Collaborative positioning for swarms:A brief survey of vision,LiDAR and wireless sensors based methods

As positioning sensors,edge computation power,and communication technologies continue to develop,a moving agent can now sense its surroundings and communicate with other agents.By receiving spatial information from both its environment and other agents,an agent can use various methods and sensor types to localize itself.With its high flexibility and robustness,collaborative positioning has become a widely used method in both military and civilian applications.This paper introduces the basic fundamental concepts and applications of collaborative positioning,and reviews recent progress in the field based on camera,LiDAR(Light Detection and Ranging),wireless sensor,and their integration.The paper compares the current methods with respect to their sensor type,summarizes their main paradigms,and analyzes their evaluation experiments.Finally,the paper discusses the main challenges and open issues that require further research.

Review of bio-inspired image sensors for efficient machine vision

With the rapid development of sensor networks,machine vision faces the problem of storing and computing massive data.The human visual system has a very efficient information sense and computation ability,which has enlightening significance for solving the above problems in machine vision.This review aims to comprehensively summarize the latest advances in bio-inspired image sensors that can be used to improve machine-vision processing efficiency.After briefly introducing the research background,the relevant mechanisms of visual information processing in human visual systems are briefly discussed,including layerby-layer processing,sparse coding,and neural adaptation.Subsequently,the cases and performance of image sensors corresponding to various bio-inspired mechanisms are introduced.Finally,the challenges and perspectives of implementing bio-inspired image sensors for efficient machine vision are discussed.

A Selective‑Response Hypersensitive Bio‑Inspired Strain Sensor Enabled by Hysteresis Effect and Parallel Through‑Slits Structures

Flexible strain sensors are promising in sensing minuscule mechanical signals,and thereby widely used in various advanced fields.However,the effective integration of hypersensitivity and highly selective response into one flexible strain sensor remains a huge challenge.Herein,inspired by the hysteresis strategy of the scorpion slit receptor,a bio-inspired flexible strain sensor(BFSS)with parallel through-slit arrays is designed and fabricated.Specifically,BFSS consists of conductive monolayer graphene and viscoelastic styrene–isoprene–styrene block copolymer.Under the synergistic effect of the bio-inspired slit structures and flexible viscoelastic materials,BFSS can achieve both hypersensitivity and highly selective frequency response.Remarkably,the BFSS exhibits a high gage factor of 657.36,and a precise identification of vibration frequencies at a resolution of 0.2 Hz through undergoing different morphological changes to high-frequency vibration and low-frequency vibration.Moreover,the BFSS possesses a wide frequency detection range(103 Hz)and stable durability(1000 cycles).It can sense and recognize vibration signals with different characteristics,including the frequency,amplitude,and waveform.This work,which turns the hysteresis effect into a"treasure,"can provide new design ideas for sensors for potential applications including human–computer interaction and health monitoring of mechanical equipment.

Neuromorphic vision sensors: Principle, progress and perspectives

Conventional frame-based image sensors suffer greatly from high energy consumption and latency.Mimicking neurobiological structures and functionalities of the retina provides a promising way to build a neuromorphic vision sensor with highly efficient image processing.In this review article,we will start with a brief introduction to explain the working mechanism and the challenges of conventional frame-based image sensors,and introduce the structure and functions of biological retina.In the main section,we will overview recent developments in neuromorphic vision sensors,including the silicon retina based on conventional Si CMOS digital technologies,and the neuromorphic vision sensors with the implementation of emerging devices.Finally,we will provide a brief outline of the prospects and outlook for the development of this field.

Progress of Materials and Devices for Neuromorphic Vision Sensors

The latest developments in bio-inspired neuromorphic vision sensors can be summarized in 3 keywords:smaller,faster,and smarter.(1)Smaller:Devices are becoming more compact by integrating previously separated components such as sensors,memory,and processing units.As a prime example,the transition from traditional sensory vision computing to in-sensor vision computing has shown clear benefits,such as simpler circuitry,lower power consumption,and less data redundancy.(2)Swifter:Owing to the nature of physics,smaller and more integrated devices can detect,process,and react to input more quickly.In addition,the methods for sensing and processing optical information using various materials(such as oxide semiconductors)are evolving.(3)Smarter:Owing to these two main research directions,we can expect advanced applications such as adaptive vision sensors,collision sensors,and nociceptive sensors.This review mainly focuses on the recent progress,working mechanisms,image pre-processing techniques,and advanced features of two types of neuromorphic vision sensors based on near-sensor and in-sensor vision computing methodologies.

Vision Sensing-Based Online Correction System for Robotic Weld Grinding

The service cycle and dynamic performance of structural parts are afected by the weld grinding accuracy and surface consistency. Because of reasons such as assembly errors and thermal deformation, the actual track of the robot does not coincide with the theoretical track when the weld is ground ofine, resulting in poor workpiece surface quality. Considering these problems, in this study, a vision sensing-based online correction system for robotic weld grinding was developed. The system mainly included three subsystems: weld feature extraction, grinding, and robot real-time control. The grinding equipment was frst set as a substation for the robot using the WorkVisual software. The input/output (I/O) ports for communication between the robot and the grinding equipment were confgured via the I/O mapping function to enable the robot to control the grinding equipment (start, stop, and speed control). Subsequently, the Ethernet KRL software package was used to write the data interaction structure to realize realtime communication between the robot and the laser vision system. To correct the measurement error caused by the bending deformation of the workpiece, we established a surface profle model of the base material in the weld area using a polynomial ftting algorithm to compensate for the measurement data. The corrected extracted weld width and height errors were reduced by 2.01% and 9.3%, respectively. Online weld seam extraction and correction experiments verifed the efectiveness of the system’s correction function, and the system could control the grinding trajectory error within 0.2 mm. The reliability of the system was verifed through actual weld grinding experiments. The roughness, Ra, could reach 0.504 µm and the average residual height was within 0.21 mm. In this study, we developed a vision sensing-based online correction system for robotic weld grinding with a good correction efect and high robustness.

Selection of Metaheuristic Algorithm to Design Wireless Sensor Network

The deployment of sensor nodes is an important aspect in mobile wireless sensor networks for increasing network performance.The longevity of the networks is mostly determined by the proportion of energy consumed and the sensor nodes’access network.The optimal or ideal positioning of sensors improves the portable sensor networks effectiveness.Coverage and energy usage are mostly determined by successful sensor placement strategies.Nature-inspired algorithms are the most effective solution for short sensor lifetime.The primary objective of work is to conduct a comparative analysis of nature-inspired optimization for wireless sensor networks(WSNs’)maximum network coverage.Moreover,it identifies quantity of installed sensor nodes for the given area.Superiority of algorithm has been identified based on value of optimized energy.The first half of the paper’s literature on nature-inspired algorithms is discussed.Later six metaheuristics algorithms(Grey wolf,Ant lion,Dragonfly,Whale,Moth flame,Sine cosine optimizer)are compared for optimal coverage of WSNs.The simulation outcomes confirm that whale opti-mization algorithm(WOA)gives optimized Energy with improved network coverage with the least number of nodes.This comparison will be helpful for researchers who will use WSNs in their applications.

基于线结构光扫描的烟支表面缺陷检测方法研究

在烟支表面缺陷检测时,为解决传统二维图像方法无法获得深度信息的问题,提出一种基于线结构光传感器旋转扫描的检测方法。烟支在两同向辊轮的旋转带动下实现自转,通过线结构光视觉传感器获取物体轴向轮廓线,使用随机采样一致性拟合轮廓线,通过计算原始点到拟合轮廓的距离判断缺陷点。选取某品牌烟支在检测过程中得到的原始数据,其中多组样本的Kappa值均达到0.97,重复性达到99.3%。研究为烟支缺陷检测提供参考。

基于多维投影时空事件帧的动态视觉传感手势识别

基于视觉的手势识别是虚拟现实、游戏仿真等领域常用的人机交互手段。在实际应用中,手势动作快速变化将导致传统RGB相机或深度相机成像模糊,给手势识别带来巨大挑战。针对上述问题,利用动态视觉传感器捕捉高速手势运动信息,提出一种基于多维投影时空事件帧(spatiotemporal event frame,STEF)的动态视觉数据手势识别方法。将时空信息嵌入到数据投影面融合形成多维投影时空事件帧,克服现有动态视觉信息事件帧表达方法时域信息丢失的局限性,提升动态视觉传感数据的特征表达能力。在此基础上,采用先进的脉冲神经网络对时空事件帧进行分类实现手势识别。在公开数据集上的识别精度达到96.67%,性能优于同类方法,表明该方法可显著提升动态视觉传感数据手势识别准确率。

中厚板多层多道焊视觉测量与工艺规划

针对中厚板多层多道焊的焊道测量问题,提出利用焊缝检测和焊道尺寸视觉测量的信息融合自适应微调焊枪位置的方法。首先基于结构光视觉传感器系统采集焊缝图像,在典型图像处理算法的基础上,结合FROSAC提取算法提取焊缝特征信息;将提取到的特征点进行坐标转换,采用视觉测量获得焊道轮廓和尺寸信息,来修正机器人的运动路径;根据焊缝特征信息分析工艺参数对焊道成型的影响,确定焊道层数、各焊道的工艺参数以及焊枪的偏移量,完成多层多道焊接工艺规划;最后基于搭建的机器人焊接视觉系统在12 mm母材上进行V形坡口多层多道焊接试验。结果表明:该方法下坡口填充良好,焊道尺寸平均测量误差小于0.2 mm,满足多层多道焊接工业应用需求。

视觉传感器在智能网联汽车上的应用分析

在智能网联汽车的发展历程中,视觉传感器赫然出现,成为空前未有的技术革新的灵魂。视觉传感器,为汽车赋予观察周遭环境,理解交通态势,激发无限驾驶提供了保障。反观全球智能网联汽车的发展态势,视觉传感器的进步已然成为推动整车技术创新和研发进步的关键力量。展望未来,随着无人驾驶技术的日益成熟,视觉传感器的作用将越发显著,其理论研究与应用探索,必将影响甚至决定智能网联汽车技术的发展方向与深度。鉴于此,文章深度讨论和探寻视觉传感器在智能网联汽车中的应用,并提出了优化策略,旨在为相关人员工作提供参考。

基于视觉传感器采集光流特征的精神疾病识别方法

目前,精神疾病的筛查主要依靠临床医生的访谈和问卷进行评估,具有高成本、主观性等问题。提出了一种基于视觉传感器采集光流特征的精神疾病识别方法,可以实时监测和预警,以较低的成本和非侵入性的方式来进行精神疾病的辅助筛查。该算法通过视觉传感器全面采集患者的面部数据来提取光流特征,并输入TSMOSNet进行训练。TSMOSNet以TSM为基础网络进行改进,替换普通卷积为光流提取头,针对视觉传感器采集而来的光流特征图进行密集采样,并添加了时序注意力模块、DML蒸馏和VideoMix数据增强等方法,增强了视觉特征,以提高精神疾病识别的准确率。实验结果表明,在H7-BDSN数据集上相比于其他方法,本研究提出的识别算法在精神疾病的筛查任务中取得了最好的效果,准确率为85%,F1分值为0.84。

手卷雪茄在线质量检测技术的研究与应用

为解决手卷雪茄人工质检效率低、成本高问题,设计并实现了基于机器视觉和传感器技术的手卷雪茄在线质量检测系统。该系统首先对雪茄进行激光打标,其次系统通过高速线阵CCD相机动态获取手卷雪茄图像,基于改进的机器视觉方法实时分析雪茄茄衣颜色及瑕疵,基于微波传感器,系统采用谐振法实现手卷雪茄含水量与密度的量测,然后基于CCD激光测微仪,通过对射式LED光幕实现手卷雪茄烟支外径高速测量,最终基于高精度重量传感器实现流水线手卷雪茄的快速称重工序,实现对雪茄质量的在线自动化质检。经过测试,该系统可满足流水线工况下实时、高精度的手卷雪茄质检工作,有效提升质检效率。

针对视觉/惯导系统的异常数据检测算法研究

视觉/惯导航系统(VINS)中传感器异常数据的故障检测(FD)方法对提高系统的定位性能和可靠性至关重要。然而,基于视觉/惯导航系统的异常数据检测与排除方法的研究相对较少。提出一种基于传感器测量残差的异常数据检测算法(VINS-ORFD),通过对相机和IMU异常数据进行主动检测并过滤,提升系统可靠性。基于TUM数据集的测试结果表明,该算法不仅可以实现视觉和IMU传感器异常数据快速检测,还能提升至少22.66%的定位精度(RMSE)。

基于视觉和惯性传感器的大型邮轮室内旅客身份识别方法

邮轮内部结构及场景复杂,基于监控用单目摄像头的旅客身份识别方法缺乏深度信息,无法准确识别旅客位置、航向及航向变化信息,难以在复杂场景下准确识别旅客身份。针对上述问题,提出了基于监控用单目摄像头与手持惯性传感器的大型邮轮室内旅客身份识别方法。根据YOLOv5视觉目标检测算法,提取监控视频帧中旅客的像素坐标与边界框;利用2D-3D坐标转换公式,将相机图像中旅客的像素坐标转换为物理世界中旅客与相机的相对坐标;再基于改进神经网络模型,估计旅客在相机相对坐标系下的航向角。利用旅客智能手机中惯性传感器,采集旅客运动数据,检测旅客加速度的变化,判别旅客行走状态;融合磁场强度,推算旅客在大地坐标系下的真实航向角;融合提取的视觉和惯性传感器数据,对旅客的有限特征及其关系进行编码,包括瞬时时刻行走状态、步长、相对航向角和相对距离,解决传感器信号的误差累积问题;基于构建的2幅多关联图,提出特征之间的相似度计算公式,再利用视觉与惯性传感器特征图匹配(vision and inertial sensors graph matching,VIGM)算法求解最大相似度矩阵,实现对2幅图中的同一旅客的识别。经长江“黄金3号”邮轮大厅、棋牌室、多功能厅和走廊4个场景实验,可以发现:VIGM算法在1~3 s窗口内平均匹配准确率达83.9%,与使用高成本深度相机的ViTag身份匹配算法相比,平均匹配准确率仅相差4.5%。实验结果表明:所提基于摄像头与惯性传感器的旅客身份识别方法及算法实现成本低,但识别效果与使用高成本深度相机的方法相当。

Analyze the Impact of ITS in Improving the Efficiency of Road Tax and Fee Collection Use of Digital Technologies in the City of Bujumbura

Road transport is currently one of the most important sectors affecting sustainable development and the improvement of the population’s standard of living. In some sub-Saharan African countries, including Burundi, the transport structure is vulnerable, under attack, or even damaged or destroyed. This is prompting decision-makers to look for every possible way to enable dynamic management of the road system, as well as the collection of tax revenues attributable to this sector. To reach this stage, we postulate that the introduction of the Intelligent Transport System (ITS) into the road tax and fee collection process would make a significant contribution (road safety, zero cash on silk Safety Officers, payment of a fine, eradication of road corruption etc.) to the digitization of the various transport sectors. As far as the city of Bujumbura is concerned (our field of intervention), the applicability of the present System could thus meet the expectations of the decision-maker, certain drivers and, by the same token, contribute to the promotion of Digital Technology in Burundi.

基于虚拟传感器的结构位移视觉测量误差抑制方法

温度、热浪和振动等环境因素给结构位移监测造成不可忽视的误差。虽然利用静止标志物降低视觉测量误差的原理可行,但是其适用范围却因背景标志物不易获得和控制而受到很大限制。利用视觉测量系统的现有条件,在近场布置主动光源作为误差检测虚拟传感器,摆脱对天然标志物的依赖。针对焦外成像产生的弥散圆等伴生问题,采用图像滤波、伽马变换、图像质量评估、图像二值化、边缘检测和目标检测等算法,形成一套面向弥散圆光斑的自适应图像增强算法;利用虚拟传感器的虚假位移,实现结构位移测量误差补偿,开展了仿真实验和室外结构位移视觉测量实验。研究结果表明:该方法可以显著降低结构位移的初始误差,残留误差满足测量精度要求。

基于激光视觉传感的布局规划方法研究

对于激光跟踪测量任务来说,测量场规划结果十分关键,设计基于激光视觉传感的布局规划方法,以满足实际应用需求。基于激光视觉传感技术设计激光视觉传感器,由相机、滤光片、激光器构成,用于采集测量场图像。对于采集图像,使用自适应中值滤波器对其实施去噪处理。设计应用规划引擎与CAM2的布局规划算法,在多次迭代中,使用粒子群算法求解,通过该算法实现测量场的布局规划。测试结果表明,设计方法两个站位的布局规划结果是互补的,说明该布局规划能够完成测量任务,规划结果有效。

激光雷达和视觉传感器组合系统的在线校准方法

针对激光雷达和视觉传感器在车辆行驶过程中因振动发生漂移的问题,提出一种组合系统的在线校准方法。基于激光点云和图像的边缘匹配原理进行数据实时检测,检测到融合的传感器数据不准确时对其采取紧急措施,以提高车辆行驶安全性。针对未检测出错误的校准参数,采用梯度下降法优化成本函数,获取新的校准参数,实现传感器数据校正。通过数据集KITTI进行多次参数偏差实验,测试表明,算法可以检测较大的错误校准且能校正较小范围内的平移和旋转误差。

基于视觉-光电传感器的光伏板清洁机器人研究

我国南方湿地及丘陵地区太阳能资源丰富,但路面条件差,光伏板安装位置高,导致灰尘和鸟粪等污染物难以清理,影响光伏电站的经济性和安全性.为解决地面及空间复杂的光伏电站的清洁难题,设计了一种基于视觉-光电传感器的光伏板清洁机器人,分析倾斜光伏板上机器人的附着条件和所需吸力.根据视觉传感器和光电传感器提供的信息,控制机器人自适应路径规划并执行清洁策略,实现光伏板表面灰尘的全面清洁和鸟粪的定位清洁.试验结果表明,光伏板清洁机器人的清洁效率为50 m^(2)/h,鸟粪的平均定位误差为1.38 mm,除尘率为91.16%,有利于促进太阳能光伏发电的提质增效.