. Hayward. The pantograph Mk-II: a haptic instrument. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. 2005: 723-728
29 Y. Koda and T. Maeno. Grasping force control in master-slave system with partial slip sensor. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. 2006: 4641-4646
30 W. T. Ang, P. K. Khosla, and C. N. Riviere. Design of all-accelerometer inertial measurement unit for tremor sensing in hand-held microsurgical instrument. Proceedings of International Conference on Robotics and Automation. 2003: 1781-1786
31 D. Sadhukhan, Autonomous ground vehicle terrain classification using internal sensors. Master’s thesis. Florida State University. 2004
32 C. Brooks, K. Iagnemma, and S. Dubowsky. Vibration-based terrain analysis for mobile robots. Proceedings of International Conference on Robotics and Automation. 2005: 3426-3431
33 P. D. Welch. The use of Fast Fourier Transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans. Audio and Electroacoustics. 1967, 15(6): 70-73
34 C. Weiss, H. Fr?hlich and A. Zell. Vibration-based terrain classification using support vector machines. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. 2006: 4429-4434
35 D. Sadhukhan and C. Moore. Online terrain estimation using internal sensors. Proceeding Florida Conference on Recent Advances in Robotics. 2003:359-364
36 M. R. Wandel, A. Lilienthal, T. Duckett. Gas distribution in unventilated indoor environments inspected by a mobile robot. Proceedings of 2003 International Conference on Advanced Robot. 2003: 507-512
37 A. T. Hayes, A. Martinoli, R. M. Goodman. Distributed odor source localization. IEEE Sensors, Special Issue on Artificial Olfaction. 2002, 2(3): 260-271
38 D. Zarzhitsky, D. F. Spears, W. M. Spears. Distributed robotics approach to chemical plume tracing. Proceedings of 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2005: 2974-2979
39 T. Lochmatter, X. Raemy, A. Martinoli. Odor source localization with mobile robot. Bulletin of the Swiss Society for Automatic Control. 2007,46: 11-14
40 W. Jatmiko, Y. Ikemoto, T. Matsuno. Distributed odor source localization in dynamic environment. Sensors. 2005, 5: 254-257
41 H. Ishida, T. Nakamoto, T. Mpriizumi. Remote sensing of gas/odor source localization and concentartion using mobile system. Sensors and Actuators. 1998, 49: 52-57
42 A. Lilienthal, T. Duckett. A stereo electronic nose for a mobile inspection robot. Proceedings of the First International Workshop on Robotic Sensing. 2003:1-6
43 A. Lilienthal, T. Duckett. Experimental analysis of smelling Braitenberg Vehicles. Proceedings of 2003 International Conference on Advanced Robot. 2003: 375-380
44 B. Porat and A. Nehorai. Localizing vapor-emitting sources by moving sensors. IEEE Trans. Signal Processing. 1996, 44(4): 1018-1021
45 G. Sandini, G. Lucarini, M. Varoli. Gradient driven self-organising system. Proceedings of 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems. 1993: 429-432
46 A. Dhariwal, G. S. Sukhatme and A. A. G. Requicha. Bacterium-inspired robots for environmental monitoring. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. 2004: 1436-1443
47 L. Marques, A.T. De Almeida. Electronic nose-based odor source localization. Proceedings of the 6th International Workshop on Advanced Motion Control. 2000: 36-40
48 T. Nakamoto, H. Ishida, T. Moriizumi. A Sensing system for odor Plumes. Analytical Chemical News and Features. 1999,1: 531-537
49 A. J. Lilienthal, M. R. Wandel, U. Weimar. Experiences using gas sensors on an autonomous mobile robot. Proceedings of EUROBOT 2001, 4th European Workshop on Advanced Mobile Robots. 2001: 4005-4010
50 L. Marques, A. Martins and A. T. de Almeida. Environmental monitoring with mobile robots. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. 2005: 1140-1145
51 H. Ishida, K. Suetsugu, T. Nakamoto, T. Moriizumi. Study of autonomousmobile sensing system for localization of odor source using gas sensors and anemometric sensors. Sens. Actuators A. 1994, 45:153-157
52 H. Ishida, Y. Kagawa, T. Nakamoto, T. Moriizumi. Odorsource localization in the clean room by an autonomous mobile sensing system. Sens. Actuators B. 1996, 33:115-121
53 R. A. Russell, D. Thiel, R. Deveza, A. Mackay-Sim. A robotic system to locate hazardous chemical leaks. Proceedings of the 2006 IEEE International Conference on Robotics and Automation. 1995: 556-561
54 A. T. Hayes, A. Martinoli, R. M. Goodman. Distributed odor source localization. IEEE Sensors. 2002, 2:261-271
55 A. Lilienthal, T. Duckett. Creating gas concentration gridmaps with a mobile robot. Proceedings of 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2003: 118-123
56 D. Martinez and L. Perrinet. Cooperation between vision and olfaction in a koala robot. Report on the 2002 Workshop on Neuromorphic Engineering. 2002:51-53
57 A. Loutfi, S. Coradeschi, L. Karlsson, M. Broxvall. Using an electronic nose on multi-sensing mobile robot. Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2004: 337-342
58 H. Ishida, H. Tanaka, H. Taniguchi. Mobile robot navigation using vision and olfaction to search for a gas/odor source. Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2004: 313-318
59 G. Kowadlo, D. Rawlinson, R. A. Russell, R. Jarvis. Bi-modal search using complementary sensing(olfaction/vision) for odour source localization. Proceedings of the 2006 IEEE International Conference on Robotics and Automation. 2006: 2041-2046
60 W. Jatmiko, K. Sekiyama and T. Fukuda. A mobile robots PSO-based for odor source localization in dynamic advection-diffusion environment. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. 2006: 4527-4532
61张兢,路彦和,雷刚.数据融合技术在火灾自动探测中的应用研究.计算机测量与控制. 2006, 14(5): 610-612
62赵建华,喻益超,李庄.火灾气体辨识的人工神经网络方法研究.消防科学与技术. 2006, 25(5): 675-678
63崔东艳,徐凤荣,任华彬.火灾探测信号的智能处理算法.传感器与为系统. 2006, 25(9): 69-72
64杨效余,钱玮.模糊神经网络在火灾报警系统中的应用.自动化仪表. 2006, 1: 40-43
65 G. N. Saridis. Toward the realization of intelligent controls. IEEE Proceeding. 1979, 67(8): 1115-1133
66 R. Brooks. A robust layered control system for a mobile robot. IEEE Journal of robotics and automation (now IEEE Transactions on Robotics and Automation) . 1986, 1(1): 1-10
67 R. C. Arkin. Towards cosmopolitan robots: intelligent navigation in extended man-made environments. Ph.D. Thesis, University of Massachusetts. 1987
68 R. R. Murphy, R. C. Arkin. SFX: an architecture for action-oriented sensor fusion. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. 1992: 1079-1086
69 J. H. Connell. SSS: a hybrid architecture applied to robot navigation. Proceedings of IEEE International Conference on Robotics and Automation. 1992, 2: 719-724
70 J. D. Rosenblatt. A distributed architecture for mobile navigation. Robotics Institute, 1997
71 M. Piaggio. A non-hierarchical hybrid architecture for intelligent robots. ATAL’98. Paris, France, 1998
72蔡自兴,周翔,李枚毅,雷鸣.基于功能/行为集成的自主式移动机器人进化控制体系结构.机器人. 2002, 22(3):170-175
73 R. James, J. Ivra, B. Grady. The unified modeling language reference manual. Boston: Addison Wesley, 1999
74徐小良,汪乐宇,周泓.有限状态机的一种实现框架.工程设计学报.2003, 10(5):251-255
75 R. Arkin. Motor schema based mobile robot navigation. International Journal of Robotics Research. 1989, 8(4): 92-112
76梅风翔.非完整系统力学基础.北京工业学院出版社. 1985:28-30
77 H. Seraji. A unified approach to motion control of mobile manipulators. The International Journal of robotics research. 1998, 17(2): 107-118
78邹小兵,蔡自兴.非完整移动机器人道路跟踪控制器设计及应用.控制与决策. 2004,19(3): 319-322
79 C. Ye, J. Borenstein.Characterization of a 2-D Laser Scanner for Mobile Robot Obstacle negotiation.IEEE Conf: on Robotics and Automation, 2002:2512-2518
80庄严,王伟,王珂等.移动机器人基于激光测距和单目视觉的室内同时定位和地图构建.自动化学报. 2005,31(6):925-933
81杨明,王宏,何克忠.基于激光雷达的移动机器人环境建模与避障.清华大学学报. 2000, 40(7): 112-116
82庄严,徐晓东,王伟.移动机器人几何-拓扑混合地图的构建及子定位研究.控制与决策. 2005, 20(7): 815-820
83石朝侠,洪炳镕,周彤等.大规模环境下的拓扑地图创建与导航.机器人. 2007, 29(5): 433-438
84 R. C. LUO, M. G. KAY, Multi-sensor Integration an Fusion in Intelligent Systems, IEEE trans.On SMC, 1989, 19(5):901-931
85王卫华,陈卫东,席裕庚.基于不确定信息的移动机器人地图创建研究进展.机器人,2001,23(6),563-568
86 D. W.Cho. Certainty grid representation for robot navigation by Bayesian method, Robotica, 1990, 8:159-165
87 M. Weigl, B. Siemiatkowska, K. A. Sikorski, A. Borkowski.Grid based mapping for autonomous mobile robot . Robotics and Autonomous Systems,1993, 11:13-21
88胡小明,吴耿锋,樊建.基于时间栅格法和免疫算法的机器人动态路径规划.微型电脑应用,2005,21(2):1-2
89 T. P. Samuel, L. R. Stergios, W. B. Joel. Weighted line fitting algorithms for mobile robot map building and efficient data representation. Proceedings of IEEE International Conference on Robotics and Automation. 2003: 121-128
90 J. S. Gutmann. Robust Navigation autonomous mobile System. PhD thesis, Albert-Ludwigs-Universit¨at Freiburg, 2000.
91席裕庚,张纯刚.一类动态不确定环境下机器人的滚动路径规划.自动化学报,2002,28(2):161-175
92 A. Ram, R. C. Arkin, K. Moorman, R. J. Clark. Case-based reactive navigation:A method for on-line selection and adaptation of reactiverobotic control parameters, IEEE trans , On SMC-PARTB :CYBERNETICS, 1997, 27(3):376-393
93 Zhaoqing Ma, Zengren Yuan.Real-time Navigation and Obstacle Avoidance Based on Grids Method for Fast Mobile Robot.Engng Applic Artif Intell, 1995, 8(1):91-95
94 K. C. J. Dietmayer, J. Sparbert, D. Streller. Model based object classification and object tracking in traffic scenes. IEEE Intelligent Vehicle Symposium. 2001: 25-30
95 T. Einsele. Localization in indoor environments using a panoramic laser range finder. Ph.D. dissertation, Technical University of M¨unchen, September 2001
96 Xavier, J. Pacheco, M. Castro, D. Ruano. Fast line, arc/circle and leg detection from laser scan data in a player driver. IEEE International Conference on Robotics and Automation. 2005: 3941-3946
97洪伟,田彦涛等.智能机器人系统中局部环境特征的提取.机器人,2003,25(3):264-269
98 J. H. Lee, T. Tsubouchi, K. Yamamoto, S. Egawa. People tracking using a robot in motion with laser range finder. Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2006: 2936-2941
99万琴,王耀南.基于卡尔曼滤波器的运动目标检测与跟踪.湖南大学学报. 2007, 34(3): 36-40
100 M. Kohler. Using the kalman filter to track human interactive motion- modelling and initialization of the kalman filter for translational motion. Informatik VII, University of Dortmund, TR 629, 1997
101 A. Mendes, L. Conde, U. Nunes. Multi-target Detection and Tracking with a Laserscanner. IEEE Intelligent Vehicles Symposium. 2004: 796-801
102李人厚译.自主移动机器人导论.西安交通大学出版社. 2006
103张恒,雷志辉,丁晓华.一种改进的中值滤波算法.中国图象图形学报. 2004, 9(4): 408-411
104刘武发,蒋蓁,龚振邦.基于MEMS加速度传感器的双轴倾角计及其应用.传感器技术. 2005, 24(3): 86-88
105飞思科技产品研发中心.神经网络理论与MATLAB7实现.电子工业出版社. 2006: 100-127
106 V. Vapnik. Statistical learning theory. Wiley. New York, 1998.
107李盼池,许少华.支持向量在模式识别中的核函数特性分析.计算机工程与设计.2005, 26(2):302-304
108 C. W. Hsu, C. J. Lin. A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks. 2002, 13(2): 415-425