From topological knowledge to geometrical map

The behavioral approach to robot navigation, characterized by a representation of the environment that is topological and robot-environmental interactions that are reactive, is preferable to purely geometrical navigation because it is far more robust against unpredictable changes of the environment. Nevertheless, there is still a need to obtain geometrical maps. This paper considers a geometrical map reconstruction that relies on the topological knowledge and uses redundant odometric measurements taken while the robot moves along the paths of the topological map. Five methods are presented and compared, in experiments involving a Nomad200 mobile robot operating in a real environment.     

The visual homing problem: An example of robotics/biology cross fertilization

In this paper, we describe how a mobile robot under simple visual control can retrieve a particular goal location in an open environment. Our model neither needs a precise map nor to learn all the possible positions in the environment. The system is a neural architecture inspired by neurobiological analysis of how visual patterns named landmarks are recognized. The robot merges these visual informations and their azimuth to build a plastic representation of its location. This representation is used to learn the best movement to reach the goal. A simple and fast on-line learning of a few places located near the goal allows this goal to be reached from anywhere in its neighborhood. The system uses only a very rough representation of the robot environment and presents very high generalization capabilities. We describe an efficient implementation of autonomous and motivated navigation tested on our robot in real indoor environments. We show the limitations of the model and its possible extensions.     

Real-time map building and navigation for autonomous robots inunknown environments

An algorithmic solution method is presented for the problem of autonomous robot motion in completely unknown environments. Our approach is based on the alternate execution of two fundamental processes: map building and navigation. In the former, range measures are collected through the robot exteroceptive sensors and processed in order to build a local representation of the surrounding area. This representation is then integrated in the global map so far reconstructed by filtering out insufficient or conflicting information. In the navigation phase, an A*-based planner generates a local path from the current robot position to the goal. Such a path is safe inside the explored area and provides a direction for further exploration. The robot follows the path up to the boundary of the explored area, terminating its motion if unexpected obstacles are encountered. The most peculiar aspects of our method are the use of fuzzy logic for the efficient building and modification of the environment map, and the iterative application of A*, a complete planning algorithm which takes full advantage of local information. Experimental results for a NOMAD 200 mobile robot show the real-time performance of the proposed method, both in static and moderately dynamic environments.     

基于动态精简式混合地图的移动机器人自主探索

针对未知环境下移动机器人自主探索和地图创建问题,在机器人操作系统的框架下,提出一种基于动态精简式混合地图的移动机器人自主探索方法.首先,提出一种基于几何规则的候选目标点生成方法,用于快速提取当前的前沿目标点;然后,从信息收益和路径成本的角度,引入一种改进的效用函数来评价候选目标点;最后,利用缓存增量式的原理优化拓扑节点,进而构建精简式混合地图.实验结果表明,通过拓扑图构建策略的改进,所提出方法具有良好的导航性能.     

Three‐dimensional map building for mobile robot navigation environments using a self‐organizing neural network

In recent years, mobile robots have been required to become more and more autonomous in such a way that they are able to sense and recognize the three‐dimensional space in which they live or work. In this paper, we deal with such an environment map building problem from three‐dimensional sensing data for mobile robot navigation. In particular, the problem to be dealt with is how to extract and model obstacles which are not represented on the map but exist in the real environment, so that the map can be newly updated using the modeled obstacle information. To achieve this, we propose a three‐dimensional map building method, which is based on a self‐organizing neural network technique called “growing neural gas network.” Using the obstacle data acquired from the 3D data acquisition process of an active laser range finder, learning of the neural network is performed to generate a graphical structure that reflects the topology of the input space. For evaluation of the proposed method, a series of simulations and experiments are performed to build 3D maps of some given environments surrounding the robot. The usefulness and robustness of the proposed method are investigated and discussed in detail. © 2004 Wiley Periodicals, Inc.     

Fuzzy potential energy for a map approach to robot navigation

A fully autonomous robot needs a flexible map to solve frequent change of robot situations and/or tasks. In this paper, based on the second type of fuzzy modeling, fuzzy potential energy (FPE) is proposed to build a map that facilitates planning robot tasks for real paths. Three rules for making use of FPEs are derived to ground the basic ideas of building a map for task navigation. How the FPE performs robot navigation is explained by its gradient directions and shown by its gradient trajectories. To code qualitative information into quantity, the proposed FPE provides a way to quickly find a path for conducting the designated task or solving a robot under an embarrassing situation. This paper pioneers novel design and application of fuzzy modeling for a special map that exploits innovation usage of task navigation for real paths. Actually, visibility graphs based on the knowledge of human experts are employed to build FPE maps for navigation. To emphasize the idea of the created FPE, seven remarks direct the roadmap towards being a utility tool for robot navigation. Three illustrative examples, containing three spatial patterns, doors, corridors and cul-de-sacs, are also included. This paper paves the way to create ideas of intelligent navigation for further developments.     

A Bayesian approach for place recognition

This paper presents a robust place recognition algorithm for mobile robots that can be used for planning and navigation tasks. The proposed framework combines nonlinear dimensionality reduction, nonlinear regression under noise, and Bayesian learning to create consistent probabilistic representations of places from images. These generative models are incrementally learnt from very small training sets and used for multi-class place recognition. Recognition can be performed in near real-time and accounts for complexity such as changes in illumination, occlusions, blurring and moving objects. The algorithm was tested with a mobile robot in indoor and outdoor environments with sequences of 1579 and 3820 images, respectively. This framework has several potential applications such as map building, autonomous navigation, search-rescue tasks and context recognition.     

自动驾驶地图的数据标准比较研究

作为实现汽车自动驾驶的关键基础设施,自动驾驶地图能够提供大量准确且语义丰富的数据来帮助用户以更精细的尺度了解周边环境状况,辅助感知、定位、驾驶规划与决策控制,满足智能时代多种高层次的应用需求,进而切实推动我国自动驾驶相关领域的发展与商业化应用。自动驾驶地图的数据标准作为自动驾驶地图生产应用的指导性规范之一,是自动驾驶地图数据标准化的基准。当前我国自动驾驶相关领域对自动驾驶地图标准化的需求迫切,地图数据标准化已成为业界共同关注的热点问题。为解决自动驾驶地图数据标准化问题,切实推动自动驾驶地图的高效发展,本文对自动驾驶地图的数据标准进行比较研究。首先介绍国内外主流的自动驾驶地图数据标准,然后针对其中4种：导航数据标准（navigation data standard,NDS）、OpenDrive、智能运输系统智能驾驶电子地图数据模型与交换格式和道路高精度电子导航地图数据规范进行分析比较研究,主要从数据结构、数据模型、地图渲染和协同应用4个维度展开,并在各个维度上给出数据标准编制时建议遵循的原则。基于分析比较研究的结果,总结出自动驾驶地图数据标准编制时建议遵循的原则。通过对自动驾驶地图的数据标准进行分析比较研究,归纳总结出数据标准编制时建议遵循的原则,这些建议遵循的编制原则对我国相应规格标准的制定具有借鉴意义。     

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。     

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.     

移动机器人导航空间表示及SLAM问题研究

导航研究是移动机器人研究的承要领域之一。 空间表示则是移动机器人导航研究的基础性问题。围绕移动机器人导航空间表示,该文首先对目前广泛采用的空间分解表示,几何特征表示,拓扑地图表示等多种移动机器人导航空间表示方法进行详细的归纳和总结。通过对移动机器人导航空间各种表示疗法进行性能对比,指出各种空间表示方法的优点与不足。最后,对移动机器人导航研究中的同时定位与地图创建（SLAM）问题作了阐述,指出SLAM研究面临的问题,探讨了SLAM的未来研究方向。     

Qualitative spatial representations from task-oriented perception and exploratory behaviors

We describe motor and perceptual behaviors that have proven useful for indoor navigation of an autonomous mobile robot. These behaviors take advantage of the large amount of structure that characterizes many indoor, office-like environments. Based on pre-existing structural landmarks, a mobile robot has the ability to explore, map, and navigate one among several office buildings sharing similar structural features, while coping with slow environment variations and local dynamics. The mobile robot develops and maintains an internal spatial representation of the environment in terms of a topological and qualitative map. The types of structural features suitable as navigation landmarks largely depend upon the available sensors. Adequate navigation performance is achieved by subdividing perception and navigation into a number of behaviors layered upon a multi-threaded real-time control architecture.     

Autonomous navigation system using Event Driven-Fuzzy Cognitive Maps

This study developed an autonomous navigation system using Fuzzy Cognitive Maps (FCM). Fuzzy Cognitive Map is a tool that can model qualitative knowledge in a structured way through concepts and causal relationships. Its mathematical representation is based on graph theory. A new variant of FCM, named Event Driven-Fuzzy Cognitive Maps (ED-FCM), is proposed to model decision tasks and/or make inferences in autonomous navigation. The FCM??s arcs are updated from the occurrence of special events as dynamic obstacle detection. As a result, the developed model is able to represent the robot??s dynamic behavior in presence of environment changes. This model skill is achieved by adapting the FCM relationships among concepts. A reinforcement learning algorithm is also used to finely adjust the robot behavior. Some simulation results are discussed highlighting the ability of the autonomous robot to navigate among obstacles (navigation at unknown environment). A fuzzy based navigation system is used as a reference to evaluate the proposed autonomous navigation system performance.     

Monocular Vision for Mobile Robot Localization and Autonomous Navigation

This paper presents a new real-time localization system for a mobile robot. We show that autonomous navigation is possible in outdoor situation with the use of a single camera and natural landmarks. To do that, we use a three step approach. In a learning step, the robot is manually guided on a path and a video sequence is recorded with a front looking camera. Then a structure from motion algorithm is used to build a 3D map from this learning sequence. Finally in the navigation step, the robot uses this map to compute its localization in real-time and it follows the learning path or a slightly different path if desired. The vision algorithms used for map building and localization are first detailed. Then a large part of the paper is dedicated to the experimental evaluation of the accuracy and robustness of our algorithms based on experimental data collected during two years in various environments.     

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.     

Real-time building of a thinning-based topological map

An accurate and compact map is essential to an autonomous mobile robot system. A topological map, one of the most popular map types, can be used to represent the environment in terms of discrete nodes with edges connecting them. It is usually constructed by Voronoi-like graphs, but in this paper the topological map is incrementally built based on the local grid map by using a thinning algorithm. This algorithm, when combined with the application of the C-obstacle, can easily extract only the meaningful topological information in real-time and is robust to environment change, because this map is extracted from a local grid map generated based on the Bayesian update formula. In this paper, position probability is defined to evaluate the quantitative reliability of the end node extracted by the thinning process. Since the thinning process builds only local topological maps, a global topological map should be constructed by merging local topological maps according to nodes with high position probability. For real and complex environments, experiments showed that the proposed map building method based on the thinning process can accurately build a local topological map in real-time, with which an accurate global topological map can be incrementally constructed.     

移动机器人自主导航系统及上位机软件设计与实现

针对移动机器人自主导航系统,采用C++语言设计了一款基于Qt的跨平台实时数据可视化上位机软件;该软件执行SLAM技术和路径规划算法,实现可视化移动机器人建图与导航过程以及实时读取数据参数等功能;首先介绍移动机器人的硬件结构和功能;其次给出了自主导航所运用到的改进RRT*算法和动态窗口法;在详细叙述上位机软件工作流程的基础上,开发和设计了实时话题显示、读取以及界面可视化等功能;最后基于ROS系统完成移动机器人自主导航功能,并通过实时地图与数据可视化来验证所设计上位机软件功能的有效性。     

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.     

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。     

A view-based neurocomputational system for relational map-making and navigation in visual environments

Artificial navigation systems stand to benefit greatly from learning maps of visual environments, but traditional map-making techniques are inadequate in several respects. This paper describes an adaptive, view-based, relational map-making system for navigating within a 3D environment defined by a spatially distributed set of visual landmarks. Inspired by an analogy to learning aspect graphs of 3D objects, the system comprises two neurocomputational architectures that emulate cognitive mapping in the rat hippocampus. The first architecture performs unsupervised place learning by combining the “What” with the “Where”, namely through conjunctions of landmark identity, pose, and egocentric gaze direction within a local, restricted sensory view of the environment. The second associatively learns action consequences by incorporating the “When”, namely through conjunctions of learned places and coarsely coded robot motions. Together, these networks form a map reminiscent of a partially observable Markov decision process, and consequently provide an ideal neural substrate for prediction, environment recognition, route planning, and exploration. Preliminary results from real-time implementations on a mobile robot called MAVIN (the Mobile Adaptive VIsual Navigator) demonstrate the potential for these capabilities.