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移动机器人同步定位与地图构建过程中的轨迹规划研究 总被引:1,自引:1,他引:1
研究了移动机器人同步定位与地图构建(SLAM)过程中的轨迹规划问题.提出了一种新的目标函数,它同时考虑机器人运动对地图覆盖面积、地图不确定性、定位不确定性、导航代价等几个方面的影响.提出了一步最优和多步最优轨迹规划的概念,并分别设计了两种最优标准下的规划算法和近似计算方法.最后,通过对比仿真实验验证了所提出的方法的有效性,并指出了今后的研究方向. 相似文献
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《自动化仪表》2017,(2)
采用以DSP+ARM微控制器为核心的嵌入式实时操作系统,设计了一种基于嵌入式系统和机器视觉定位的室内移动机器人。利用视觉导航图像处理技术、形态学方法和一种基于尺度空间理论的Harris角点检测方法,借助陀螺仪和加速度计的惯性导航技术进行地图的匹配定位,并按环境的变化情况更新地图以实现导航。基于超声波传感器设计了避障模块,实现了自主避障。设计了一种基于Zig Bee技术的无线通信模块,实现了机器人的智能控制,增加了机器人之间以及机器人和服务器之间的信息交换。软件核心算法采用多传感器融合技术,将D-S理论和人工神经网路相结合;在非线性化系统中,利用BP神经网路多层前馈网络的反相传播学习方式,很好地实现了模式识别。与其他机器人系统相比,该系统具有独立操作性强、功能多样化、扩展性强等特点,克服了目前机器人存在的成本高、功耗大、实时性差和定位不准确的问题。 相似文献
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在未知的三维环境中,移动机器人自主导航通常需要实时构建与环境全局一致的栅格地图,而现有大部分系统缺少地图更新策略,构建的栅格地图与实际环境不一致.文中将同步定位与建图模块获得的环境信息以点云形式提供给栅格建图模块处理,同时提出基于关键帧的高效数据结构和地图实时更新策略,实时构建可用于移动机器人自主导航的全局一致的地图.室内动态的实验数据测试表明,文中方法可以有效实时更新地图,生成与环境一致的三维栅格地图,支持其后续的自主导航操作. 相似文献
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当前移动机器人导航方法大多数是改善局部路径规划的反应式导航而没有充分考虑全局环境中的行人,借助全局范围的行人感知,提出并实现一种基于多层代价地图的全局路径规划方法。首先基于行人感知进行个人空间和群组交互的社会代价建模,基于行人轨迹预测生成包含预测阶段社会代价的多层动态代价地图,提供预测阶段的社会约束信息。全局路径规划器在动态代价地图基础上定义代价函数进行最优状态的启发式搜索,引入“规划-预测-执行”时序周期进行动态规划。最后通过和传统路径规划器在行人运动、群组交互等仿真、实际场景下进行对比试验,该方法对应路径长度、执行时间更短,和人/群组保持的距离更符合社会性。 相似文献
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本文基于立体视觉定位技术,提出了基于双目立体视觉的栅格地图构建方法,用以解决目前视觉SLAM技术构建的稀疏特征地图难以直接用于自主导航的问题。本文提出的方法仅以视觉信息作为输入实时完成移动机器人自定位与外界环境栅格地图的构建。首先采用双目立体视觉定位获取机器人运动参数,利用稠密匹配估算空间点云分布,在考虑机器人实际高度的情况下将三维点云投影成二维数据,最后通过二值贝叶斯滤波器在线构建栅格地图。本文所构建的栅格地图包含环境几何信息,可直接应用于机器人路径规划与导航。实验结果验证了本文所以出的定位与地图构建方法的可行性。 相似文献
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一个基于全景视觉的移动机器人导航系统的设计与实现 总被引:1,自引:1,他引:0
针对移动机器人路径规划与导航的实际应用,设计了一个基于全景视觉的移动机器人路径规划导航系统.首先,对导航系统的体系结构和功能进行描述.然后,分别就如何采用全景视觉传感器进行环境探索与地图创建,基于回归神经网络的广度优先搜索法和Voronoi骨架图法两种路径规划算法原理,以及如何实现按规划路径实施导航这三个方面进行了详细阐述.最后,结合实际机器人进行导航实验,评估导航系统的性能和路径规划算法的有效性. 相似文献
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基于几何-拓扑广域三维地图和全向视觉的移动机器人自定位 总被引:2,自引:0,他引:2
面向大规模室内环境, 研究了基于全向视觉的移动机器人自定位. 提出用分层的几何-拓扑三维地图管理广域环境特征, 定义了不同层次的三维局部环境特征及全局拓扑属性, 给出了分层地图的应用方法. 构建了全向视觉传感器成像模型及其不确定性传播方法, 使得地图中的概率元素能够在系统中有效应用. 采用随机点预估搜索的方法提取环境元素对应的曲线边缘特征. 用带反馈的分层估计方法在融合中心对多观测特征产生的相应估计状态进行总体融合. 以分层逻辑架构设计实现了移动机器人交互式自定位系统. 实验分析了真实环境中不同初始位姿和观测信息情况下定位系统的收敛性和定位精度, 在考虑动态障碍物的遮挡情况下完成了机器人的在线环境感知和运动自定位任务. 实验结果表明本文方法的可靠性和实用性. 相似文献
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This paper presents the real-time autonomous navigation of an electric wheelchair in a large-scale urban area. Accurate self-pose localization and well-chosen motion control are crucial for application to urban areas, as electric wheelchairs move on paved roads in dynamic environments and travel along sidewalks at a brisk speed. Our system is equipped with a localization module based on a 3D map and a path planning module based on a navigation map. However, the large-scale 3D map causes a high memory load, and the embedded PC can not deal with the map data. In addition, the large-scale navigation map increases the computational cost of path planning, which causes delays in navigation. To achieve real-time navigation independent of map size, we propose a 6-DoF pose localization switching reference 3D map and a two-step path planning framework. We ran tests by using an electric wheelchair on a real street in Tokyo and found that the proposed navigation system achieved autonomous navigation for over 8.8?km in about 133 minutes. The experimental results showed that the memory load was kept constant and the path planning was performed at high frequency, regardless of the size of the map or the distance to the destination. 相似文献
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基于立体视觉的移动机器人导航算法 总被引:1,自引:0,他引:1
移动机器人立体视觉系统不仅提供三维地形图用于障碍规避和路径规划,其结果还可以用于视觉导航。以移动机器人立体视觉系统为基础,研究了基于前后两个位置上立体图对的视觉测量算法用于移动机器人的连续导航,讨论了影响导航精度的因素和改进方法;研究了基于局部和全局三维地形图的地形匹配算法用于定期校正位置误差,算法实现简便,定位精度取决于地形图精度。实验结果证明了两种方法的有效性,可以兼顾近距离和中远距离导航任务。 相似文献
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Yoo JK Kim JH 《IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics》2012,42(1):125-139
When a humanoid robot moves in a dynamic environment, a simple process of planning and following a path may not guarantee competent performance for dynamic obstacle avoidance because the robot acquires limited information from the environment using a local vision sensor. Thus, it is essential to update its local map as frequently as possible to obtain more information through gaze control while walking. This paper proposes a fuzzy integral-based gaze control architecture incorporated with the modified-univector field-based navigation for humanoid robots. To determine the gaze direction, four criteria based on local map confidence, waypoint, self-localization, and obstacles, are defined along with their corresponding partial evaluation functions. Using the partial evaluation values and the degree of consideration for criteria, fuzzy integral is applied to each candidate gaze direction for global evaluation. For the effective dynamic obstacle avoidance, partial evaluation functions about self-localization error and surrounding obstacles are also used for generating virtual dynamic obstacle for the modified-univector field method which generates the path and velocity of robot toward the next waypoint. The proposed architecture is verified through the comparison with the conventional weighted sum-based approach with the simulations using a developed simulator for HanSaRam-IX (HSR-IX). 相似文献
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Lisien B. Morales D. Silver D. Kantor G. Rekleitis I. Choset H. 《Robotics, IEEE Transactions on》2005,21(3):473-481
This paper presents a new map specifically designed for robots operating in large environments and possibly in higher dimensions. We call this map the hierarchical atlas because it is a multilevel and multiresolution representation. For this paper, the hierarchical atlas has two levels: at the highest level there is a topological map that organizes the free space into submaps at the lower level. The lower-level submaps are simply a collection of features. The hierarchical atlas allows us to perform calculations and run estimation techniques, such as Kalman filtering, in local areas without having to correlate and associate data for the entire map. This provides a means to explore and map large environments in the presence of uncertainty with a process named hierarchical simultaneous localization and mapping. As well as organizing information of the free space, the map also induces well-defined sensor-based control laws and a provably complete policy to explore unknown regions. The resulting map is also useful for other tasks such as navigation, obstacle avoidance, and global localization. Experimental results are presented showing successful map building and subsequent use of the map in large-scale spaces. 相似文献
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Driving on Point Clouds: Motion Planning,Trajectory Optimization,and Terrain Assessment in Generic Nonplanar Environments
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We present a practical approach to global motion planning and terrain assessment for ground robots in generic three‐dimensional (3D) environments, including rough outdoor terrain, multilevel facilities, and more complex geometries. Our method computes optimized six‐dimensional trajectories compliant with curvature and continuity constraints directly on unordered point cloud maps, omitting any kind of explicit surface reconstruction, discretization, or topology extraction. We assess terrain geometry and traversability on demand during motion planning, by fitting robot‐sized planar patches to the map and analyzing the local distribution of map points. Our motion planning approach consists of sampling‐based initial trajectory generation, followed by precise local optimization according to a custom cost measure, using a novel, constraint‐aware trajectory optimization paradigm. We embed these methods in a complete autonomous navigation system based on localization and mapping by means of a 3D laser scanner and iterative closest point matching, suitable for both static and dynamic environments. The performance of the planning and terrain assessment algorithms is evaluated in offline experiments using recorded and simulated sensor data. Finally, we present the results of navigation experiments in three different environments—rough outdoor terrain, a two‐level parking garage, and a dynamic environment, demonstrating how the proposed methods enable autonomous navigation in complex 3D terrain. 相似文献