旋刀式割草机的改进设计-除草机设计【三维SW】【含CAD图纸、文档全套】【NJ系列】

购买设计请充值后下载，资源目录下的文件所见即所得，都可以点开预览，资料完整，充值下载可得到资源目录里的所有文件。【注】：dwg后缀为CAD图纸，doc,docx为WORD文档，原稿无水印，可编辑。带三维备注的都有三维源文件，由于部分三维子文件较多，店主做了压缩打包，都可以保证打开的，三维预览图都是店主用电脑打开后截图的，具体请见文件预览，有不明白之处，可咨询QQ:1304139763=题目最后备注XX系列，只是店主整理分类，与内容无关，请忽视 塔里木大学毕业设计前 言割草作为草坪修整的一项最基本作业，人们希望使用简单高效经济适用的割草机械来完成草坪修建作业，并轻松完成割草作业任务。同时，要考虑环境保护和可持续发展的要求，设法减轻工作中对环境的影响，尽量降低其噪音污染，不给周围环境造成负面影响。割草的主要作用是保持草坪上草的可控性正常生长，并通过割草机的修剪，保持人们预期的生长高度以及生长形态，使草坪美化环境的功能和其他功能得到充分发挥。目前，大型生态园的绿地设计已是房地产开发团中一个重要环节，草地是绿化的一种重要形式。目前，庭院里修整草地通常使用身背柴油机作动力，采用尼龙绳甩打草叶；或者是使用电动、柴油机的割草机，这种割草机体积大、噪音大，适用于空旷大草坪，易夹草出故障，遇到石块可能使刀片断裂，发生事故。随着国内人们生活水平的提高，人们对居住的绿化水平要求越来越高，尤其带花园的别墅不断增加，更需要开发一种适合小面积草地、简单灵活的手推式割草机。本设计讨论分析了旋刀式割草机机的基础参数，参考已有旋刀式割草机的参数，既要保证工作要求，又要尽量减小噪音污染并提高刀片的时候用寿命。设计出一款经济适用，成本低廉，可大范围推广的割草机械，为人们的居住环境的美化提供良好的实现工具。 2007年IEEE的程序 机器人与仿生学国际会议 2007年12月15日-18，三亚，中国 割草机器人多传感器融合与导航技术的研究 从明和房波 大连理工大学机械工程学院 大连，116024，中国 congmdlut.edu.cn 引言本文提出了一种多传感器系统从超声波传感器和导航相结合的测量机器人割草机。利用传感系统使机器人割草机来映射未知的环境。对于自动割草机器人能在未知的环境中进行定位和导航执行割草任务是很重要的。由于环境的复杂性，简单的一种传感器是不足够割草机器人来完成这些任务。我们开发了一个配有DSPTMS320F2812作为CPU割草机器人。感测系统集成由超声波传感器，红外传感器，碰撞传感器，编码器，一个温度传感器和电子罗盘组成。超声波测距技术变换是基于小波变换的精度高来表示的，以提高超声波传感器测量精度。仿真研究表明，所提出的多传感器信息融合的方法是非常有效的对于导航割草机器人。实验结果表明，该传感系统基于相关的规定障碍检测和定位显示出巨大的潜力，为在动态工作条件下的割草机器人提供一个强大的高性价比的解决方案。 关键词多传感器融合，超声波传感器，割草机机器人，定位，导航。 1.绪论草坪修剪被许多人认为是一个最枯燥，累人的日常任务。首先迫切需要执行的任务是能适应环境的机器人。一些预测表明，割草机器人将是一个最有前途的个人机器人应用，并有重大的市场在世界上。因此，智能化的概念割草机器人（IRM）在1997年度会议的OPEI（ 户外电力会议设备研究所）上第一次提出 1 。该机器人主要面对一般家庭帮助忙碌的人们和乏力的老人们节省支付雇佣劳动力的报酬，同时消除人们来自噪声中，花粉和割草刀片的危害。割草机器人是服务于家庭护理的室外移动机器人，是那种真正的智能机电一体化的环境清理设备 2 3 。最重要的是割草机器人为代表的一些地区覆盖的环保机器人不仅用于室内地面清洁，如 4 也在危险的环境中，例如去地雷，清理辐射点，勘探资源等。与室内移动机器人不同，割草机器人得到很大的挑战。 在整个工作区域内，割草机器人使用传感器来感知环境以及识别他们的实时状态下的环境障碍，地图构建，定位和导航。由于环境的复杂性，一种简单的传感器是不足以让割草机器人来完成这些任务的。因此有必要结合来自不同的传感器上观察到的传感器数据减少机器人在任何工作环境工作的不确定性。为来自各种传感器的信息能合并，传感器鲁棒性和实时性的融合是必需的 5 。在传感器出现误差或失败的情况下，多融合传感器融合也可以减少不确定信息，并提高其可靠性。低成本的传感系统，说明其低功耗，高性能。超声波传感器检测范围是0.3m 5m，他们提供良好的范围信息。然而，环境引起的镜面漫反射是超声波传感器的不确定因素，让他们不具吸引力。红外传感器的检测范围是0.02m 1m，他们可以检测在超声波传感器的盲区的障碍。为了满足割草机器人低成本和高精度的测距技术的需求，在研究超声波测距技术基于高精度的小波分析变换（WT）的数据报道，提高超声波传感器的测量精度。测量数据从传感系统集成，实现规划最佳的，可靠地，完全覆盖整个工作计划的地区，使割草机器人避免未知的障碍。最后，通过仿真研究和实验结果表明该传感系统的导航效果，障碍物检测和割草机器人定位。 2.信息资源管理系统的硬件 IRM采用DSP TMS320F2812作为其CPU，包括四个单元：车辆系统，切割系统，传感系统和控制系统。传感系统是用来收集外部动态信息的工作环境避障，地图构建，导航与定位。它也可以用来检测车辆系统的运动参数和切削机构的工作状况。该控制器将获得的信息与数据库进行比较，然后发出修正后精确的命令让机器人完成任务。信息资源管理的硬件，如图1所示。 IMR硬件概要图1 机器人必须身体强壮，计算速度快，行动准确和安全。它应该有能力，而在全部或大部分的割草期间无需人的干预。IRM由于模块化设计，各单元的管理是相对独立的。模块化设计使维护更容易。IRM任何损坏单元都可以直接取代而不影响其它单元的功能。 3.传感系统A：超声波传感器单元 超声波传感器可以提供良好的范围是基于飞行时间（TOF）信息原理，主要是由于其简单性和成本相对较低，他们已广泛应用于移动机器人的障碍物回避，地图构建等。这种类型的外部传感器能很好测量的障碍物的距离。灵敏度函数的主瓣内包含一个20度角，如图2所示的【6】。大量的试验结果表明，传感器的精度范围为2cm。 图2为超声波传感器的典型的强度分布对于IRM，我们建立了一个传感器阵列由12超声波传感器间隔30度的间隔。超声波信号可以覆盖所有的空间，可以要求哪些机器人检测整个空间的环境信号。用基于TOF的测量的超声换能器的经典技术，计算出的距离最近的反射器利用声音在空气中的速度从发射脉冲到回波到达时间。距离D为反映对象的计算 D =（CT）/ 2 （1）其中C是声音的速度，T是飞行时间。该TOF法产生一系列的值时，回波幅度首次超过临界值后发送，忽略第二回波从进一步的反射。 超声波传感器单元包括一个触发脉冲生成单元，一个多通道选择单元和回声接收单元。传感器接口电路设计发送和接收超声波脉冲，捕获的总是第一个返回的回声。一个对象相关的数据的范围要考虑到即使是位于在锥轴离轴。 超声波频率通常在40和180千赫之间，而在该系统中超声波传感器的频率使用的是40千赫。光束角度是20度。40千赫PWM脉冲是由通用DSP的定时器单元产生的。驱动发射机有效而不带来大的振动，40千赫的超声波一次突发的时间是8周期。当超声波脉冲发射时，传感器将经历“振铃”。振铃引起的由所发送的脉冲可以使接收器检测到一个错误回声。这个不能够捕获解决DSP中断问题，直到延迟间隔已过。这意味着在延迟的时间间隔那测距仪不能检测物体距离该传感器是少于一半的声音传播的距离。这是该超声波传感器的盲区，如图3所示。 图3超声波发射和接收示意图B.：.红外传感器装置和其他传感器 针对超声波传感器的盲区，增加了红外传感器。红外传感器可以检测在20cm内的障碍，这弥补了超声波传感器由于失明问题所造成的区域的问题。 这个单元有16个红外传感器。每个红外搜索器范围有6度，是灵敏度函数主要的圆锥曲线的视图。该传感器具有一个高精度测量范围，有效测量范围是一个目标到一米左右。一些测试表明，该传感器的测距精度在1cm左右。 为了节省DSP的资源，16个红外传感器采用DSP TMS320F2812的数据接口代替IO接口。这种结构也可以同时读取传感器的状态，以确保该系统的时间性能。传感器接口电路用于发送和接收红外脉冲并总是捕获第一个回波来处理其振幅。 割草机器人在室外环境中工作时，其温度变化迅速。温度的变化会影响声音的速度。因此，温度传感器用于保证超声波传感器的精度。碰撞传感器是一组敏感的样本，采用它是为了防止意外的碰撞造成的损害。由于潮湿的环境会危害IRM电路，湿度传感器被引入用于检测湿度环境。虽然这些传感器不完全是一个自主割草机器人机必要的，但他们可以提供有益的功能，让工作更具有效性和安全性。 4.导航技术A. 映射 正如图4所示，基准方向的X定义和机器人的坐标为，。关于内置电子罗盘对于机器人的帮助，角，这是从第一个传感器得来的角度，可容易衡量。实际上，如果只在角（标题的机器人角）的测量，从其他传感器的角度可以发现 角是我们的世界坐标中心。该超声波传感器组的最大环数为n，半径为R（在我们的系统中，n = 12和R = 0.25m）。该环的原点到中心之间的距离是r，并且该向中心的基准角度是。根据参考位置机器人的中心是（，）。这个距离是从原点到通过两个传感器数据检测的二维平面称之为。现在让我们用DMI测量值来分别表示从超声波和红外传感器得到的数据，用于精确距离。这些值之间会有一个误差在这项工作中，我们自然假设是一个均匀随机变量在（W，W）范围内。在这里，W表示最大距离测量误差。这里的问题是，给定的，r ，和，估计占用的坐标细胞和（或等价的）以最有效的方法。涉及检测对象的方程可以写为 图4所示机器人在X-Y段的位置由于对象涉及机器人的方程被写为如果我们定义的位置为：=，然后我们有将插入到中，在这里我们有N个这样的方程。我们把它们矩阵形式如果我们引入新的矩阵，然后（10），可以写为在这里，如果我们进行最小二乘法估计，我们得到因此，我们用最小二乘法估计找到最好的位置。B. 仿真研究基于传感器导航系统已经进行了测试在显示该传感器融合方法的有效性的两种环境分别如图5和图6所示。割草机是一个结构化的实验室初步测试如图5所示。开始在（0.3m，0.5m，0），一个虚拟的机器人在虚拟广场走廊一次。墙在人工环境中是由真正的地图表示的。全车是独立的。它有一个最大的运行速度是0.4米/秒。实验室面积调查出在10cm精度优于1cm为佳。提取映射，提出了一开始的目标。机器人位置和方向是由电子罗盘成立 8 。 图5数据采集与导航在结构化环境中的结果图5中的结果显示的映射质量和该传感器融合方法的有效性。在测试中，我们发现，在估计的位置的平均误差（）在环境中的障碍是在 0.2 ，0.2米范围内。在模拟中，我们看到，在（11）中，实际上应该得到的是不满足。在可以为位置更好的估计的情况下可以表示为在这种情况下，估计角不会改变但估计距离是缩放到它的最佳估计。因此，对于位置，距离估计是和以前一样，而最小二乘估计的作品只对角。仿真结果表明，这种方法产生更精确的结果。 图6仿真结果的墙下行为墙后，被选定为初值问题域是因为它建立一个相当简单的问题评价 9 。它这也奠定了更为复杂的基础的问题领域，如迷宫的穿越，映射和用于草坪修剪和吸尘全覆盖路径规划。墙上的仿真结果图6所示的行为后和实验结果在图6表明，该IRM有能力在非结构化的环境中执行它的割草任务。在图5中传感器的程序导航仿真如下。 5 . 超声波测距技术基于小波变换遗憾的是，由于环境的复杂性和噪声的影响，实际接收到的多回波具有随时间变化的特性，并且是一个典型的非平稳信号。此外，在超声波脉冲回声混合噪声是非高斯白噪声，但噪声，和与目标回波相关。TOF法不能在这样的条件下直接使用。引用广义相关方法估计时间延迟的 10 ，我们把提出了广义自相关方法基于小波变换的时延估计 11 出现在图7。 图7基于小波变换的广义自相关延迟估计 其中（t）是母小波和（t）是女儿小波。该系数是规模（或缩放因素）和（）是时间位移。小波变换的信号x（t）是y（t）。实际上这是一个过滤过程使用大量的带通滤波器的超声回波等于的Q值，这相当于的白化滤波器对广义相关方法的时间延迟的估计，为了消除输入噪声的影响做以下处理。可以找到，作为由于傅里叶变换关系自相关函数之间和他的力谱：我们获得的广义自相关函数是：最后，检测到的峰值来完成TOF的估计和计算实际的超声波速度。 图8嘈杂的超声回波 图9基于小波去噪的回声 图10自相关函数 图11峰值检测 嘈杂的超声回波信号如图8所示，和利用小波变换去噪后的超声回波显示图9。很明显，该噪声混入的超声波回波经WT操作后得到有效地消除作。自动去除噪声的超声波回波的相关运算如图10所示。图11显示了包络线，通过希尔伯特变换。正如我们可以看到，如果每一个峰的横坐标点确定，TOF估计可计算。考虑的超声回波衰减和高精度的要求在实践中的需求，只有前4回波被用来估计TOF。在TOF估计的值是，这是对称于X轴。使用这种方法，估计超声波速度可以计算出来。到目前为止，障碍检测和定位系统成功实现。运用该方法，障碍物检测和定位系统已成功实施。基于广义自相关法小波变换，提出了实现实时超声波速度测量，该方法可以消除温度，湿度和风力的影响，超声波速度测量可以在机器人工作的动态条件下完成。在这种传感系统的基础上，广义自相关方法显示出巨大潜力提供用于割草机器人一个强大的解决方案在动态的工作条件下。 6. 实验结果我们利用超声波传感器测量机器人和平面之间的距离。测量结果和实际距离如表一所示： 表一 超声波传感器的实验数据（单位：厘米）从表一中，我们可以看到，超声波传感器测量误差在3%。然后，基于广义自相关法小波变换，提出了实现实时超声波速度测量。通过上述方法，我们再次测量机器人和平面对象距离的。测量结果与实际距离显示在表二中。 表二 超声波传感器的基于小波变换的数据（单位：厘米）基于小波变换的实验结果表明，使用上述的测量误差技术是小于1% 为5m范围区域内，这种传感系统的障碍物检测和定位拥有巨大的潜力，能作为个强大的解决方案用提高于割草机器人性价比在动态工作条件下。 7. 结论在本文中，我们提出了一个多传感器系统结合超声波传感器测量用于割草机器人导航。该传感系统具有低成本，低功耗，高性能，使割草机器人机能映射未知环境。其有效性是通过仿真研究和实验结果得到的。使用不同种类的传感器集成在传感系统可以克服超声波传感器的盲区和多传感器融合的镜面反射的缺陷。一种高精度超声波测距技术的方法基于小波变换已被引入到改善更多的超声波传感器的测量精度准确的感官信息。该系统应用于割草机器人，证明了实验的可靠性和实时性。今后的工作将着眼于应用所提出的跟踪技术的多传感器融合方案应用于在非结构化环境中的机器人割草机控制全覆盖路径规划 12 。 参考文献Ming Cong and Bo Fang School of Mechanical Engineering, Dalian University of Technology Dalian, 116024, China congm * This work is supported by national natural science fund #50675027to Ming Cong Abstract - This paper presents a multisensor system for combining measurements from ultrasonic sensors and navigation for robot mowers. The proposed sensing system enables robot mowers to mapping unknown environments. It is important for an autonomous robot mower to explore its surroundings in performing the task of localization and navigation for mowing. Because of the complexity of the environment, one simple kind of sensors is not sufficient for robot mower to accomplish these tasks. We develop a robot mower equipped with DSP TMS320F2812 as its CPU. The sensing system integrates with ultrasonic sensors, infrared sensors, collision sensors, encoders, a temperature sensor and an electronic compass. A method of high accuracy ultrasonic ranging technology based on wavelet transform is reported to improve the measurement precision of ultrasonic sensors. Simulation studies show that the proposed multisensor fusion method is very effective for the navigation of robot mowers. Experimental results indicate that this sensing system based on generalized auto-correlation method for obstacle detection and localization shows great potential for providing a high performance-to-price ratio and robust solution for robot mowers in dynamic working condition. Index Terms - multisensor fusion, ultrasonic sensors, robot mower, mapping, navigation I. INTRODUCTION Lawn mowing is considered by many to be one of the most boring and tiring routine tasks. The environmental robots are needed urgently to perform the task. Some predictions indicate that the robot mowers will be one of the most promising personal robot applications and have substantial market in the world. Therefore, the concept of Intelligent Robot Mower (IRM) had been proposed for the first time in 1997s annual conference of the OPEI (Outdoor Power Equipment Institute) 1. The robots mainly face to the general families to help the busy people and the hypodynamic old folks save the payments for hiring labours, also remove people from noise, pollen and danger of mowing blade. The robot mowers serve for home care as the outdoor mobile robots, actually kind of intelligent mechatronics devices for environment clean-up 23. The important thing is that the robot mowers are representative of some area-covering environmental robots used not only for indoor floor cleaning as in 4 but also in hazardous environments such as removing landmines, cleaning up radiant points and prospecting for resources etc. The robot mowers get great challenges differing from indoor mobile robots. The robot mowers use sensors to understand environments as well as their real-time states for obstacle avoidance, map building, location and navigation in the whole work area. Because of the complexity of the environment, one simple kind of sensors is not sufficient for robot mower to accomplish these tasks. It is necessary to combine the observed sensor data coming from different sensors to reduce the uncertainties of the robot in any working environment. To merge the information from the various sensors, robust and real-time sensor fusion is required 5. In cases of sensor error or failure, multisensor fusion can also reduce uncertainty in the information and increase its reliability. A sensing system of low cost, low power consumption, high performance is described. The detecting range of ultrasonic sensors is 0.3m5m, they provide good range information. However, uncertainties in ultrasonic sensors caused by the specular reflection from environments make them less attractive. The detecting range of infrared sensors is 0.02m1m, they can detect the obstacles within the ultrasonic sensors blind zone. In order to satisfy the needs of robot mowers for the low cost and high accuracy ranging technology, the research on the high accuracy ultrasonic ranging technology based on wavelet transform (WT) is reported to improve the measurement precision of ultrasonic sensors. Measurement data gathered from the sensing system are integrated to avoid the robot mower from unknown obstacles and plan an optimum, reliable and realizable plan completely coverage of entire working area. Finally, simulation studies and experimental results show the effectiveness of the sensing system for the navigation, obstacle detection and localization of robot mowers. II. SYSTEM HARDWARE OF IRM The IRM uses DSP TMS320F2812 as its CPU, including four units: vehicle system, cutting system, sensing system and control system. The sensing system is used to collect the external dynamic information of the working environment for obstacle avoidance, map building, navigation and localization. It is also used to detect vehicle systems movement parameters and cutting mechanisms working status. The controller compares the acquired information with the database, and then sends out revisory and accurate command to the robot to perform its tasks. The hardware of the IRM is shown in Fig. 1. Multisensor Fusion and Navigation for Robot Mower * 978-1-4244-1758-2/08/$25.00 2008 IEEE. 417 Proceedings of the 2007 IEEE International Conference on Robotics and Biomimetics December 15 -18, 2007, Sanya, China Fig. 1 Hardware overview of IMR The robot must be physically strong, computationally fast, behaviourally accurate and safety. It should have the ability to perform on its own, and required no human intervention during the whole or most part of the mowing period. The IRM is modularized designed and each unit of the IRM is relatively independent. Modularized design makes the maintenance much easier. Any broken unit of the IRM can be replaced directly without influencing the functions of other units. III. SENSING SYSTEM A. Ultrasonic Sensor Unit Because ultrasonic sensors can provide good range information based on the time of the flight (TOF) principle, mainly due to their simplicity and relatively low cost, they have been widely used in mobile robots for obstacle avoidance, map building and so on. This type of external sensor is very good in obstacles distance measurement. The main lobe of the sensitivity function is contained within an angle of 20 degrees, as shown in Fig. 2 6. A number of tests showed that the range accuracy of the sensors is in the order of 2cm. Fig. 2 Typical intensity distribution of an ultrasonic sensor On IRM, we set up a sensor array which consists of 12 ultrasonic sensors spaced 30 degrees apart. The ultrasonic signals can cover all the space around and satisfy the space requirement about which robot can detect the environmental signals. Classical techniques used in ultrasonic transducers are based on TOF measurement, which calculates the distance of the nearest reflector using the speed of sound in air and the emitted pulse and echo arrival times. The distance d to a reflected object is calculated by ()2dct= (1) where c is the speed of sound, and t is the time-of-flight. The TOF method produces a range value when the echo amplitude first exceeds the threshold level after transmitting, ignoring a second echo from a further reflector. The ultrasonic sensor unit includes a trigger pulse generation unit, a multi-channel selection unit and an echo receiving unit. A sensor interface circuitry designed to send and receive ultrasonic sound pulses catches always the first returning echo. The range data related to an object is considered to be on the conic axes even if it is located off the axes. The ultrasonic wave typically has a frequency between 40 and 180 kHz, and the frequency of the ultrasonic sensors used in the system is 40 kHz. The beam angle is 20 degrees. The 40 kHz PWM pulse is generated by the general-purpose timer unit of DSP. To drive the transmitter effectively and not to bring much vibration, an 8 cycle burst of ultrasound at 40 kHz is sent out once a time. When the ultrasonic pulse is emitted, the sensor will experience “ringing”. Ringing caused by the transmitted pulse can cause the receiver to detect a false echo. This problem is solved by not enabling the capture interrupt of DSP until a delay interval has passed. This means that the ranger can not detect an object whose distance from the sensor is less than half the distance that sound travels during the delay interval. This is the blind zone of the ultrasonic sensor, as shown in Fig. 3. Trigger pulse Emitted signal Received signal TOF Blind zone Echo Fig. 3 The sketch map of ultrasonic transmission and reception B. Infrared Sensor Unit and Other Sensors To overcome the ultrasonic sensors blind zone, infrared sensors are added. The infrared sensors can detect obstacles within 20cm, which patch up the problem caused by the blind zone problem of ultrasonic sensors. This unit has 16 infrared sensors. Each infrared range finder has a conic view of 6 degrees which is the main lobe of the sensitivity function. This sensor has a useful measuring range of a target up to about one meter with high accuracy. A number of tests showed that the range accuracy of the sensors is in the order of lcm. In order to save the DSPs resource, 16 infrared sensors are connected with DSP TMS320F2812s data interface 418 instead of the IO interface. This kind of architecture can also read the sensors status at the same time, ensuring the real- time capability of the system. A sensor interface circuitry designed to send and receive infrared pulses catches always the first retuning echo to process its amplitude. Robot mower works in an outdoor environment, where the temperature changes rapidly. The changing of temperature will affect the speed of sound. Therefore, a temperature sensor is used to guarantee the precision of the ultrasonic sensor. Collision sensor is a group of sensitive swatches, which used to prevent the damage caused by unexpected collision. Because moist environment do harm to the circuit of the IRM, humidity sensors are introduced to detect the humidity of the environment. Although these sensors are not absolutely necessary for an autonomous robot mower, they can provide helpful functions to make the work availability and safety. IV. SENSOR-BASED NAVIGATION A. Mapping As seen in Fig. 4, a reference direction x is defined and the robot coordinates are shown as R x , R y . By the help of an electronic compass built in on the robot 7, the angle i , which is the ith sensors angle from the 1st sensor, can be easily measured. Actually if only the angle S (heading angle of the robot) is measured, other sensor angles can be found as iSi =+ (2) where i is the angle to the our world coordinate center. The number of maximum sensor group on the ultrasonic ring is n, and the radius is r (in our system n=12 and r=0.25m). The distance between the origin and the center of the ring is R, and reference angle to the center is . The reference position of the robots center is ( R x , R y ). The distance from the origin to object which is detected by the ith sensor data on the two dimensional plane is called i R . Now let i dm denote measured value which is combined data from the ultrasonic and infrared sensors, for the exact distance i R . There will be an error i between these values as iii dm d =+. (3) In this work we naturally assume that i is a uniform random variable in the range of (-W, W). Here W denotes the maximum distance measurement error. Here the problem is, given R x , R y , r, 12 , n g34 , and 12 , n dm dm dmg34 , to estimate the coordinates of the occupied cells i x and i y (or equivalently i R ) in most efficient way. The equations involving the detected object can be written as 222 ( )cos()( )sin() iR i i R i i Rxrd yrd=+ + (4) 22 2 ()2()(cos()sin() iii RR rd rdx y=+ + + + 22 2 ( ) 2( )cos( ) ii R R R rd rd =+ + + (5) y x x y R R g455 d d O Fig. 4 The robot position on x-y section The equations involving the robot due to the object can be written as 222 ( )cos()( )sin() iiii i Rxrd yrd=+ + (6) 222 2 ()2()(cos()sin() ii i ii ii i Rxy rd rdx y =+ + + If we define the positions as: 11 , , TT in Ppp p xy=g34 , then we have 22 2 ( ) 2( ) cos( ), sin( ) ii ii ii R R rd rd y P=+ + (7) After the inserting the 2 i R in 2 R , ( ) cos( ) cos(),sin() iiii rd R y P+ = (8) Here again we have n such equations. And we write them in matrix form i mAP= (9) And if we introduce new matrix as () cos(),sin() iiii LP= and 0,0 = , then (10), can be written as 111 1 2 cos( ) ( ) () cos( ) ( ) R nRn nn rdm R L p L rdm R L p + g170g186g170 g186g170g186 g171g187g171 g187g171g187 g171g187g171 g187g171g187 g171g187g171 g187g171g187= g171g187g171 g187g171g187 g171g187g171 g187g171g187 g171g187g171 g187g171g187 + g172g188g172 g188g172g188 Here if we perform the least squares estimate for i P , we obtain 1 ()( ) TT lsq i PAAAm = (11) Thus we find the best squares estimate of the positions. B. Simulation Studies Sensor-based navigation has been tested with simulation to shown the usefulness of this sensor fusion method in the two environments respectively as shown in Fig. 5 and Fig. 6. The mower has been primarily tested in a structured laboratory as shown in Fig. 5. Start at (0.3m, 0.5m, 0degree), a virtual 419 robot was driven around a virtual square corridor one time. The walls in the artificial environment are denoted by the real map. The entire vehicle is self-contained. It has a maximum travel speed on 0.4 m/s. The laboratory area was surveyed out to a 10cm grid with accuracy better than about 1cm. To extract the mapping, a start and goal points were presented. The robot position and orientation were established by the electronic compass 8. Fig. 5 Data collection and navigation result in structured environment The result in Fig. 5 demonstrates the mapping quality and the usefulness of this sensor fusion method. In the tests, we find that the average error ( ) in estimating the position of the obstacles in the environment was in the range of -0.2, 0.2m. In the simulations we see that () lsq i P in (11), obtained does not satisfy () ilsqi RP= which actually should. In the case a better estimate for the positions can be given as ()() () i ei lsqi lsq i R PP P = (12) In this case, estimate for the angle i does not change but the estimate for distance i R is scaled to it best estimate. Therefore for the position, the distance estimate i R remains the same as before, while the least squares estimate works only for the angle i . Simulations show that this way produces more accurate results. Fig. 6 The simulation result of wall-following behavior Wall following was selected for the initial problem domain because it is a fairly simple problem to set up and evaluate 9. It also lays the groundwork for more complex problem domains, such as maze traversal, mapping and complete coverage path planning which is used on lawn mowing and vacuuming. The simulation result of wall- following behavior shown in Fig. 6, and the experimental result in Fig. 6 demonstrate that the IRM have the capability to perform its mowing task in unstructured environment. The program of sensor-based navigation simulation in Fig. 5 is given below. Sub Main Dim PI,Fcr,Fct,X_target,Y_target,X,Y As Single Dim X_grid, Y_grid, i, j, C As Integer Dim Frx,Fry,d, dist_targ, rot, Fx, Fy As Single Dim Fcx,Fcy, Rx,Ry As Single PI=3.1415927 Fcr=1 Fct=1 X_target=GetMarkX(0) Y_target=GetMarkY(0) SetCellSize(0,0.1) Set cell size 10 cm x 10 cm SetTimeStep(0.1) Set simulation time step of 0.1 seconds Do Start main loop X=GetMobotX(0) Present mobot coordinates (in meters) Y=GetMobotY(0) X_grid=CoordToGrid(0,X) indexes of cells where the Y_grid=CoordToGrid(0,Y) mobot center is MeasureRange(0,-1,3) Perform a range scan and update the Certainty Grid (max. cell value=3) Frx=0 Fry=0 Each ocuppied cell inside the windows of 33 x 33 cells applies a repulsive force to the mobot. For i=X_grid-16 To X_grid+16 For j=Y_grid-16 To Y_grid+16 C=GetCell(0,i,j) If C0 Then d=Sqr(X_grid-i)2+(Y_grid-j)2) If d0 Then Frx=Frx+Fcr*C/d2*(X_grid-i)/d Fry=Fry+Fcr*C/d2*(Y_grid-j)/d End If End If Next Next dist_targ=Sqr(X-X_target)2+(Y-Y_target)2) Fcx=Fct*(X_target-X)/dist_targ Fcy=Fct*(Y_target-Y)/dist_targ Rx=Frx+Fcx Ry=Fry+Fcy rot=RotationalDiff(0,X+Rx,Y+Ry) shortest rotational difference between current direction of travel and direction of vector R SetSteering(0,0.5,3*rot)mobot turns into the direction of R at constant speed and steering rate proportional to the rotational difference StepForward Loop Until dist_targ0.1 Loop until mobot reaches the target End Sub 420 V. ULTRASONIC RANGING TECHNOLOGY BASED ON WT Unfortunately, the practical received multi-echoes has time-varying property and is a typical non-stationary signal because the influence of the environmental complexity and the noise. Furthermore, the noise mixed in the ultrasonic pulse- echo is Non-Gaussian white noise but colored noise, and correlated with the target echo. The TOF method can not be used directly in such conditions. Referencing the generalized correlation method for estimation of time delay 10, we put forward the generalized auto-correlation method for estimation of time-of-flight based on wavelet transform 11 and present in Fig. 7. Fig. 7 Delay estimation of generalized auto-correlation based on WT Where ()t is the mother wavelet and () a t is the daughter wavelet. The coefficient is the scale (or scaling factor) and is the time displacement. The wavelet transform of the signal ()x t is ()yt . Actually this is a filtering process of the ultrasonic echo using a multitude of bandpass filters of equal Q , which is equivalent to the whitening filter of the generalized correlation method for estimation of time delay, in order to eliminate the input noise which can influence the following processing. () yy R can be found as ( ) ( ) ( ) ( ) ( ) ( ) yy xx a a R Eyt yt R t t t = As there has the relationship of Fourier transform between auto-correlation function () yy R and his power spectrume: 2 () () ()()() ()() yy yy xx xx GFRG aaG a = We obtain the generalized auto-correlation function as Last, the peak values of () yy R are detected to accomplish the estimation of TOF and calculate the real ultrasonic velocity. Fig. 8 Noisy ultrasonic echo Fig. 9 Denoised echo using WT Fig. 10 Auto-correlation function () yy R Fig. 11 Peak detection The noisy ultrasonic echo is shown in Fig. 8, and the denoised ultrasonic echo by wavelet transform is shown in Fig. 9. It is obvious that the noise mixed in the ultrasonic echo is effectively eliminated after WT operation. The auto- correlation operation () yy R of the denoised ultrasonic echo is shown in Fig. 10. Fig. 11 shows the envelope of () yy R through Hilbert transform. As we can see, if the abscissa of every peak point is determined, the estimation of TOF g109 ND can be calculated. Considered the attenuation of the ultrasonic echo and the demand of the high precision in practice, only the former four echoes are used to estimate the TOF. The values of the TOF estimation are g110g110 g109 g108 g109 g109 3,2, ,2,3DDDDDD , which are symmetrical to the x-axis. Using this method, the estimation of the ultrasonic