基于生物认知的移动机器人路径规划方法

针对移动机器人在非结构环境下的导航任务,受哺乳动物空间认知方式的启发,提出一种基于生物认知进行移动机器人路径规划的方法．结合认知地图特性,模拟海马体的情景记忆形成机理,构建封装了场景感知、状态神经元及位姿感知相关信息的情景认知地图,实现了机器人对环境的认知．基于情景认知地图,以最小事件距离为准则,提出事件序列规划算法用于实时导航过程．实验结果表明,该控制算法能使机器人根据不同任务选择最佳规划路径．     

基于卷积神经网络的仿鼠脑海马结构认知地图构建方法

针对融合视觉信息的仿鼠脑海马模型闭环检测精度较低、地图构建不准确的问题,文中提出基于卷积神经网络的仿鼠脑海马结构认知地图构建方法.利用改进的卷积神经网络模型提取视觉输入特征,融合空间细胞计算模型得到位置信息,并构建认知地图.基于汉明距离计算视觉信息与视图库中图像的相似度,实现对复杂动态环境中熟悉场景的识别,完成机器人在环境中的定位及位置纠正.仿真与物理实验验证文中方法的有效性与鲁棒性.     

一种服务机器人家庭全息地图构建方法研究*

提出了一种满足家庭服务机器人环境认知和智能服务需要的融合环境和目标信息的家庭全息地图。设计了局部几何—全局拓扑的全息地图分层表示模型。分析了机器人坐标系、局部环境坐标系和目标的相对关系,给出了机器人局部环境自定位算法和基于坐标变换的服务机器人全息地图构建方法。家庭环境下机器人实物实验表明,基于局部几何—全局拓扑表示的全息地图,服务机器人路径规划和任务执行效率得到有效提升。     

基于细化算法的移动机器人拓扑地图创建

在大规模未知环境中,移动机器人要自主完成导航和路径规划等智能任务,关键问题是创建周围环境地图.拓扑地图.以图(Graph)的结构形式表现-个环境的连通性,是一种紧凑的环境表示方法.文中借鉴图像处理中的细化算法来创建室内环境的拓扑地图,首先以栅格地图建模机器人环境,然后将环境的栅格地图进行细化,提取出环境的有效拓扑信息.而且,此方法创建的拓扑地图,未直接使用传感器原始数据,对环境的变化具有较强的鲁棒性.仿真实验结果表明,基于细化算法创建的环境拓扑地图,清晰、简洁,不会产生多余的节点和路径信息.相比于栅格地图,信息存储量明显减少,从而提高了移动机器人自主运行、导航和路径规划的能力,大大提高了系统的工作效率.     

一种移动机器人环境模型——灰色定性地图

针对移动机器人的环境建模问题,提出一种综合拓扑地图和儿何地图特点的混合环境模型——灰色定性地图.用凸剖分算法将环境中的自由空间分解为一组凸多边形.灰色定性地图的定性层由凸多边形及其之间的邻接关系构成,用于模拟人类在路径规划时的高层定性推理.定量层由凸多边形顶点的坐标和势场向量构成,用于决定机器人在连续空间中的运动方向和速度.理论分析和实验均表明:灰色定性地图可以模拟人类对环境认知的知识表达,并且可以仪由凸多边形邻接信息和顶点信息支持机器人完成路径规划且确保路径的平滑性,有效地降低了环境模型的空间复杂度.     

一种桌面机器人及其自主地图绘制算法

利用嵌入式技术设计了一种桌面机器人系统,机器人体积不到200 cm3,系统利用全局摄像机采集图像,通过无线通信组件对机器人定位导航,从而进行面向地图绘制的智能行为研究;从桌面机器人系统采集到地图信息,并生成神经网络训练样本,利用可增长自组织特征映射图GSOM(Growing Self-organizing Map)的地图绘制算法,通过不断增加新的神经元实现网络规模的增长,从而生成以少数SOM图神经元分布描述环境特征信息的拓扑地图;在机器人系统上进行了基于GSOM模型的自主地图的绘制实验,并利用所得拓扑地图进行了准确的机器人导航实验.实验结果表明基于GSOM的自主地图绘制方法可行,机器人系统表现出类似生物的自主智能行为.该方法可以应用于大环境下机器人的自主地图测绘与导航.     

基于海马体位置细胞的认知地图构建与导航

针对移动机器人环境认知问题,受老鼠海马体位置细胞在特定位置放电的启发,构建动态增减位置细胞认知地图模型(Dynamic growing and pruning place cells-based cognitive map model,DGP-PCCMM),使机器人在与环境交互的过程中自组织构建认知地图,进行环境认知....     

双目立体视觉栅格地图构建方法

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。     

基于人工引导的机器人建图技术

移动机器人能够创建精确的环境地图,是其具有准确的定位能力以及完成其他任务的前提。因此地图创建技术是移动机器人的关键技术。提出了一种基于人工引导方式的地图创建技术。该技术一方面弥补了传统机器人建图过程中自动搜索路径低效率的弱点,另一方面通过人机交互,机器人可以在建图的过程中记住关键的路径点,建立栅格地图与拓扑地图的混合地图,为移动机器人将来的导航奠定基础。     

视觉传感系统的自主移动机器人路径规划方法

在未知环境下,机器人很难快速获取周边环境信息并建立实时环境地图,实现自主运行.为此提出基于视觉导航的方法,利用全景摄像机作为机器人的视觉传感器系统采集环境信息,将彩色地图进行HSI空间下模糊聚类图像分割,得到环境二值图像;将图像进行栅格化处理来构建环境地图,运用8方向连接的Dijkstra进行全局路径规划,计算出最优路径,从而实现移动机器人的快速、自主运动.经过仿真实验证明,该方法有效且可行.     

一种基于连接机制和时空经验的认知地图学习与导航方法

提出了一种连接主义方法, 利用移动机器人自身的时空经验, 在缺乏全局坐标信息和环境先验模型的情况下, 建立面向目标的认知地图. 在线形成的时序处理网络 (TSPN)可提供简洁的历史感知信息, 以神经元激活特性保存空间知识, 引导机器人运动. 结合TSPN和反应式行为模块的导航系统可实现动态的路标及方向检测、路径学习和实时导航功能. 仿真和实际实验验证了系统的有效性和适应性.     

未知环境下单目视觉移动机器人路径规划

针对室内未知环境下的避障和局部路径规划,提出了一种单目移动机器人路径规划算法,该算法通过对环境图像的自适应阈值分割,获取障碍物与地面交线轮廓点集。通过对现有几种单目测距方法的分析比较,提出一种改进的空间几何约束单目视觉测距计算方法,并依据单目测距的几何关系建立了图像坐标系与机器人坐标系的映射,绘建了一定比例的局部地图。在局部地图上通过改进的人工势场算法为机器人规划路径,改进的人工势场算法解决了传统算法目标点不可到达的问题。通过MATLAB进行仿真实验,结果表明该方法可以规划出有效合理的路径。     

Navigation behavior based on self-organized spatial representation in hierarchical recurrent neural network

ABSTRACT

A cognitive map is an internal model of the external world and contains the spatial representation of the surrounding environment. The existence of the cognitive map was first identified in rats; rats can navigate to their desired destination using cognitive maps while dealing with environmental uncertainty. We performed a mobile robot navigation experiment where obstacles were randomly placed using hierarchical recurrent neural network (HRNN) with multiple timescales. The HRNN was trained to navigate the mobile robot to the destination indicated by a snapshot image. After the training, the HRNN was able to successfully avoid the obstacles and navigate to the destination from any location in the environment. Analysis of the internal states of the HRNN showed that the module with fast timescale handles obstacle avoidance and the one with slow timescale has spatial representation corresponding to the spatial position of the destination. Moreover, in the experiment wherein the novel path appeared, the trained HRNN performed shortcut behavior. The shortcut behavior shows that the HRNN performed navigation using the self-organized spatial representation in the slow recurrent neural network. This indicates that training of goal-oriented navigation, i.e. the navigation motivated by a snapshot image of the destination results in the self-organization of cognitive map-like representation.     

Long-term experiments with an adaptive spherical view representation for navigation in changing environments

Real-world environments such as houses and offices change over time, meaning that a mobile robot’s map will become out of date. In this work, we introduce a method to update the reference views in a hybrid metric-topological map so that a mobile robot can continue to localize itself in a changing environment. The updating mechanism, based on the multi-store model of human memory, incorporates a spherical metric representation of the observed visual features for each node in the map, which enables the robot to estimate its heading and navigate using multi-view geometry, as well as representing the local 3D geometry of the environment. A series of experiments demonstrate the persistence performance of the proposed system in real changing environments, including analysis of the long-term stability.     

Cognitive navigation based on nonuniform Gabor space sampling, unsupervised growing networks, and reinforcement learning

We study spatial learning and navigation for autonomous agents. A state space representation is constructed by unsupervised Hebbian learning during exploration. As a result of learning, a representation of the continuous two-dimensional (2-D) manifold in the high-dimensional input space is found. The representation consists of a population of localized overlapping place fields covering the 2-D space densely and uniformly. This space coding is comparable to the representation provided by hippocampal place cells in rats. Place fields are learned by extracting spatio-temporal properties of the environment from sensory inputs. The visual scene is modeled using the responses of modified Gabor filters placed at the nodes of a sparse Log-polar graph. Visual sensory aliasing is eliminated by taking into account self-motion signals via path integration. This solves the hidden state problem and provides a suitable representation for applying reinforcement learning in continuous space for action selection. A temporal-difference prediction scheme is used to learn sensorimotor mappings to perform goal-oriented navigation. Population vector coding is employed to interpret ensemble neural activity. The model is validated on a mobile Khepera miniature robot.     

基于VSLAM的自主移动机器人三维同时定位与地图构建

移动机器人在探索未知环境且没有外部参考系统的情况下,面临着同时定位和地图构建（SLAM）问题。针对基于特征的视觉SLAM（VSLAM）算法构建的稀疏地图不利于机器人应用的问题,提出一种基于八叉树结构的高效、紧凑的地图构建算法。首先,根据关键帧的位姿和深度数据,构建图像对应场景的点云地图;然后利用八叉树地图技术进行处理,构建出了适合于机器人应用的地图。将所提算法同RGB-D SLAM（RGB-Depth SLAM）算法、ElasticFusion算法和ORB-SLAM（Oriented FAST and Rotated BRIEF SLAM）算法通过权威数据集进行了对比实验,实验结果表明,所提算法具有较高的有效性、精度和鲁棒性。最后,搭建了自主移动机器人,将改进的VSLAM系统应用到移动机器人中,能够实时地完成自主避障和三维地图构建,解决稀疏地图无法用于避障和导航的问题。     

Biologically-inspired robot spatial cognition based on rat neurophysiological studies

This paper presents a robot architecture with spatial cognition and navigation capabilities that captures some properties of the rat brain structures involved in learning and memory. This architecture relies on the integration of kinesthetic and visual information derived from artificial landmarks, as well as on Hebbian learning, to build a holistic topological-metric spatial representation during exploration, and employs reinforcement learning by means of an Actor-Critic architecture to enable learning and unlearning of goal locations. From a robotics perspective, this work can be placed in the gap between mapping and map exploitation currently existent in the SLAM literature. The exploitation of the cognitive map allows the robot to recognize places already visited and to find a target from any given departure location, thus enabling goal-directed navigation. From a biological perspective, this study aims at initiating a contribution to experimental neuroscience by providing the system as a tool to test with robots hypotheses concerned with the underlying mechanisms of rats’ spatial cognition. Results from different experiments with a mobile AIBO robot inspired on classical spatial tasks with rats are described, and a comparative analysis is provided in reference to the reversal task devised by O’Keefe in 1983.     

An appearance-based visual compass for mobile robots

Localization is one of the most important basic skills of a mobile robot. Most approaches, however, still rely either on special sensors or require artificial environments. In this article, a novel approach is presented that can provide compass information for localization, purely based on the visual appearance of a room. A robot using such a visual compass can quickly learn a cylindrical map of the environment, consisting of simple statistical features that can be computed very quickly. The visual compass algorithm is efficient, scalable and can therefore be used in real-time on almost any contemporary robotic platform. Extensive experiments on a Sony Aibo robot have validated that the approach works in a vast variety of environments.