Mobile robot localization algorithm based on multi-sensor information fusion

In order to effectively reduce the uncertainty error of mobile robot localization with a single sensor and improve the accuracy and robustness of robot localization and mapping,a mobile robot localization algorithm based on multi-sensor information fusion(MSIF)was proposed.In this paper,simultaneous localization and mapping(SLAM)was realized on the basis of laser Rao-Blackwellized particle filter(RBPF)-SLAM algorithm and graph-based optimization theory was used to constrain and optimize the pose estimation results of Monte Carlo localization.The feature point extraction and quadrilateral closed loop matching algorithm based on oriented FAST and rotated BRIEF(ORB)were improved aiming at the problems of generous calculation and low tracking accuracy in visual information processing by means of the three-dimensional(3D)point feature in binocular visual reconstruction environment.Factor graph model was used for the information fusion under the maximum posterior probability criterion for laser RBPF-SLAM localization and binocular visual localization.The results of simulation and experiment indicate that localization accuracy of the above-mentioned method is higher than that of traditional RBPF-SLAM algorithm and general improved algorithms,and the effectiveness and usefulness of the proposed method are verified.

Object-tracking robot using ultrasonic sensor and servo motor

This paper proposes a method that rotation angle of servo motor and distance values of ultrasonic sensor are used for tracking an object in real-time while the robot keeps regular distance.Object detection distance widens by using ultrasonic sensors and object recognition,and movement of robot is controlled by angle of servo motor and distance of ultrasonic sensors.Not adopting the existing tracking methods:camera,laser-infrared(LRF)and many ultrasonic sensors,the proposed method proves that it is possible to track object using ultrasonic sensor and servo motor.Trajectory of robot is represented and analysed according to movement of object in limited conditions.

Localized Coverage Connectivity Based on Shape and Area Using Mobile Sensor Robots in Wireless Sensor Networks

A wireless sensor network (WSN) is spatially distributing independent sensors to monitor physical and environmental characteristics such as temperature, sound, pressure and also provides different applications such as battlefield inspection and biological detection. The Constrained Motion and Sensor (CMS) Model represents the features and explain k-step reach ability testing to describe the states. The description and calculation based on CMS model does not solve the problem in mobile robots. The ADD framework based on monitoring radio measurements creates a threshold. But the methods are not effective in dynamic coverage of complex environment. In this paper, a Localized Coverage based on Shape and Area Detection (LCSAD) Framework is developed to increase the dynamic coverage using mobile robots. To facilitate the measurement in mobile robots, two algorithms are designed to identify the coverage area, (i.e.,) the area of a coverage hole or not. The two algorithms are Localized Geometric Voronoi Hexagon (LGVH) and Acquaintance Area Hexagon (AAH). LGVH senses all the shapes and it is simple to show all the boundary area nodes. AAH based algorithm simply takes directional information by locating the area of local and global convex points of coverage area. Both these algorithms are applied to WSN of random topologies. The simulation result shows that the proposed LCSAD framework attains minimal energy utilization, lesser waiting time, and also achieves higher scalability, throughput, delivery rate and 8% maximal coverage connectivity in sensor network compared to state-of-art works.

Investigation on mobile robot navigation based on Kinect sensor

Mobile robot navigation in unknown environment is an advanced research hotspot.Simultaneous localization and mapping(SLAM)is the key requirement for mobile robot to accomplish navigation.Recently,many researchers study SLAM by using laser scanners,sonar,camera,etc.This paper proposes a method that consists of a Kinect sensor along with a normal laptop to control a small mobile robot for collecting information and building a global map of an unknown environment on a remote workstation.The information(depth data)is communicated wirelessly.Gmapping(a highly efficient Rao-Blackwellized particle filer to learn grid maps from laser range data)parameters have been optimized to improve the accuracy of the map generation and the laser scan.Experiment is performed on Turtlebot to verify the effectiveness of the proposed method.

Swarm intelligence based dynamic obstacle avoidance for mobile robots under unknown environment using WSN

To solve dynamic obstacle avoidance problems, a novel algorithm was put forward with the advantages of wireless sensor network (WSN). In view of moving velocity and direction of both the obstacles and robots, a mathematic model was built based on the exposure model, exposure direction and critical speeds of sensors. Ant colony optimization (ACO) algorithm based on bionic swarm intelligence was used for solution of the multi-objective optimization. Energy consumption and topology of the WSN were also discussed. A practical implementation with real WSN and real mobile robots were carried out. In environment with multiple obstacles, the convergence curve of the shortest path length shows that as iterative generation grows, the length of the shortest path decreases and finally reaches a stable and optimal value. Comparisons show that using sensor information fusion can greatly improve the accuracy in comparison with single sensor. The successful path of robots without collision validates the efficiency, stability and accuracy of the proposed algorithm, which is proved to be better than tradition genetic algorithm (GA) for dynamic obstacle avoidance in real time.

Design of dead reckoning system for mobile robot

A dead reckoning system for a wheeled mobile robot was designed, and the method for robot’s pose estimation in the 3D environments was presented on the basis of its rigid-body kinematic equations. After analyzing the locomotion architecture of mobile robot and the principle of proprioceptive sensors, the kinematics model of mobile robot was built to realize the relative localization. Considering that the research on dead reckoning of mobile robot was confined to the 2 dimensional planes, the locomotion of mobile robot in the 3 coordinate axis direction was thought over in order to estimate its pose on uneven terrain. Because the computing method in a plane is rather mature, the calculation in height direction is emphatically represented as a key issue. With experimental results obtained by simulation program and robot platform, the position of mobile robot can be reliably estimated and the localization precision can be effectively improved, so the effectiveness of this dead reckoning system is demonstrated.

3D obstacle detection of indoor mobile robots by floor detection and rejection

Obstacle detection is essential for mobile robots to avoid collision with obstacles.Mobile robots usually operate in indoor environments,where they encounter various kinds of obstacles;however,2D range sensor can sense obstacles only in 2D plane.In contrast,by using 3D range sensor,it is possible to detect ground and aerial obstacles that 2D range sensor cannot sense.In this paper,we present a 3D obstacle detection method that will help overcome the limitations of 2D range sensor with regard to obstacle detection.The indoor environment typically consists of a flat floor.The position of the floor can be determined by estimating the plane using the least squares method.Having determined the position of the floor,the points of obstacles can be known by rejecting the points of the floor.In the experimental section,we show the results of this approach using a Kinect sensor.

The investigation of an autonomous intelligent mobile robot system for indoor environment navigation

The autonomous mobile robotics system designed and implemented for indoor environment navigation is a nonholonomic differential drive system with two driving wheels mounted on the same axis driven by two PID controlled motors and two caster wheels mounted in the front and back respectively. It is furnished with multiple kinds of sensors such as IR detectors, ultrasonic sensors, laser line generators and cameras, constituting a perceiving system for exploring its surroundings. Its computation source is a simultaneously running system composed of multiprocessor with multitask and multiprocessing programming. Hybrid control architecture is employed on the mobile robot to perform complex tasks. The mobile robot system is implemented at the Center for Intelligent Design, Automation and Manufacturing of City University of Hong Kong.

Curvature Optical Fiber Whiskers for Mobile Robot Guidance

A novel optical fiber tactile sensory system is proposed for obstacle avoidance of mobile robot. The principle of this whisker like tactile sensor is based on the geometric curvature changes of the optical fiber, which modulate the optical fiber′s light output. With high compliance of plastic optical fiber, the whiskers can produce only small flexing force upon mechanical contact with an obstacle. It can produce reliable proximity signals in extended tactile range, which can be translated into a larger stopping distance for the mobile robot. This sensor is lightweight, and of low cost to allow as many sensor, as necessary to be mounted on a robot.

多传感器融合SLAM研究综述

如今,移动机器人技术的发展使得同步定位与建图(SLAM)技术越来越受到学者的关注。在未知环境下,使移动机器人能够自主完成建图或者探索,是SLAM最基本的要求。在过去的十年,单传感器为机器人的建图和探索提供了良好的效果,而多传感器融合SLAM则以其强鲁棒、高精度的技术特性,为提升移动机器人建图的精度和速度提供了更高的可能性,成为了SLAM发展的主要研究方向。文中总结了现今多传感器融合SLAM的方案,首先对单传感器方案进行了比较;然后对多传感器融合技术的方案进行了对比;最后,分析了多传感器融合SLAM的难点与解决方案,并对多传感器融合SLAM的未来与发展进行了探讨。

地面移动机器人路径避障控制策略研究

针对地面移动机器人在复杂工作环境的避障要求,分别设计了模糊控制器算法和模糊神经网络算法。首先在在地面移动机器人上的安装多传感器检测系统,在此基础上设计了自适应加权多传感器信息融合模型,将融合算法的结果作为避障控制算法的输入。分别在模糊神经网络算法和模糊控制器基础上,真实的模拟出地面移动机器人避障路径,结果表明模糊神经网络算法下的地面移动机器人避障运动路径更平滑,地面移动机器人路径与障碍物的距离更大。最后通过地面移动机器人实验平台上的避障实验,验证了模糊神经网络避障算法的优越性和可靠性。

移动机器人多传感器数据融合方法研究

多传感器数据融合技术能够更好地解决智能设备产生的数据不兼容问题,提高设备运行效率和生产力。现有方法缺乏高性能表现以及未考虑数据隐私保护问题,将联邦学习引入多传感器数据融合中,联邦学习局部模型采用门控循环单元(Gated Recurrent Unit,GRU)算法解决多传感器数据拟合问题,首次设计了一种在时间域和空间域上并行的立体式多传感器数据融合方法,该方法既具备优异的融合性能,又保证了各客户端数据的隐私性。实验结果表明了该方法的正确性与合理性,并在鲁棒性方面呈现出优势。

基于多传感器融合的室内SLAM

为有效解决单一传感器同时定位与地图构建(simultaneous localization and mapping, SLAM)定位精度低、障碍物识别不全问题,提出一种多传感器融合的SLAM方法。通过将RGB-D相机采集的点云进行降采样、滤波处理,极大降低算法的计算量。利用点云库对激光点云和降采样RGB-D相机点云进行融合,融合的点云利用PL-ICP完成点云配准,提高对外部环境的准确识别。利用扩展卡尔曼滤波融合IMU和轮式里程计与点云进行位姿匹配,保证定位的精度。实验结果表明,该方法可以有效提高对室内建图和导航的精度。

移动机器人避障路径多激光传感器融合规划技术

为控制移动机器人成功躲避静、动障碍物,安全快速运行至目的地,研究移动机器人避障路径多激光传感器融合规划技术。此技术在机器人身上安装2个激光雷达传感器,感知机器人所处环境障碍信息,由基于多激光传感器融合的障碍物定位方法,将2个激光雷达传感器感知信息加权融合,提取静态障碍物位置数据后,使用基于动态避障的机器人路径规划方法,改进A∗算法结合静态障碍物位置数据,规划机器人避障的全局避障路径;考虑到全局避障路径中会有可能出现动态障碍物,由人工势场法,计算动态障碍物与目的地对机器人的斥力、引力、合力以及合力方向,调节机器人在全局避障路径中,动态避障的运行方向。实验中,本方法下对障碍物定位偏差在0.1 m内,此技术对静、动障碍物的避障能力合格,运行至目的地时,与障碍物0次碰撞,规划路径和运行时间较短,分别为158 m和9.2 min,效率较高。

基于EKF的多传感器融合定位算法研究

针对室外移动机器人定位系统精度依靠传感器融合存在累计误差的问题,提出一种基于扩展卡尔曼滤波(EKF)的多传感器融合的室外移动机器人定位方法。通过实时差分定位(Real-Time Kinematic,RTK)和里程计信息、IMU信息对室外移动机器人进行扩展卡尔曼滤波融合定位,在真实室外环境中进行实验。实验结果表明:该算法能消除累计误差,提高机器人的定位精度,动态定位精度可达2.5 cm以内,相较于里程计-IMU融合定位,误差减少了92.4%左右,相较于传统的RTK算法,定位精度提高了55.4%。多次实验表明,该算法具有较好的鲁棒性。

CPU环境下多传感器数据融合的机器人3D目标检测方法

实时、准确的3D目标检测算法能提供目标的位置和形态信息,为移动机器人实现高效导航、有效避障等各项任务提供保障。现有的3D目标检测算法对硬件设备运算能力的依赖较为严重,为了在确保检测精度的同时降低方法对硬件设备的要求,提出一种能部署在移动机器人CPU环境下的多传感器融合3D目标检测方法。方法结合了2D目标检测和点云聚类技术,利用2D目标检测技术从图像中获取目标的检测信息,根据相机与雷达的空间映射关系对检测框内的点云进行分割,并对分割后的点云进行聚类和信息提取,从而实现3D目标的检测和定位功能。通过与经典的多传感器3D目标检测算法MVX-Net的对比,该算法有更优的检测精度,同时具有更小的计算复杂度。此外,该方法在实际移动机器人CPU设备的边缘终端上进行部署分析,算法的处理速度达到0.069s/帧,满足10Hz激光雷达频率的需求。

基于异构数据融合的SLAM研究综述

激光与视觉SLAM技术经过几十年的发展,目前都已经较为成熟,并被广泛应用于军事和民用领域.单一传感器的SLAM技术都存在局限性,如激光SLAM不适用于周围存在大量动态物体的场景,而视觉SLAM在低纹理环境中鲁棒性差,但两者融合使用具有巨大的取长补短的潜力,激光与视觉甚至是更多传感器融合的SLAM技术将会是未来的主流方向.本文回顾了SLAM技术的发展历程,分析了激光雷达与视觉的硬件信息,给出了一些经典的开源算法与数据集.根据融合传感器所使用的算法,从传统基于不确定度、基于特征以及基于深度学习的角度详细介绍了多传感器融合方案,概述了多传感器融合方案在复杂场景中的优异性能,并对未来发展作出了展望.

煤矿井下移动机器人多传感器自适应融合SLAM方法

基于同时定位与建图(SLAM)技术的移动机器人能够快速、准确、自动化地采集空间数据,进行空间智能感知和环境地图构建,是实现煤矿智能化和无人化的关键。针对目前煤矿井下多传感器融合SLAM方法存在机器人前端位姿估计退化失效和后端融合精度不足的问题,提出了一种煤矿井下移动机器人激光雷达(LiDAR)−视觉−惯性(IMU)自适应融合SLAM方法。对LiDAR点云数据进行聚类分割,提取线面特征,利用IMU预积分状态进行畸变校正,采用基于自适应Gamma校正和对比度受限的自适应直方图均衡化(CLAHE)的图像增强算法处理低照度图像,再提取视觉点线特征。用IMU预积分状态为LiDAR特征匹配与视觉特征跟踪提供位姿初始值。根据LiDAR相邻帧的线面特征匹配得到移动机器人位姿,之后进行视觉点线特征跟踪,分别计算LiDAR、视觉、IMU位姿变化值,通过设定动态阈值来检测前端里程计的稳定性,自适应选取最优位姿。对不同传感器构建残差项,包括点云匹配残差、IMU预积分残差、视觉点线残差、边缘化残差。为了兼顾精度与实时性,基于滑动窗口实现激光点云特征、视觉特征、IMU测量的多源数据联合非线性优化,实现煤矿井下连续可用、精确可靠的SLAM。对图像增强前后效果进行试验验证,结果表明,基于自适应Gamma校正和CLAHE的图像增强算法能显著提升背光区和光照区的亮度和对比度,增加图像中的特征信息,大幅提升特征点提取数量和匹配质量,匹配成功率达90.7%。为验证所提方法的性能,在狭长走廊和煤矿巷道场景下进行试验验证,结果表明,所提方法在狭长走廊场景的定位均方根误差为0.15 m,构建的点云地图一致性较高;在煤矿巷道场景中的定位均方根误差为0.19 m,构建的点云地图可真实地反映煤矿井下环境。

基于改进A^(*)算法的POL-Robot路径规划

针对复杂干扰环境下的移动机器人智能导航问题,多种导航方法被提出和应用,其中偏振光导航有良好的自主性和抗干扰性。基于偏振光传感器的组合导航系统较传统视觉算法能有效提高航向角和位置精度,可满足室外机器人导航要求,但欠缺路径规划功能。为实现偏振光移动机器人的智能导航应用,基于此系统,融入智能规划算法,实现路径规划功能。传统路径规划算法存在搜索效率低、生成路径曲率非连续等问题,为此提出一种改进A^(*)算法和轨迹优化方法,采用8邻域搜索方式,并改进了其启发函数,最后,利用梯度插补方式平滑路径。仿真及实验表明,改进后方案路径长度平均减小6.1%,路径转折点个数平均减少53.8%,实现了基于偏振光的移动机器人的智能导航。

基于超声波传感器的双足步行机器人避障系统设计

文章主要研究如何将双足步行机器人的开环控制系统升级为闭环控制系统。为实现这一目标,在硬件配置和控制算法两方面做了优化。首先,为双足步行机器人配置1个可转动180°的舵机云台,用于安装超声波传感器。通过转动云台,超声波传感器可探测到机器人左、前、右3个方向的障碍物。其次,安装Arduino MEGA 2560开发板作为主控制器,根据障碍物的方位、距离下达前进、转向或加减速指令,从而达到避障效果。最后,设计控制算法,其核心是让机器人向没有障碍物或距离障碍物最远的一侧步行,步行速度与障碍物距离成正相关关系。通过实验检验机器人的避障效果,机器人在具有口字形、凹字形、Z字形障碍物的场地中,均能按照控制算法成功避障。文章成功将开环控制系统的双足步行机器人改为闭环控制系统,为机器人智能化方向发展奠定了基础。