基于CCD的循迹机器人小车模糊控制方法研究与实现

设计一种基于STM32为主控芯片的循迹机器人小车,用CCD摄像头作为路径识别装置,采用边缘检测算法提取路径中线,根据小车的速度与转弯半径,采用模糊控制调节小车左右轮的速度差,达到路径识别与跟踪的目的。设计中通过建立里程计模型把小车的实时理想位置通过蓝牙传送至PC端,PC端通过数据处理将理论轨迹和实际运行轨迹进行对比,实验结果表明,小车可以依靠CCD摄像头很好地识别任意路径轨迹,在不同的运行速度下,模糊控制都有较理想的循迹控制效果。     

感应式循迹小车的设计与实现

传统的循迹小车采用光电传感器作路面轨道检测,其工作可靠性受环境光线的影响很大,实际运行中经常要根据环境光线的变化对传感器的灵敏度进行调整。提出一种感应式循迹小车的设计方法,用金属铝箔胶带代替黑色轨道线,在小车上设置多只金属感应传感器,基于感应的方法来检测铝箔胶带路线的位置,把检测的结果送单片机处理,再由单片机输出相应控制信号驱动小车运行。所提出的方法能够完全消除环境光线对循迹小车的干扰,提高小车运行的可靠性。样品小车的测试结果表明,基于新方法设计的小车运行平稳,在长时间工作中没有出现脱轨现象,小车的整体性能良好。     

基于51单片机居家智能灭火机器人循迹系统的设计

本系统的设计是使小车在预定轨道上按要求稳定、准确地运行,采用了单片机AT89C51单片机微处理器作为小车的检测和控制系统。通过红外发射和接收一体探头传感器检测路面黑色循迹线,使小车按预定轨道行驶,通过步进电机扫描系统对路径进行扫描,最终实现了小车按预定轨道行驶。     

基于LDC1000电感传感器单片机循迹小车

本设计是基于LDC1000电感传感器单片机控制的简易自动寻迹小车系统,旨在设计出一款可以按照预设的轨迹行走.控制系统以STC12C5A60S2、MSP430F5529为控制核心,用单片机产生PWM波,控制小车速度.利用LDC1000对路面铁丝轨迹进行检测,并确定小车当前的位置状态,单片机对采集到的信号予以分析判断,及时控制驱动电机以调整小车转向,从而使小车能够沿着铁丝轨迹自动行驶,实现小车自动寻迹的目的.     

电动小车的循迹

在2001、2003年连续两届全国大学生电子设计竞赛中,均出现了简易智能小车这种集光、机、电于一体的题目。本文具体介绍一下小车如何正确地进行循迹并给出必要的源代码。1.小车控制及驱动单元的选择此部分是整个小车的大脑,是整个小车运行的核心部件,起着控制小车所有运行状态的     

基于红外反射式智能循迹遥控小车系统设计

介绍了一种采用STC89C52、L298N和TCRT5000设计的智能循迹和红外遥控的小车.智能循迹采用红外传感器检测路面信息,传递给单片机自动分析处理,最后控制电机调节小车按预定轨道平稳行驶.红外遥控部分是手动模式,单片机解码遥控器发出的指令,控制电机操纵小车.液晶显示模块使操作更加简单、智能、人性化.实践表明,小车能够准确实现沿黑线轨道平稳行驶和接收遥控器指令.     

基于红外传感器ST188的自动循迹小车设计

设计了一种以红外传感器ST188、AT89S51为控制核心的自动循迹小车,系统采用单片机AT89S51产生PWM波调控小车速度,红外传感器ST188对路面黑色轨迹进行检测,并将检测到的信号反馈给微控系统AT89S51,AT89S51由采集到的信号发出指令,控制小车电机驱动电路以调整行驶方向,从而使小车能够沿着黑色轨迹自动行驶,实现小车自动循迹的目的.     

基于Arduino的智能循迹小车的设计与实现

以Arduino为主控芯片,以STM32和LDC1000电感数字传感器为小车循迹模块,设计与制作了一个可自动循迹的小车.整个小车系统分为采集、处理、控制三部分:首先,采用LDC1000采集铁丝轨迹信息并将其转化为数字信号传输给数据处理模块;然后,以STM32作为系统处理模块,根据设计的算法处理输入的数据后,再输出不同的...     

基于LM393控制的太阳能循迹小车设计与实现

现有的循迹小车一般采用的是红外反射方式反馈小车的行驶轨迹,控制电路结构较为复杂,且成本较高,在小车行驶轨迹一定程度上容易出现偏差。采用LM393电压比较器设计的能在特定跑道上循迹行驶的轮式智能小车,该循迹小车主要包括太阳能供电部分和循迹控制部分。控制电路部分主要包括蓄电池、开关、第一和第二光敏感应电路、直流电机、比较电路和电机驱动电路。试验样机测试表明,以LM393电压比较器为核心控制的循迹小车具有控制精度高、起停快等特点,解决了循迹小车的控制电路结构复杂、行驶轨迹偏差较大的问题。     

单片机应用——智能循迹小车设计

由单片机控制的自动循迹小车能够自动识别轨迹线形,实现自动转弯、前进。     

基于单片机的智能循迹小车设计

智能循迹汽车是汽车电子、人工智能、机械制造等多种学科领域的结合体,具有较高的应用价值.智能循迹小车运用单片机为基础进行设计,同时利用传感器进行识别赛道的信息,接着在利用传感器检测智能车的加速度及其速度,实现快速稳定的循迹进行行驶.智能汽车研究也越来越受到关注,智能汽车的适应能力较强,可按照事先的轨道进行自行运作,不需要路径管理,即可完成预期目标.在科技发展迅猛的现代化社会,汽车普及率也已经处于高峰状态,不少汽车制造商提出无人驾驶的概念,比如:特斯拉,因此设计具有很高科研价值.     

基于CCD摄像头智能车分段PID控制算法设计

介绍了基于MC9S12XS128单片机控制的智能车系统,该系统以CCD摄像头传感器作为路径识别装置,通过图像识别提取道路黑线信息,计算出反应道路形状的舵机控制量,对舵机进行控制。对智能车寻线舵机控制系统的软件设计思路和控制算法思想等进行了详细的论述。测试结果表明智能车能准确稳定地跟踪引导黑线行驶,该算法能够很好地对智能车进行控制。     

基于角点增强改进的TLD目标跟踪算法

针对TLD算法的特征点无法有效表述目标问题,提出了一种基于角点增强改进的TLD目标跟踪算法。改进算法在跟踪模块加入了对目标表述能力更强,具有光照不敏感性和旋转不变性的Shi-Tomas角点作为跟踪特征点。跟踪器运行时,在角点经光流法跟踪和双向误差检测后,利用剩余的稳定角点定位目标窗口。对照结果表明,改进算法在面对目标抖动和形变时可以稳定跟踪;有效抑制因跟踪平滑点造成的漂移现象;提高了跟踪的稳定性。针对TLD算法跟踪过程中因在线模板积累造成的计算量持续增大、实时性持续降低的问题,提出了一种依据相似度中值的模板判断删除机制。该删除机制在模板积累到设定阈值时运行,根据模板与当前目标的相似度,删除不再具备代表性的模板;调整模板空间并更新模板数目。实验表明,该删除机制在应对模板更新快、持续时间长的跟踪情景时有效降低算法计算量,实时性可提高约20%。     

Tree-based object tracking without mobility statistics in wireless sensor networks

Object tracking in wireless sensor networks is to track mobile objects by scattered sensors. These sensors are typically organized into a tree to deliver report messages upon detecting object’s move. Existing tree construction algorithms all require a mobility profile that characterizes the movement statistics of the target object. Mobility profiles are generally obtained based on historical running traces. The contribution of this work is twofold. We first show that the problem of finding an optimal message report tree that requires the least amount of report messages is NP-hard. We then propose analytic estimates of mobility profiles based on Markov-chain model. This profiling replaces an otherwise experimental process that generates and analyzes running traces. Simulation results show that the analytic profiling works well and can replace costly statistical profiling without noticeable performance degradation.     

Zero-error target tracking with limited communication

We study the problem of target tracking in a sensor network environment. In particular, we consider a target that moves according to a Markov chain, and a tracker that queries sets of sensors to obtain tracking information. We are interested in finding the minimum number of queries per time step such that a target is trackable under three different requirements. First we investigate the case where the tracker is required to know the exact location of the target at each time step. We then relax this requirement and explore the case where the tracker may lose track of the target at a given time step, but it is able to ";catch-up"; at a later time, regaining up-to-date information about the target's track. Finally, we consider the case where tracking information is only known after a delay of d time steps. We provide necessary and sufficient conditions on the number of queries per time step to track in the above three cases. These conditions are stated in terms of the entropy rate of the target's Markov chain.     

基于AT89S52的智能简易车辆系统设计

介绍了以单片机AT89S52为控制核心的智能简易车辆系统设计方案。系统根据角度传感器检测的信号,控制小车在跷跷板上做相应运动,其中采用角度传感器感应板面水平角是否为零,确保小车达到平衡。同时采用反射式光电传感器感应板面引导线,保证小车不脱离轨道,并指引小车在规定区域内顺利爬上板面。系统显示装置采用RT1602液晶显示器,能够实时显示行距、时间;小车动力源采用步进电机,并用STA475集成晶体管芯片构成的桥式电路对其驱动,电源直接由12 V蓄电池供电,单片机根据各模块检测的信号作出判断,保证小车正常行动。     

Level set analysis for leukocyte detection and tracking

We propose a cell detection and tracking solution using image-level sets computed via threshold decomposition. In contrast to existing methods where manual initialization is required to track individual cells, the proposed approach can automatically identify and track multiple cells by exploiting the shape and intensity characteristics of the cells. The capture of the cell boundary is considered as an evolution of a closed curve that maximizes image gradient along the curve enclosing a homogeneous region. An energy functional dependent upon the gradient magnitude along the cell boundary, the region homogeneity within the cell boundary and the spatial overlap of the detected cells is minimized using a variational approach. For tracking between frames, this energy functional is modified considering the spatial and shape consistency of a cell as it moves in the video sequence. The integrated energy functional complements shape-based segmentation with a spatial consistency based tracking technique. We demonstrate that an acceptable, expedient solution of the energy functional is possible through a search of the image-level lines: boundaries of connected components within the level sets obtained by threshold decomposition. The level set analysis can also capture multiple cells in a single frame rather than iteratively computing a single active contour for each individual cell. Results of cell detection using the energy functional approach and the level set approach are presented along with the associated processing time. Results of successful tracking of rolling leukocytes from a number of digital video sequences are reported and compared with the results from a correlation tracking scheme.     

Robust observation model for visual tracking in particle filter

A robust observation model for visual tracking is proposed in this paper. The model consists of three appearance models: fixed appearance model, adaptive appearance model, and two-frame appearance model. The three appearance models are used, respectively, for catching unchanged components, slow changes, and rapid changes in object appearance. During tracking, the robust observation model is incorporated in a particle filter, and the particle filter can automatically select proper appearance models to track object according to the current tracking situation. Occlusion analysis is implemented using the M-estimation technique. Experimental results demonstrate that the proposed algorithm can track objects well under many challenging tracking situations.