红外传感器在教育机器人避障电路设计中的应用

红外传感器在家用电器遥控和机器人避障方面有着广泛的应用,笔者根据红外传感器的这一特性设计出一款教育机器人避障电路,介绍了红外传感器在机器人避障电路中的应用,给出红外传感器与单片机系统的硬件接口电路和避障软件的编程思路,并通过测试,得出了影响红外传感器避障的相关因素,并提出了改进的方法和措施.     

传感器在多关节机器人系统实时避障中的应用

评述近几年来在多关节机器人系统实时避障中传感器技术的应用，详细介绍了传 感器的选择及多传感器信息融合技术，并指出这一领域中值得进一步研究的一些问题和可能 的发展方向．     

多传感器信息融合在机器人避障中的应用

本文在移动机器人的避障系统中,充分利用数字信号处理器(DSP)超强的运算能力,以及单片机(MCU)善于控制的特点,构造了DSP+MCU的双CPU系统,并且利用粗糙集理论进行多传感器信息融合,实现了移动智能机器人在不确定环境中实时获取外部信息,快速地实现避障.     

基于声纳环传感器的机器人避障研究

任何一种移动机器人要实现未知环境自主导航都必须有效而可靠地感知环境信息,而超声波传感器在检测障碍物距离信息方面应用十分广泛。介绍了旅行家II号声纳环传感系统的设计与实现原理,并对声纳的精度进行了测试。在此基础上,提出了移动机器人一种简单避障策略,并运用2种基本避障实验:静态避障和动态避障,验证了该避障策略的正确性和有效性。     

热释电红外传感器在防盗系统中的应用

介绍一种自动防盗报警系统。该系统由热释电红外传感器、电话和检控电路组成。若有人体在警戒范围内移动 ,红外传感器检测出信号 ,检控电路使电话自动拨号 (如 110 ) ,并通过其中的语言电路报告现场地址。     

基于多超声波传感器避障机器人小车的设计

本文介绍了采用多超声波传感器的避障小车的设计与实现.通过多个超声波发送脉冲检测与障碍物间的距离,控制方向舵机进行转向,实现小车的避障功能.小车采用前轮舵机转向,后轮H桥驱动电路和齿轮减速驱动的方式,以Arduino Mega ADK控制板作为控制核心,进行了软硬件系统的设计,搭建出自动避障小车平台,取得了良好的实验效果.     

基于AVR单片机的智能车避障系统的实现

本设计方案以ATmega128为系统的核心,借助超声波传感器,提出一种超声波三角形测距定位的方法,结合模糊控制理论和PI控制算法,设计出一种模糊-PI双模控制器,用于控制智能车,实现了智能车户外智能避障,高速稳定运行。     

自动避障和火焰搜索在智能灭火机器人中的实现

针对目前已有的硬件条件,从硬件和软件两方面描述了自动避障和火焰搜索在智能灭火机器人中的实现方法.现场运行实验表明,该系统能够较好地实现灭火机器人的自动避障和火焰搜索.     

基于单片机的智能车避障的实现

主要介绍了一种具有避障功能的智能小车的设计方案。该方案以AT89S52为整个系统的核心,使用不同频率的两种红外对管进行避障,用红外LED发射管在障碍区外形成一个隐形的安全区,850nm红外和940nm的红外发射二极管均匀分布在障碍区圆上,通过单片机处理红外对管传输过来的数据从而实现智能控制,达到避障的目的。     

基于多传感器信息融合的智能小车避障

针对智能小车在不确定坏境下自主避障的情况,采用超声波传感器和红外传感器相结合来感知外界环境信息。将传感器采集到的各种数据利用T-S模糊神经网络进行融合．通过实验仿真表明：此方法能够使智能小车对障碍物灵活避障．     

基于改进人工势场法的智能无人车路径规划仿真研究

传统的人工势场法由于存在局部极小值问题,使智能无人车无法到达目标点。本文提出一种角度偏移的改进人工势场方法来进行避障的路径规划。介绍传统人工势场模型,详细介绍改进人工势场方法,并且对改进人工势场法进行仿真,实验证明方法的有效性。     

面向无人艇的T-DQN智能避障算法研究

无人艇(Unmanned surface vehicle, USV)作为一种具有广泛应用前景的无人系统, 其自主决策能力尤为关键. 由于水面运动环境较为开阔, 传统避障决策算法难以在量化规则下自主规划最优路线, 而一般强化学习方法在大范围复杂环境下难以快速收敛. 针对这些问题, 提出一种基于阈值的深度Q网络避障算法(Threshold deep Q network, T-DQN), 在深度Q网络(Deep Q network, DQN)基础上增加长短期记忆网络(Long short-term memory, LSTM)来保存训练信息, 并设定经验回放池阈值加速算法的收敛. 通过在不同尺度的栅格环境中进行实验仿真, 实验结果表明, T-DQN算法能快速地收敛到最优路径, 其整体收敛步数相比Q-learning算法和DQN算法, 分别减少69.1%和24.8%, 引入的阈值筛选机制使整体收敛步数降低41.1%. 在Unity 3D强化学习仿真平台, 验证了复杂地图场景下的避障任务完成情况, 实验结果表明, 该算法能实现无人艇的精细化避障和智能安全行驶.     

Abstracting Vehicle Shape and Kinematic Constraints from Obstacle Avoidance Methods

Most obstacle avoidance techniques do not take into account vehicle shape and kinematic constraints. They assume a punctual and omnidirectional vehicle and thus they are doomed to rely on approximations when used on real vehicles. Our main contribution is a framework to consider shape and kinematics together in an exact manner in the obstacle avoidance process, by abstracting these constraints from the avoidance method usage. Our approach can be applied to many non-holonomic vehicles with arbitrary shape. For these vehicles, the configuration space is three-dimensional, while the control space is two-dimensional. The main idea is to construct (centred on the robot at any time) the two-dimensional manifold of the configuration space that is defined by elementary circular paths. This manifold contains all the configurations that can be attained at each step of the obstacle avoidance and is thus general for all methods. Another important contribution of the paper is the exact calculus of the obstacle representation in this manifold for any robot shape (i.e. the configuration regions in collision). Finally, we propose a change of coordinates of this manifold so that the elementary paths become straight lines. Therefore, the three-dimensional obstacle avoidance problem with kinematic constraints is transformed into the simple obstacle avoidance problem for a point moving in a two-dimensional space without any kinematic restriction (the usual approximation in obstacle avoidance). Thus, existing avoidance techniques become applicable. The relevance of this proposal is to improve the domain of applicability of a wide range of obstacle avoidance methods. We validated the technique by integrating two avoidance methods in our framework and performing tests in the real robot. Javier Minguez received the physics science degree in 1996 from the Universidad Complutense de Madrid, Madrid, Spain, and the Ph.D. degree in computer science and systems engineering in 2002 from the University of Zaragoza, Zaragoza, Spain. During his student period, in 1999 he was a research visitor in the Robotics and Artificial Intelligence Group, LAASCNRS, Toulouse, France. In 2000, he visited the Robot and ComputerVision Laboratory (ISR-IST), Technical University of Lisbon, Lisbon, Portugal. In 2001, he was with the Robotics Laboratory, Stanford University, Stanford, USA. He is currently a fulltime Researcher in the Robot, Vision, and Real Time Group, in the University of Zaragoza. His research interests are obstacle avoidance, motion estimation and sensor-based motion systems for mobile robots. Luis Montano was born on September 6, 1958 in Huesca, Spain. He received the industrial engineering degree in 1981 and the PhD degree in 1987 from the University of Zaragoza, Spain. He is an Associate Professor of Systems Engineering and Automatic Control at the University of Zaragoza (Spain). He has been Head of the Computer Science and Systems Engineering Department of the University of Zaragoza. Currently he is the coordinator of the Production Technologies Research in the Aragon Institute of Engineering Research and of the Robotics, Perception and Real Time group of the University of Zaragoza. He is principal researcher in robotic projects and his major research interests are mobile robot navigation and cooperative robots. José Santos-Victor received the PhD degree in Electrical and Computer Engineering in 1995 from Instituto Superior Técnico (IST - Lisbon, Portugal), in the area of Computer Vision and Robotics. He is an Associate Professor at the Department of Electrical and Computer Engineering of IST and a researcher of the Institute of Systems and Robotics (ISR), at the Computer and Robot Vision Lab - VisLab. (http://vislab.isr.ist.utl.pt) He is the scientific responsible for the participation of IST in various European and National research projects in the areas of Computer Vision and Robotics. His research interests are in the areas of Computer and Robot Vision, particularly in the relationship between visual perception and the control of action, biologically inspired vision and robotics, cognitive vision and visual controlled (land, air and underwater) mobile robots. Prof. Santos-Victor is an IEEE member and an Associated Editor of the IEEE Transactions on Robotics.     

基于Jetson和超声传感器的清洁车避障方法研究

为解决清洁车普遍存在的清扫盘和喷水杆容易因触碰路肩造成损失的问题,提出了一种基于Jetson和超声传感器的清洁车路肩距离检测及避障方法。该方法采用超声传感器检测清洁车清扫盘到路肩的距离,以Jetson AGX Xavier作为控制核心。Jetson控制器对超声传感器检测到的距离信息进行处理,生成控制指令,通过CAN总线将控制指令传送到车载CAN设备控制器,控制清扫盘和喷水杆收放来减少损失。行车实验表明,所提出的检测方法和系统可以有效避免清洁车离路肩过近,减少清扫盘和喷水杆与路肩的碰撞。     

一种语音控制的自主循迹与避障的智能小车的设计

智能小车以SPCE061A单片机为控制核心,采用反射性光电探测器对白纸上的黑色路径进行探测,按照预定的路径行驶。借助SPCE061A的语音特色,采用语音控制和中断定时控制相结合的方法,实现了通过语音对小车进行控制。实验结果表明,小车工作稳定,能够自主循迹与避障。     

一种语音控制的自主寻迹与避障智能小车设计

智能小车以STC12C5A60S2单片机为控制核心,采用反射型光电探测器RPR-220对白纸中的黑色路径进行探测,按照预定的路径行驶,小车运用短距离无线通信模块NRF24L01将行驶状况传输给计算机;计算机运行VB软件设计的人机交互接口软件显示小车行驶状况,它还可通过微软的Speech SDK5.3识别操作者发出的语音指令,对小车进行控制。实验结果表明小车工作稳定,能够自主寻迹与避障,操作者可以遥控小车的行驶速度与方向。     

基于分层模糊控制的自主车辆避障仿真

针对自主车辆在避障中所使用单层模糊逻辑控制器输入、输出变量多而导致模糊规则难以详细划分的问题,提出了将车辆避障过程划分为车辆绕开障碍物过程和车辆趋向目标过程的新方法。建立了Matlab仿真环境下精确的车辆运动学模型,并根据人类驾驶经验制定了详细的模糊控制规则,以达到理想的避障效果。仿真结果表明,该算法计算量小、运算速度快、精度高,可以满足车辆避障时的系统要求,也具有一定的工程实用价值,为下一步精确控制奠定了基础。     

基于模糊控制的超声波避障方法的研究

依据移动机器人上超声波传感器的布置和分组情况,将反映移动机器人当前感知环境的期望类别进行了概括。在此基础上分析了移动机器人的模糊避障原理,并建立了一种基于人的驾驶经验的模糊逻辑控制的路障躲避方法。通过对人的驾驶经验的分析,针对移动机器人建立了路障躲避的模糊规则,并给出了输入与输出变量的隶属度函数。在Simulink中模拟机器人行驶的环境,建立相应的模型对系统进行仿真,结果表明移动机器人能实现自主行驶。