Simplified map representation and map learning system for autonomous navigation of mobile robots

Autonomous mobile robots need environmental maps to navigate to specific destinations, but there are difficulties in generating and acquiring efficient maps for them. Map learning systems and map representation for autonomous robot navigation are highly interrelated and need a total system design that combines these two factors. This study considers a combined simple map representation and map learning system. The proposed map representation includes geometrical relationships between important places and grid maps for these places, but not a total grid map of the environment. In particular, the study focuses on the ability to recognize places based on image features. Successful experiments on autonomous navigation with the proposed map representation using an actual mobile robot are described.     

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．     

Navigation integration of a mobile robot in dynamic environments

This article presents a design and experimental study of navigation integration of an intelligent mobile robot in dynamic environments. The proposed integration architecture is based on the virtual‐force concept, by which each navigation resource is assumed to exert a virtual force on the robot. The resultant force determines how the robot will move. Reactive behavior and proactive planning can both be handled in a simple and uniform manner using the proposed integration method. A real‐time motion predictor is employed to enable the mobile robot to deal in advance with moving obstacles. A grid map is maintained using on‐line sensory data for global path planning, and a bidirectional algorithm is proposed for planning the shortest path for the robot by using updated grid‐map information. Therefore, the mobile robot has the capacity to both learn and adapt to variations. To implement the whole navigation system efficiently, a blackboard model is used to coordinate the computation on board the vehicle. Simulation and experimental results are presented to verify the proposed design and demonstrate smooth navigation behavior of the intelligent mobile robot in dynamic environments. ©1999 John Wiley & Sons, Inc.     

基于激光传感器构建环境拓扑地图

地图构建是移动机器人在未知环境中实现导航任务的基础。利用激光传感器扫描数据构建环境边界的几何地图,并在构建环境几何地图的基础上,将传感器的扫描数据以机器人为中心分割成4个区域,并利用提出的中线法抽取机器人所处环境自由空间的拓扑地图。实验表明:该方法能有效、实时、紧凑地表示环境。     

基于行为控制的移动机器人导航方法

针对移动机器人在完全未知或者部分未知的环境中进行自主导航容易陷入各种陷阱的问题,提出了一种基于多行为控制的导航方法;机器人通过激光雷达对周边环境进行感知,并将采集到的信息与行为转换条件进行匹配用于行为转换的决策;同时在该方法中通过栅格地图引入了记忆信息,从而增强机器人对周边环境的认知能力,从而提高机器人的决策能力;通过仿真实验证明了在简单环境中算法的有效性,同时也证明该算法对于某些复杂的环境有效可行,具有优化性、实时性与智能性的特点。     

基于激光雷达与双目视觉的移动机器人SLAM研究

为解决移动机器人在环境未知条件下,利用单一传感器自主导航时不能及时定位、构建地图不精确的问题,提出采用一种改进RBPF算法,在计算提议分布时将移动机器人的观测数据(视觉信息与激光雷达信息)和里程计信息融合;针对一般视觉图像特征点提取算法较慢的问题,采用基于ORB算法对视觉图像进行处理以加快视觉图像处理速度的方法;最后通过在安装有开源机器人操作系统(ROS)的履带式移动机器人进行实验,验证了采用该方法可构建可靠性更高、更精确的2D栅格图,提高了移动机器人SLAM的鲁棒性.     

机器人动态神经网络导航算法的研究和实现

针对Pioneer3-DX 移动机器人, 提出了基于强化学习的自主导航策略, 完成了基于动态神经网络的移动机器人导航算法设计. 动态神经网络可以根据机器人环境状态的复杂程度自动地调整其结构, 实时地实现机器人的状态与其导航动作之间的映射关系, 有效地解决了强化学习中状态变量表的维数爆炸问题. 通过对Pioneer3-DX移动机器人导航进行仿真和实物实验, 证明该方法的有效性, 且导航效果明显优于人工势场法.     

机器人局部环境特征提取方法的研究

考虑到机器人导航过程中的实时性与可靠性要求,充分利用激光雷达信息的快速性与精确性,针对结构化环境中诸如墙壁、拐角、通道等这些典型环境特征分别设计了一套快速有效地特征提取的算法;另外算法还考虑到机器人建立环境地图的需要,在环境特征提取时对某一些密集的障碍物进行了合并,并注意保留了环境特征的一些拓扑信息,为建图作了一些前期准备,拿到移动机器人MORCS上进行实验获得了满意的结果,验证了算法的实时性与可靠性.     

Path planning based on geographical features information for an autonomous mobile robot

We propose a path-planning algorithm for an autonomous mobile robot using geographical information, under the condition that the robot moves in an unknown environment. Images input by a camera at every sampling time are analyzed and geographical elements are recognized, and the geographical information is embedded in an environmental map. Then the path is updated by integrating the known information and the prediction on the unknown environment. We used a sensor fusion method to improve the mobile robot's dead-reckoning accuracy. The experimental results confirm the effectiveness of the proposed algorithm as the robot reached the goal successfully using the geographical information.     

基于模糊逻辑的机器人导航系统研究

由于未知环境下机器人导航容易出现死锁问题,设计了一种基于栅格的地图模型叫“数据栅格”,并在此基础上提出了一种基于行为的导航方法即“安全导航法”。数据栅格记录了周围环境中障碍物信息和机器人路径信息,安全导航法就是应用数据栅格技术来解决未知环境下机器人导航遇到的死锁问题。模糊逻辑用来设计和协调各种导航行为。仿真和实际环境的实验结果也证实了该方法的良好性能。     

Simultaneous localization and map building for mobile robotnavigation

Inexpensive ultrasonic sensors, incremental encoders, and grid-based probabilistic modeling are used for improved robot navigation in indoor environments. For model-building, range data from ultrasonic sensors are constantly sampled and a map is built and updated immediately while the robot is travelling through the workspace. The local world model is based on the concept of an occupancy grid. The world model extracted from the range data is based on the geometric primitive of line segments. For the extraction of these features, methods such as the Hough transform and clustering are utilized. The perceived local world model along with dead-reckoning and ultrasonic sensor data are combined using an extended Kalman filter in a localization scheme to estimate the current position and orientation of the mobile robot, which is subsequently fed to the map-building algorithm. Implementation issues and experimental results with the Nomad 150 mobile robot in a real-world indoor environment (office space) are presented     

激光测距在移动机器人自主导航中的应用

移动机器人在运动范围内需要有足够的传感器信息可供利用来进行自主导航,在完全未知的环境中,由机器人依靠其自身携带的传感器所提供的信息建立环境模型是机器人进行自主定位和导航的前提之一。介绍了激光测距在移动机器人自主导航中的应用研究;通过二维测距传感器对其周围环境进行扫描,提出了自主导航中地图创建、定位如何用二维扫描获得三维数据流的算法描述,并实验验证该算法的运用使机器人获得一个很好的三维视觉结构图。     

A Bioinspired Neural Network for Real-Time Concurrent Map Building and Complete Coverage Robot Navigation in Unknown Environments

Complete coverage navigation (CCN) requires a special type of robot path planning, where the robots should pass every part of the workspace. CCN is an essential issue for cleaning robots and many other robotic applications. When robots work in unknown environments, map building is required for the robots to effectively cover the complete workspace. Real-time concurrent map building and complete coverage robot navigation are desirable for efficient performance in many applications. In this paper, a novel neural-dynamics-based approach is proposed for real-time map building and CCN of autoxnomous mobile robots in a completely unknown environment. The proposed model is compared with a triangular-cell-map-based complete coverage path planning method (Oh , 2004) that combines distance transform path planning, wall-following algorithm, and template-based technique. The proposed method does not need any templates, even in unknown environments. A local map composed of square or rectangular cells is created through the neural dynamics during the CCN with limited sensory information. From the measured sensory information, a map of the robot's immediate limited surroundings is dynamically built for the robot navigation. In addition, square and rectangular cell map representations are proposed for real-time map building and CCN. Comparison studies of the proposed approach with the triangular-cell-map-based complete coverage path planning approach show that the proposed method is capable of planning more reasonable and shorter collision-free complete coverage paths in unknown environments.      

激光—相机系统语义栅格建图和路径规划

目的 SLAM（simultaneous localization and mapping）是移动机器人在未知环境进行探索、感知和导航的关键技术。激光SLAM测量精确，便于机器人导航和路径规划，但缺乏语义信息。而视觉SLAM的图像能提供丰富的语义信息，特征区分度更高，但其构建的地图不能直接用于路径规划和导航。为了实现移动机器人构建语义地图并在地图上进行路径规划，本文提出一种语义栅格建图方法。方法 建立可同步获取激光和语义数据的激光-相机系统，将采集的激光分割数据与目标检测算法获得的物体包围盒进行匹配，得到各物体对应的语义激光分割数据。将连续多帧语义激光分割数据同步融入占据栅格地图。对具有不同语义类别的栅格进行聚类，得到标注物体类别和轮廓的语义栅格地图。此外，针对语义栅格地图发布导航任务，利用路径搜索算法进行路径规划，并对其进行改进。结果 在实验室走廊和办公室分别进行了语义栅格建图的实验，并与原始栅格地图进行了比较。在语义栅格地图的基础上进行了路径规划，并采用了语义赋权算法对易移动物体的路径进行对比。结论 多种环境下的实验表明本文方法能获得与真实环境一致性较高、标注环境中物体类别和轮廓的语义栅格地图，且实验硬件结构简单、成本低、性能良好，适用于智能化机器人的导航和路径规划。     

环境特征提取在移动机器人导航中的应用

针对移动机器人在未知结构化环境中导航的需要,采用2D激光雷达作为主要传感器,对诸如墙壁、拐角、出口等这些典型的环境特征分别设计了一套有效的特征提取算法,并在该算法的基础上提出了基于特征点的移动机器人导航策略.该策略不需要里程计等其他一些内部传感器的信息,并且也不依赖具体的环境表述模型,从激光雷达扫描一次所得的数据中即可提取出环境特征,从而来指引机器人导航,实现起来快速可靠.应用到移动机器人MORCS-1上进行实验,取得了满意的结果,算法的实时性与鲁棒性得到了验证.     

基于场景图谱的室内移动机器人目标搜索

在移动机器人执行日常家庭任务时,首先需要其能够在环境中避开障碍物,自主地寻找到房间中的物体。针对移动机器人如何有效在室内环境下对目标物体进行搜索的问题,提出了一种基于场景图谱的室内移动机器人目标搜索,其框架结合了导航地图、语义地图和语义关系图谱。在导航地图的基础上建立了包含地标物体位置信息的语义地图,机器人可以轻松对地标物体进行寻找。对于动态的物体,机器人根据语义关系图中物体之间的并发关系,优先到关系强度比较高的地标物体旁寻找。通过物理实验展示了机器人在语义地图和语义关系图的帮助下可以实现在室内环境下有效地寻找到目标,并显著地减少了搜索的路径长度,证明了该方法的有效性。     

基于轨迹引导的移动机器人导航策略优化算法

针对在杂乱、障碍物密集的复杂环境下移动机器人使用深度强化学习进行自主导航所面临的探索困难,进而导致学习效率低下的问题,提出了一种基于轨迹引导的导航策略优化（TGNPO）算法。首先,使用模仿学习的方法为移动机器人训练一个能够同时提供专家示范行为与导航轨迹预测功能的专家策略,旨在全面指导深度强化学习训练;其次,将专家策略预测的导航轨迹与当前时刻移动机器人所感知的实时图像进行融合,并结合坐标注意力机制提取对移动机器人未来导航起引导作用的特征区域,提高导航模型的学习性能;最后,使用专家策略预测的导航轨迹对移动机器人的策略轨迹进行约束,降低导航过程中的无效探索和错误决策。通过在仿真和物理平台上部署所提算法,实验结果表明,相较于现有的先进方法,所提算法在导航的学习效率和轨迹平滑方面取得了显著的优势。这充分证明了该算法能够高效、安全地执行机器人导航任务。     

Global Navigation in Dynamic Environments Using Case-Based Reasoning

This paper presents a global navigation strategy for autonomous mobile robots in large-scale uncertain environments. The aim of this approach is to minimize collision risk and time delays by adapting to the changes in a dynamic environment. The issue of obstacle avoidance is addressed on the global level. It focuses on a navigation strategy that prevents the robot from facing the situations where it has to avoid obstacles. To model the partially known environment, a grid-based map is used. A modified wave-transform algorithm is described that finds several alternative paths from the start to the goal. Case-based reasoning is used to learn from past experiences and to adapt to the changes in the environment. Learning and adaptation by means of case-based reasoning permits the robot to choose routes that are less risky to follow and lead faster to the goal. The experimental results demonstrate that using case-based reasoning considerably increases the performance of the robot in a difficult uncertain environment. The robot learns to take actions that are more predictable, minimize collision risk and traversal time as well as traveled distances.