Effective landmark placement for accurate and reliable mobile robot navigation

Being able to navigate accurately is one of the fundamental capabilities of a mobile robot to effectively execute a variety of tasks including docking, transportation, and manipulation. As real-world environments often contain changing or ambiguous areas, existing features can be insufficient for mobile robots to establish a robust navigation behavior. A popular approach to overcome this problem and to achieve accurate localization is to use artificial landmarks. In this paper, we consider the problem of optimally placing such artificial landmarks for mobile robots that repeatedly have to carry out certain navigation tasks. Our method aims at finding the minimum number of landmarks for which a bound on the maximum deviation of the robot from its desired trajectory can be guaranteed with high confidence. The proposed approach incrementally places landmarks utilizing linearized versions of the system dynamics of the robot, thus allowing for an efficient computation of the deviation guarantee. We evaluate our approach in extensive experiments carried out both in simulations and with real robots. The experiments demonstrate that our method outperforms other approaches and is suitable for long-term operation of mobile robots.     

Interactive Teaching for Vision-Based Mobile Robots: A Sensory-Motor Approach

For the last decade, we have been developing a vision-based architecture for mobile robot navigation. Using our bio-inspired model of navigation, robots can perform sensory-motor tasks in real time in unknown indoor as well as outdoor environments. We address here the problem of autonomous incremental learning of a sensory-motor task, demonstrated by an operator guiding a robot. The proposed system allows for semisupervision of task learning and is able to adapt the environmental partitioning to the complexity of the desired behavior. A real dialogue based on actions emerges from the interactive teaching. The interaction leads the robot to autonomously build a precise sensory-motor dynamics that approximates the behavior of the teacher. The usability of the system is highlighted by experiments on real robots, in both indoor and outdoor environments. Accuracy measures are also proposed in order to evaluate the learned behavior as compared to the expected behavioral attractor. These measures, used first in a real experiment and then in a simulated experiment, demonstrate how a real interaction between the teacher and the robot influences the learning process.     

Learning to transfer optimal navigation policies

Autonomous agents that act in the real world utilizing sensory input greatly rely on the ability to plan their actions and to transfer these skills across tasks. The majority of path-planning approaches for mobile robots, however, solve the current navigation problem from scratch, given the current and goal configuration of the robot. Consequently, these approaches yield highly efficient plans for the specific situation, but the computed policies typically do not transfer to other, similar tasks. In this paper, we propose to apply techniques from statistical relational learning to the path-planning problem. More precisely, we propose to learn relational decision trees as abstract navigation strategies from example paths. Relational abstraction has several interesting and important properties. First, it allows a mobile robot to imitate navigation behavior shown by users or by optimal policies. Second, it yields comprehensible models of behavior. Finally, a navigation policy learned in one environment naturally transfers to unknown environments. In several experiments with real robots and in simulated runs, we demonstrate that our approach yields efficient navigation plans. We show that our system is robust against observation noise and can outperform hand-crafted policies.     

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。     

Probabilistic terrain classification in unstructured environments

Autonomous navigation in unstructured environments is a complex task and an active area of research in mobile robotics. Unlike urban areas with lanes, road signs, and maps, the environment around our robot is unknown and unstructured. Such an environment requires careful examination as it is random, continuous, and the number of perceptions and possible actions are infinite.We describe a terrain classification approach for our autonomous robot based on Markov Random Fields (MRFs ) on fused 3D laser and camera image data. Our primary data structure is a 2D grid whose cells carry information extracted from sensor readings. All cells within the grid are classified and their surface is analyzed in regard to negotiability for wheeled robots.Knowledge of our robot’s egomotion allows fusion of previous classification results with current sensor data in order to fill data gaps and regions outside the visibility of the sensors. We estimate egomotion by integrating information of an IMU, GPS measurements, and wheel odometry in an extended Kalman filter.In our experiments we achieve a recall ratio of about 90% for detecting streets and obstacles. We show that our approach is fast enough to be used on autonomous mobile robots in real time.     

基于激光雷达的动态障碍物实时检测

动态障碍的存在直接影响到环境地图的构建精度,可靠实时地检测出动态障碍物是未知环境下移动机器人构建环境地图的根本前提。基于2D激光雷达传感器,提出了一种移动机器人在未知环境下实时检测动态障碍物的方法。将激光雷达的观测数据经过滤波映射到世界坐标系,构建相邻采样时刻的三幅栅格地图;判断相邻时刻三幅栅格地图上对应栅格的占用状态,确定环境中的静态障碍物,以静态障碍物为参考,根据当前的栅格地图可以检测出环境中的动态障碍物。基于激光雷达时空关联性分析,采用八邻域滚动窗口的方法处理不确定性因素。在实际移动机器人MORCS-1上进行的实验结果表明,该方法可使移动机器人准确有效地检测出未知环境中的动态障碍物,实时性好,可靠性高。     

An algorithm for safe navigation of mobile robots by a sensor network in dynamic cluttered industrial environments

Mobile robots have been widely implemented in industrial automation and smart factories. Different types of mobile robots work cooperatively in the workspace to complete some complicated tasks. Therefore, the main requirement for multi-robot systems is collision-free navigation in dynamic environments. In this paper, we propose a sensor network based navigation system for ground mobile robots in dynamic industrial cluttered environments. A range finder sensor network is deployed on factory floor to detect any obstacles in the field of view and perform a global navigation for any robots simultaneously travelling in the factory. The obstacle detection and robot navigation are integrated into the sensor network and the robot is only required for a low-level path tracker. The novelty of this paper is to propose a sensor network based navigation system with a novel artificial potential field (APF) based navigation algorithm. Computer simulations and experiments confirm the performance of the proposed method.     

Driving on Point Clouds: Motion Planning,Trajectory Optimization,and Terrain Assessment in Generic Nonplanar Environments

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.     

Mobile robot motion estimation by 2D scan matching with genetic and iterative closest point algorithms

The paper reports on mobile robot motion estimation based on matching points from successive two‐dimensional (2D) laser scans. This ego‐motion approach is well suited to unstructured and dynamic environments because it directly uses raw laser points rather than extracted features. We have analyzed the application of two methods that are very different in essence: (i) A 2D version of iterative closest point (ICP), which is widely used for surface registration; (ii) a genetic algorithm (GA), which is a novel approach for this kind of problem. Their performance in terms of real‐time applicability and accuracy has been compared in outdoor experiments with nonstop motion under diverse realistic navigation conditions. Based on this analysis, we propose a hybrid GA‐ICP algorithm that combines the best characteristics of these pure methods. The experiments have been carried out with the tracked mobile robot Auriga‐α and an on‐board 2D laser scanner. © 2006 Wiley Periodicals, Inc.     

Efficient vision-based navigation

In this article, we present a novel approach to learning efficient navigation policies for mobile robots that use visual features for localization. As fast movements of a mobile robot typically introduce inherent motion blur in the acquired images, the uncertainty of the robot about its pose increases in such situations. As a result, it cannot be ensured anymore that a navigation task can be executed efficiently since the robot’s pose estimate might not correspond to its true location. We present a reinforcement learning approach to determine a navigation policy to reach the destination reliably and, at the same time, as fast as possible. Using our technique, the robot learns to trade off velocity against localization accuracy and implicitly takes the impact of motion blur on observations into account. We furthermore developed a method to compress the learned policy via a clustering approach. In this way, the size of the policy representation is significantly reduced, which is especially desirable in the context of memory-constrained systems. Extensive simulated and real-world experiments carried out with two different robots demonstrate that our learned policy significantly outperforms policies using a constant velocity and more advanced heuristics. We furthermore show that the policy is generally applicable to different indoor and outdoor scenarios with varying landmark densities as well as to navigation tasks of different complexity.     

Development of a low-cost active 3D triangulation laser scanner for indoor navigation of miniature mobile robots

For miniaturized mobile robots that aim at exploring unknown environments, no-contact 3D sensing of basic geometrical features of the surrounding environment is one of the most important capabilities for survival and the mission. In this paper, a low-cost active 3D triangulation laser scanner for indoor navigation of miniature mobile robots is presented. It is implemented by moving both a camera and a laser diode together on the robot’s movable part. The movable part is actuated by a servo motor through a gear train to achieve ±90° scanning view angle. The software module includes image processing and data post-processing. 3D world coordinates are calculated from 2D image coordinates based on the triangulation principle. With a 3D laser scanning method, navigation algorithms for obstacle avoidance and gateway passing are proposed. Finally, experiments are conducted to validate performance of the scanner and to test the efficiency of the navigation algorithms.     

A Social Robot that Stands in Line

Recent research in mobile robot navigation make it feasible to utilize autonomous robots in service fields. But, such applications require more than just navigation. To operate in a peopled environment, robots should recognize and act according to human social behavior. In this paper, we present the design and implementation of one such social behavior: a robot that stands in line much as people do. The system employs stereo vision to recognize lines of people, and uses the concept of personal space for modeling the social behavior. Personal space is used both to detect the end of a line and to determine how much space to leave between the robot and the person in front of it. Our model of personal space is based on measurements from people forming lines. We demonstrate our ideas with a mobile robot navigation system that can purchase a cup of coffee, even if people are waiting in line for service.     

Geographical information systems for map based navigation in urban environments

In order to solve most of the existing mobile robotics applications, the robot needs some information about its spatial environment encoded in what it has been commonly called a map. The knowledge contained in such a map, whatever approach is used to obtain it, will mainly be used by the robot to gain the ability to navigate in a given environment. We are describing in this paper, a method that allows a robot or team of robots to navigate in large urban areas for which an existing map in a standard human understandable fashion is available. As detailed maps of most urban areas already exist, it will be assumed that a map of the zone where the robot is supposed to work is given, which has not been constructed using the robot’s own sensors. We propose in this paper, the use of an existing Geographical Information System based map of an urban zone so that a robot or a team of robots can connect to this map and use it for navigation purposes. Details of the implemented system architecture as well as a position tracking experiment in a real outdoor environment, a University Campus, are provided.     

基于人脸识别与光流追踪的移动机器人视觉导航方法

针对移动机器人视觉导航中跟踪目标丢失的问题,提出了基于人脸识别与稀疏光流算法(KLT)结合的移动机器人视觉导航方法(FR-KLT视觉导航方法)。采用OpenCV库中的Haar特征提取人脸识别算法实时检测识别目标人脸,通过Harris角点检测获取目标人体特征点,对目标人体进行精准定位;KLT光流追踪法测算目标移动趋势,并预测目标下一刻大致位置。目标人体位置变动时移动机器人对目标进行实时追踪导航。通过Pioneer-LX机器人在真实环境下试验,验证了该方法准确识别并跟踪目标的实时性和有效性。     

一种基于学习向量量化神经网络的图象分割方法

基于视觉传感器实现道路信息的理解是目前移动机器人自主导航的重要研究方向,其中道路图象的正确分割是提取有效路径信息的关键。该文针对复杂、干扰因素多的室外环境下传统方法难以实现道路图象正确分割的问题,提出了一种基于LV Q神经网络的道路图象分割方法。该方法通过选取道路图象的归一化色彩分量为特征向量,应用基于LV Q学习算法的神经网络分类器进行道路与非道路识别;为解决环境噪声对神经网络输出的影响,本文设计了串行级联式四阶形态滤波器实现对神经网络输出的分割图象的滤波处理。通过对实测图象进行分割处理验证了该方法的有效性和鲁棒性,可用于室外环境下机器人的实时视觉导航控制。     

Visual navigation and obstacle avoidance using a steering potential function

Humans have a remarkable ability to navigate using only vision, but mobile robots have not been nearly as successful. We propose a new approach to vision-guided local navigation, based upon a model of human navigation. Our approach uses the relative headings to the goal and to obstacles, the distance to the goal, and the angular width of obstacles, to compute a potential field over the robot heading. This potential field controls the angular acceleration of the robot, steering it towards the goal and away from obstacles. Because the steering is controlled directly, this approach is well suited to local navigation for nonholonomic robots. The resulting paths are smooth and have continuous curvature. This approach is designed to be used with single-camera vision without depth information but can also be used with other kinds of sensors. We have implemented and tested our method on a differential-drive robot and present our experimental results.     

Real-time monocular image-based path detection

In this work, we present a new real-time image-based monocular path detection method. It does not require camera calibration and works on semi-structured outdoor paths. The core of the method is based on segmenting images and classifying each super-pixel to infer a contour of navigable space. This method allows a mobile robot equipped with a monocular camera to follow different naturally delimited paths. The contour shape can be used to calculate the forward and steering speed of the robot. To achieve real-time computation necessary for on-board execution in mobile robots, the image segmentation is implemented on a low-power embedded GPU. The validity of our approach has been verified with an image dataset of various outdoor paths as well as with a real mobile robot.     

地图构建中动态障碍干扰滤除的一种方法

在移动机器人环境建图中,动态障碍物的存在直接影响传感器的读数,导致产生不一致的环境地图,因此,移动机器人构建地图必须滤除动态障碍物干扰。采用直线插补的方法在先前的局部图上搜寻机器人与目标点之间是否存在障碍物,若存在障碍,则可判定该障碍物已移走(即为动态障碍),应该予以滤除。实验结果证明,该方法能在建图过程中有效地滤除动态障碍,并能有效减少静态障碍物探测的误差累积,算法复杂度小。     

基于增强转移网络（ATN）的室外移动机器人道路图像理解

道路图像理解是室外移动机器人视觉导航自主驾驶研究中的一个关键技术 ,由于基于视觉导航的室外移动机器人自主驾驶时 ,对实时性和鲁棒性要求很高 ,因此 ,为了满足室外移动机器人自主驾驶的实时性和鲁棒性要求 ,将人工智能研究句法分析中的一个形式体系——增强转移网络 (ATN )成功地应用于室外移动机器人的道路理解中 ,进而提出了基于 ATN的室外移动机器人道路图像理解算法 ,该算法在统一的 ATN构建思想指导下 ,针对不同的道路情况 ,不仅可以灵活地构建出不同的道理理解 ATN网络 ,还可达到本质上的统一及应用上的灵活。经实验检验 ,该算法在满足系统要求的鲁棒性条件下 ,具有非常高的实时性 ,即能充分地满足自主移动机器人高速自主导航的需要     

Rapid, safe, and incremental learning of navigation strategies

In this paper we propose a reinforcement connectionist learning architecture that allows an autonomous robot to acquire efficient navigation strategies in a few trials. Besides rapid learning, the architecture has three further appealing features. First, the robot improves its performance incrementally as it interacts with an initially unknown environment, and it ends up learning to avoid collisions even in those situations in which its sensors cannot detect the obstacles. This is a definite advantage over nonlearning reactive robots. Second, since it learns from basic reflexes, the robot is operational from the very beginning and the learning process is safe. Third, the robot exhibits high tolerance to noisy sensory data and good generalization abilities. All these features make this learning robot's architecture very well suited to real-world applications. We report experimental results obtained with a real mobile robot in an indoor environment that demonstrate the appropriateness of our approach to real autonomous robot control.