共查询到19条相似文献,搜索用时 140 毫秒
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《电子技术与软件工程》2016,(8)
智能移动机器人技术主要应用在路径规划、机器人的定位与导航、运动控制等范畴。本文通过对移动机器人现今状态以及未来展望进行的综合论述。以移动机器人的整体结构系统,并重点的探讨了移动机器人技术中的占主导位置的路径规划技术、导航技术和多传感器信息融合技术,研究出高智能情感移动技术以及视觉导航技术等是移动机器人未来发展的主要方向。 相似文献
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《电子技术与软件工程》2017,(10)
随着社会的不断进步以及科技的不断发展,智能移动机器人应用到各个行业的发展中,据统计,其运动控制、路径的规划等应用最为广泛。可以说智能移动机器人已和我们的生活息息相关,但是人们对其的了解还少之甚少,基于此,在本文,笔者就从不同的方面对智能移动机器人技术进行分析,并在现有的基础上对未来机器人的发展进行展望。笔者认为现阶段的智能移动机器人的核心技术包括三方面:一是路径规划技术;二是导航技术以及多传感器信息融合技术,未来的主要发展方向也有三:高智能机器人、情感机器人以及视觉导航机器人。 相似文献
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由于移动机器人的广泛应用,传统的基于惯性测量单元或GPS/INS的导航系统在某些环境下的应用受到限制.而视觉SLAM的快速发展为移动机器人的导航提供了基础,但是当前还面临着一些挑战,如何让视觉SLAM更好的应用在机器人导航中成为研究的热点.本文介绍了视觉SLAM中的一些关键技术,总结了纯视觉SLAM和多传感器视觉SLA... 相似文献
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《电子科技文摘》2000,(3)
Y90-62011 00050831999年 IEEE 机器人和自动机国际会议录,卷1=1999 IEEE international conference on robotics and au-tomation,Vol.1[会,英]/IEEE Rolxxics and Automa-tion Society.—IEEE,1999.—826P.(HC)本会议录共4卷,此为第1卷,有论文132篇,内容有移动机器人运动,未知环境中导航,双足机器人,水下车辆,装配机器人和编程,制造系统的离散控制,运动规划,机器人控制,制动器,遥操作,接触和抓取控制,视觉伺服控制,跟踪传感,移动机器人和应用,基于传感器导航,水下机器人,灵活制造系统,任务调度,视觉伺服控制,声纳感知,移动机器人的相互协调等。 相似文献
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移动机器人在开展局部路径规划时,对障碍物的准确识别及避障路径规划效果会直接影响机器人的安全运行。为此,提出基于CNN的移动机器人局部路径激光雷达辅助规划方法。该方法首先依据CNN方法建立障碍物定位模型,结合设计的视觉控制器,完成场地内障碍物位置的定位;再使用激光雷达采集机器人与障碍物位置之间的距离,再通过VFH算法对距离量化,利用自适应阈值计算机器人移动时的转向,从而确定机器人的局部航向,实现机器人的局部路径规划。实验结果表明,使用该方法实施路径规划时,规划出的路径长度最多为82 m,规划时间最多为5.12 s,规划效果好。 相似文献
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为移动机器人在无定位信息的无线传感器网络(WSN)中选择路程短、代价低的导航路径,提出了一种基于无线传感器网络的移动机器人导航方法,包括全网络导航路径规划和局部节点趋近算法。该方法通过结合各节点传感器数据,构造代价函数,在网络中建立伪梯度势场,为移动机器人规划最优路径;移动机器人通过探测接收信号强度指示(RSSI),逐一趋近该路径上的传感器节点到达目标节点。仿真结果表明,该方法能够根据移动机器人的导航要求,引导移动机器人迅速沿最优路径到达目标节点。 相似文献
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为了实现暗环境下移动机器人导航中障碍物的检测与运动机器人的定位,采用了一种组合式光栅投射立体视觉传感器研究方法,首先通过光栅投射和立体视觉相融合的方法,建立光栅投射立体视觉传感器几何和数学模型,然后利用空间设备位置约束原理和投影平面相交的方法,进行了机器人视场内空间物体的3维坐标计算,建立了可靠真实的障碍物检测和分析方法,并进行了理论分析和实验验证,取得了距离计算精度0.8mm的数据。结果表明,该方法对于图像计算的精度在亚像素级。该方法有利于目前黑暗环境中机器人无法自主导航难题的突破,为黑暗环境中无全球定位系统支持的机器人导航提供了基础探索。 相似文献
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Woojin Chung Seokgyu Kim Minki Choi Jaesik Choi Hoyeon Kim Chang-bae Moon Jae-Bok Song 《Industrial Electronics, IEEE Transactions on》2009,56(10):3941-3950
We present one approach to achieve safe navigation in an indoor dynamic environment. So far, there have been various useful collision avoidance algorithms and path planning schemes. However, those algorithms possess fundamental limitations in that the robot can avoid only ldquovisiblerdquo ones among surrounded obstacles. In a real environment, it is not possible to detect all the dynamic obstacles around the robot. There are many occluded regions due to the limited field of view. In order to avoid collisions, it is desirable to exploit visibility information. This paper proposes a safe navigation scheme to reduce collision risk considering occluded dynamic obstacles. The robot's motion is controlled by the hybrid control scheme. The possibility of collision is dually reflected to path planning and speed control. The proposed scheme clearly indicates the structural procedure on how to model and to exploit the risk of navigation. The proposed scheme is experimentally tested in a real office building. The experimental results show that the robot moves along the safe path to obtain sufficient field of view. In addition, safe speed constraints are applied in motion control. It is experimentally verified that a robot safely navigates in dynamic indoor environment by adopting the proposed scheme. 相似文献
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《Mechatronics》2022
Path planning is one of the key technologies for mobile robot applications. However, the traditional robot path planner has a slow planning response, which leads to a long navigation completion time. In this paper, we propose a novel robot path planner (SOA+A2C) that produces global and local path planners with the seeker optimization algorithm (SOA) and the advantage actor-critic (A2C) algorithm, respectively. In addition, to solve the problems of poor convergence performance when training deep reinforcement learning (DRL) agents in complex path planning tasks and path redundancy when metaheuristic algorithms, such as SOA, are used for path planning, we propose the incremental map training method and path de-redundancy method. Simulation results show that first, the incremental map training method can improve the convergence performance of the DRL agent in complex path planning tasks. Second, the path de-redundancy method can effectively alleviate path redundancy without sacrificing the search capability of the metaheuristic algorithm. Third, the SOA+A2C path planner is superior to the Dijkstra & dynamic window approach (Dijkstra+DWA) and the Dijkstra & timed elastic band (Dijkstra+TEB) path planners provided by the robot operating system (ROS) in terms of path length, path planning response time, and navigation completion time. Therefore, the developed SOA+A2C path planner can serve as an effective tool for mobile robot path planning. 相似文献
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D'Orazio T. Lovergine F.P. Ianigro M. Stella E. Distante A. 《Industrial Electronics, IEEE Transactions on》1994,41(6):654-662
This paper is concerned with the problem of determining the position of a mobile vehicle during navigation. In order to achieve this objective a multisensor navigation system for self location of the robot has been developed. By tracking a few known landmarks with a vision module, the system is able to monitor continuously its position and to integrate these estimates with the measures provided by the vehicle odometers. This paper describes in detail the vision module used by the navigation system 相似文献
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Haigron P Bellemare ME Acosta O Göksu C Kulik C Rioual K Lucas A 《IEEE transactions on medical imaging》2004,23(11):1380-1390
This paper presents a new approach dealing with virtual exploratory navigation inside vascular structures. It is based on the notion of active vision in which only visual perception drives the motion of the virtual angioscope. The proposed fly-through approach does not require a premodeling of the volume dataset or an interactive control of the virtual sensor during the fly-through. Active navigation combines the on-line computation of the scene view and its analysis, to automatically define the three-dimensional sensor path. The navigation environment and the camera-like model are first sketched. The basic stages of the active navigation framework are then described: the virtual image computation (based on ray casting), the scene analysis process (using depth map), the navigation strategy, and the virtual path estimation. Experimental results obtained from phantom model and patient computed tomography data are finally reported. 相似文献