移动机器人同时定位与地图创建研究进展

对移动机器人的同时定位与地图创建􀁫(Simultaneous Localization and Mapping)的最新研究进行了综述.指出SLAM 面临的问题,介绍了SLAM的基本实现方法.通过对各种改进的SLAM实现方法的性能对比,详尽地分析了如何降低SLAM的复杂度、提高SLAM的鲁棒性等关键技术问题,同时对多机器人协作的SLAM也进行了论述.探讨了SLAM的研究与发展方向.􀁱     

室外自主移动机器人AMOR的导航技术

在非结构化环境,移动机器人行驶运动规划和自主导航是非常挑战性的问题。基于实时的动态栅格地图,提出了一个快速的而又实效的轨迹规划算法,实现机器人在室外环境的无碰撞运动导航。AMOR是自主研发的室外运动移动机器人,它在2007年欧洲C-ELROB大赛中赢得了野外自主侦察比赛的冠军。它装备了SICK的激光雷达,用来获取机器人运动前方的障碍物体信息,建立实时动态的环境地图。以A*框架为基础的改造算法,能够在众多的路径中快速地找到最佳的安全行驶路径,实现可靠的自主导航。所有的测试和比赛结果表明所提方案是可行的、有效的。     

移动机器人同步定位与地图构建研究进展

同步定位与地图构建（Simultaneous localization and mapping, SLAM）作为能使移动机器人实现全自主导航的工具近来倍受关注.本文对该领域的最新进展进行综述,特别侧重于一些旨在降低计算复杂度的简化算法的分析上,同时对它们进行分类,并指出其优点和不足.本文首先建立了SLAM问题的一般模型,指出了解决SLAM问题的难点;然后详细分析了基于EKF的一些简化算法和基于其他估计思想的方法;最后,对于多机器人SLAM和主动SLAM等前沿课题进行了讨论,并指出了今后的研究方向.     

基于分治法的同步定位与环境采样地图创建

在不使用几何参数描述大规模环境的前提下, 提出了基于分治法的同步定位与环境采样地图创建 (Simultaneous localization and sampled environment mapping, SLASEM)算法来同时进行定位与地图创建. 该算法采用环境采样地图(Sampled environment map, SEM)描述环境, 使算法不局限于用几何参数描述的规则环境. 同时该算法实时地创建局部地图, 并基于分治法合并局部地图, 保证了算法的实时性. 在合并两个子地图时, 算法首先从环境采样地图中提取出角点, 利用角点约束初步更新子地图; 然后利用符号正交距离函数作为虚拟测量函数, 再次细微地更新子地图; 最后将两个子地图合并到一个大地图, 约简冗余的环境采样粒子, 以提高地图的紧凑性. 两个实验的结果验证了所提算法的有效性和实时性.     

强化学习在移动机器人自主导航中的应用

概述了移动机器人常用的自主导航算法及其优缺点,在此基础上提出了强化学习方法。描述了强化学习算法的原理,并实现了用神经网络解决泛化问题。设计了基于障碍物探测传感器信息的机器人自主导航强化学习方法,给出了学习算法中各要素的数学模型。经仿真验证,算法正确有效,具有良好的收敛性和泛化能力。     

Planetary exploration by a mobile robot: Mission teleprogramming and autonomous navigation

Sending mobile robots to accomplish planet exploration missions is scientifically promising and technologically challenging. We present in this paper a complete approach that encompasses the major aspects involved in the design of a robotic system for planetary exploration. It includes mission teleprogramming and supervision at a ground station, and autonomous mission execution by the remote mobile robot. We have partially implemented and validated these concepts. Experimental results illustrate the approach and the results.     

A Discussion of Simultaneous Localization and Mapping

This paper aims at a discussion of the structure of the SLAM problem. The analysis is not strictly formal but based both on informal studies and mathematical derivation. The first part highlights the structure of uncertainty of an estimated map with the key result being “Certainty of Relations despite Uncertainty of Positions”. A formal proof for approximate sparsity of so-called information matrices occurring in SLAM is sketched. It supports the above mentioned characterization and provides a foundation for algorithms based on sparse information matrices. Further, issues of nonlinearity and the duality between information and covariance matrices are discussed and related to common methods for solving SLAM. Finally, three requirements concerning map quality, storage space and computation time an ideal SLAM solution should have are proposed. The current state of the art is discussed with respect to these requirements including a formal specification of the term “map quality”. This article is based on research conducted during the author's Ph.D. studies at the German Aerospace Center (DLR) in Oberpfaffenhofen. Udo Frese was born in Minden, Germany in 1972. He received the Diploma degree in computer science from the University of Paderborn in 1997. From 1998 to 2003 he was a Ph.D. student at the German Aerospace Center in Oberpfaffenhofen. In 2004 he joined University of Bremen. His research interests are mobile robotic, simultaneous localization and mapping and computer vision.     

主体命名方式AST

主体的命名和定位是移动主体中的关键技术,它的优劣影响到整个移动主体系统的性能,在分析了目前几种主体命名和定位方式之后,提出一种新的主体命名和定位的方法：Agent Shadow Tracing。这种方法将主体看成是生成它的主机的一种资源,为主体在生成它的主机上分配一个固定的界面（shadow）,在主体移动时,别的主体和程序可以通过与界面交互达到与移动主体通信的目的,这种方法避免了目前主体命名和定位方式的一些缺点,较好地解决了移动主体之间的通信问题。     

SLAM for autonomous planetary rovers with global localization

This paper describes a novel approach to simultaneous localization and mapping (SLAM) techniques applied to the autonomous planetary rover exploration scenario to reduce both the relative and absolute localization errors, using two well‐proven techniques: particle filters and scan matching. Continuous relative localization is improved by matching high‐resolution sensor scans to the online created local map. Additionally, to avoid issues with drifting localization, absolute localization is globally corrected at discrete times, according to predefined event criteria, by matching the current local map to the orbiter's global map. The resolutions of local and global maps can be appropriately chosen for computation and accuracy purposes. Further, the online generated local map, of the form of a structured elevation grid map, can also be used to evaluate the traversability of the surrounding environment and allow for continuous navigation. The objective of this study is to support long‐range low‐supervision planetary exploration. The implemented SLAM technique has been validated with a data set acquired during a field test campaign performed at the Teide Volcano on the island of Tenerife, representative of a Mars/Moon exploration scenario.     

Localization of an Autonomous Mobile Robot Based on Ultrasonic Sensory Information

Based on ultrasonic sensory information, an approach is proposed for localization of autonomous mobile robot (AMRs). In the proposed method, it will be proven that the combination of three ultrasonic transmitters and two receivers can determine both the position and the orientation of an AMR with respect to a reference frame uniquely. In this manner, since only ultrasonic sensors are used, the proposed method will be highly cost-effective and easy to implement. To show the validity and feasibility of the proposed method, the hardware configuration and a series of experiments will be given for illustration.     

Automatic Mapping of Dynamic Office Environments

We present a robot, InductoBeast, that greets a new office building by learning the floorplan automatically, with minimal human intervention and a priori knowledge. Our robot architecture is unique because it combines aspects of both abductive and inductive mapping methods to solve this problem. We present experimental results spanning three ofiice environments, mapped and navigated during normal business hours. We hope these results help to establish a performance benchmark against which robust and adaptive mapping robots of the future may be measured.     

基于深度学习的移动机器人自主导航实验平台

随着移动机器人的发展,其应用场景越来越复杂,对自主导航这一关键技术提出了更高要求。本文搭建了移动机器人实验平台,设计了基于深度学习的自主导航方法,将RGB图像作为卷积神经网络模型的输入,即可直接输出导航控制信号,不仅降低硬件成本,而且避免复杂的特征工程和规划策略。实验结果表明该平台具有良好的自主导航性能,对移动机器人适应未知复杂环境作业有着重要参考价值。同时,能够为机器人工程专业实践教学提供实验平台,通过开展相关应用拓展,促进学生创新研究能力的培养。     

基于路径识别的移动机器人视觉导航

跟随路径导引是自主式移动机器人广泛采用的一种导航方式,其中视觉导航具有其他传感器导航方式所无法比拟的优点,是移动机器人智能导航的主要发展方向。为了提高移动机器人视觉导航的实时性和准确性,提出了一个基于路径识别的视觉导航系统,其基本思想是首先用基于变分辨率的采样二值化和形态学去噪方法从原始场景图像中提取出目标支持点集,然后用一种改进的哈夫变化检测出场景中的路径,最后由路径跟踪模块分直行和转弯两种情况进行导航计算。实验结果表明,该视觉导航系统具有较好的实时性和准确性。     

移动机器人基于激光测距和单目视觉的室内同时定位和地图构建

该文研究了部分结构化室内环境中自主移动机器人同时定位和地图构建问题.基于激光和视觉传感器模型的不同,加权最小二乘拟合方法和非局部最大抑制算法被分别用于提取二维水平环境特征和垂直物体边缘.为完成移动机器人在缺少先验地图支持的室内环境中的自主导航任务,该文提出了同时进行扩展卡尔曼滤波定位和构建具有不确定性描述的二维几何地图的具体方法.通过对于SmartROB-2移动机器人平台所获得的实验结果和数据的分析讨论,论证了所提出方法的有效性和实用性.     

Autonomous Driving with Concurrent Goals and Multiple Vehicles: Experiments and Mobility Components

In this paper, we describe a complete system for mission planning and execution for multiple robots in natural terrain. We report on experiments with a system for autonomously driving two vehicles based on complex mission specifications. We show that the system is able to plan local paths in obstacle fields based on sensor data, to plan and update global paths to goals based on frequent obstacle map updates, and to modify mission execution, e.g., the assignment and ordering of the goals, based on the updated paths to the goals.Two recently developed sensors are used for obstacle detection: a high-speed laser range finder, and a video-rate stereo system. An updated version of a dynamic path planner, D*, is used for on-line computation of routes. A new mission planning and execution-monitoring tool, GRAMMPS, is used for managing the allocation and ordering of goals between vehicles.We report on experiments conducted in an outdoor test site with two HMMWVs. Implementation details and performance analysis, including failure modes, are described based on a series of twelve experiments, each over 1/2 km distance with up to nine goals.The work reported here includes a number of results not previously published, including the use of a real-time stereo machine and a high-performance laser range finder, and the use of the GRAMMPS planning system.     

Autonomous Driving with Concurrent Goals and Multiple Vehicles: Mission Planning and Architecture

We introduce a new distributed planning paradigm, which permits optimal execution and dynamic replanning of complex multi-goal missions. In particular, the approach permits dynamic allocation of goals to vehicles based on the current environment model while maintaining information-optimal route planning for each individual vehicle to individual goals. Complex missions can be specified by using a grammar in which ordering of goals, priorities, and multiple alternatives can be described. We show that the system is able to plan local paths in obstacle fields based on sensor data, to plan and update global paths to goals based on frequent obstacle map updates, and to modify mission execution, e.g., the assignment and ordering of the goals, based on the updated paths to the goals.The multi-vehicle planning system is based on the GRAMMPS planner; the on-board dynamic route planner is based on the D* planner. Experiments were conducted with stereo and high-speed ladar as the to sensors used for obstacle detection. This paper focuses on the multi-vehicle planner and the systems architecture. A companion paper (Brumitt et al., 2001) analyzes experiments with the multi-vehicle system and describes in details the other components of the system.     

结合类脑导航的强化学习无人机自主导航

随着无人移动平台的不断发展,为其赋予高效的自主导航能力变得尤为重要;针对无人机自主导航常用的端到端强化学习方法存在训练效率低、泛化能力和通用性差等问题,引入了类脑导航模型,基于长短时记忆(LSTM)神经网络构建了类脑细胞导航模型,通过整合编码无人机智能体的自运动信息,实现了网格细胞和头朝向细胞的编码,进一步将这些信息作为深度强化学习算法D3QN的状态补充表示;通过在AirSim仿真环境的实验表明,类脑导航模型的引入能够有效提高算法的训练能力和无人机智能体的导航性能,相较于原D3QN算法,在环境目标改变的情况下仍能寻找到新的目标点,有效提升了算法的泛化能力。     

Integrating Exploration, Localization, Navigation and Planning with a Common Representation

Two major themes of our research include the creation of mobile robot systems that are robust and adaptive in rapidly changing environments, and the view of integration as a basic research issue. Where reasonable, we try to use the same representations to allow different components to work more readily together and to allow better and more natural integration of and communication between these components. In this paper, we describe our most recent work in integrating mobile robot exploration, localization, navigation, and planning through the use of a common representation, evidence grids.     

月球巡视探测器定位技术研究

介绍了月球巡视探测器进行定位的目的及意义．通过对月球探测环境的综合分析,界定了月球环境下探测机器人进行导航定位的相应局限性．对当前国内国际可用的星际探测机器人的定位算法进行了研究,对其中的定位技术进行了归类总结,通过相关文献分析对其中关键算法的可用性、可靠性与自主性进行了评定．最后指出了目前月球巡视探测器定位算法研究中的难点及存在的问题．     

自主移动机器人足球比赛视觉定位方法综述

综述了RoboCup足球赛中全自主移动机器人基于视觉的定位技术,包括机器人自定位和多机器人协作物体定位.介绍了定位技术的发展情况与分类.从机器人环境构建形式的不同以及先验位姿和概率方法的应用与否等方面,系统地分析和比较了各种自定位方法.对于多机器人协作物体定位,阐述了静态方法和动态跟踪方法.总结了定位过程中需要重点研究的传感器模型构建、图像处理、特征匹配以及协作过程涉及的相关问题.最后就视觉定位存在的问题和技术发展趋势进行了讨论.