Saliency and spatial information-based landmark selection for mobile robot navigation in natural environments

In this paper, a landmark selection and tracking approach is presented for mobile robot navigation in natural environments, using textural distinctiveness-based saliency detection and spatial information acquired from stereo data. The presented method focuses on achieving high robustness of tracking rather than self-positioning accuracy. The landmark selection method is designed to select a small amount of the most salient feature points in a wide variety of sparse unknown environments to ensure successful matching. Landmarks are selected by an iterative algorithm from a textural distinctiveness-based saliency map extended with spatial information, where a repulsive potential field is created around the position of each already selected landmark for better distribution in order to increase robustness. The template matching of landmarks is aided with visual odometry-based motion estimation. Other robustness increasing strategies includes estimating landmark positions by unscented Kalman filters as well as from surrounding landmarks. Experimental results show that the introduced method is robust and suitable for natural environments.     

Optimal placement of optical mice for accurate mobile robot velocity estimation

There can be some restrictions during the installation of optical mice at the bottom of a mobile robot, owing to the non-circular base or other existing structures of the robot. This paper presents the optimal placement of optical mice under positional restriction during installation for the velocity estimation of a mobile robot. First, the velocity kinematics between a mobile robot and an array of two or more optical mice is derived, mapping the velocity of a mobile robot in the world coordinate frame to the velocities of optical mice in their local frames. Second, the error characteristics of the velocity estimation of a mobile robot are represented by the uncertainty ellipsoid, and the performance index is then defined as the inverse of the volume of the ellipsoid, which should be minimized for the optimal placement of optical mice. Third, the global optimization strategy is stated in terms of the distances to each optical mouse and the geometrical center of all optical mice, and the local optimization strategy is described as the positional change of each optical mouse, leading to optimal optical mouse placement. Fourth, simulation results for three optical mice within a given elliptical region are given to show how the positional restriction during installation affects the resulting optimal optical mouse placement.     

An enhanced topological map for efficient and reliable mobile robot navigation with imprecise sensors

An enhanced topological mapping system for efficient and reliable navigation is presented. The map has a topological framework and some additional features. Firstly, it utilizes such rough metrical information as the length and orientation of the links. Secondly, it provides a reliable localization algorithm with which the robot first finds the interval describing the robot’s probable location by estimating the projected traveled distance using dead reckoning and then fine-tunes the estimation using landmark detection modules. Finally, it provides a planning algorithm with which the robot’s path is chosen so that the robot reaches the goal location as fast as possible without losing its way despite using such imprecise sensors as ultrasonic range finders.We have implemented and tested the proposed mapping system both on a simulator and a real mobile robot, the CAIR-2. This paper also describes landmark detection modules that utilize ultrasonic range finders. Although landmark detection modules are too simple and imprecise to estimate position by themselves, these experiments show that the proposed mapping system can reliably guide robots.     

Wheeled mobile robot navigation using proportional navigation

We present a method for wheeled mobile robot navigation based on the proportional navigation law. This method integrates the robot's kinematics equations and geometric rules. According to the control strategy, the robot's angular velocity is proportional to the rate of turn of the angle of the line of sight that joins the robot and the goal. We derive a relative kinematics system which models the navigation problem of the robot in polar coordinates. The kinematics model captures the robot path as a function of the control law parameters. It turns out that different paths are obtained for different control parameters. Since the control parameters are real, the number of possible paths is infinite. Results concerning the navigation using our control law are rigorously proven. An extensive simulation confirms our theoretical results.     

Positioning and navigation of mobile robot

Positioning tracking is not a new idea as we have been seen from the ability of the GPS (Global Positioning System) to track the position of the object, in general with acceptable accuracy but the cost of GPS installation is expensive. However, in the case of detecting the exact position in signal-blocked closed environment (e.g. inside building, forest, mines and others); the accuracy factor provide by GPS is low. Thus, this project presents a positioning tracking system that is able to track the movement of an object within a small area or inside buildings. A complete set of the positioning tracking system consists of a pair of computer mechanical mouse and a microcontroller. From the position displayed on the computer screen, the position of the object can be located. The pair of mouse detects each movement of the object and sends the movement data to microcontroller. Linear, angular displacement and positioning calculation are also being discussed. From the results, it’s shown that the positioning system is applicable. However, some small errors are also occurred but in acceptable range. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Control architecture for mobile robot operation and navigation

This paper presents the navigation and operation system (NOS) for a multipurpose industrial autonomous mobile robot for both indoor and outdoor environments. This architecture supports task specification in terms of an event-driven state-based machine that provides high quality mission performance in uncertain environments. All processes in the NOS have been integrated in a distributed architecture designed to consider the real-time constraints of each control level of the system. Particular task models obtained from the system requirements specifications are integrated at the highest level of the architecture so that the rest of the levels remain unchanged for a wide range of industrial applications, such as transportation and operation with onboard devices.     

Appearance-based navigation and homing for autonomous mobile robot

In this paper, we develop an algorithm for navigating a mobile robot using the visual potential. The visual potential is computed from an image sequence and optical flow computed from successive images captured by a camera mounted on the robot, that is, the visual potential for navigation is computed from appearances of the workspace observed as an image sequence. The direction to the destination is provided at the initial position of the robot. The robot dynamically selects a local pathway to the destination without collision with obstacles and without any knowledge of the robot workspace. Furthermore, the guidance algorithm to destination allows the mobile robot to return from the destination to the initial position. We present the experimental results of navigation and homing in synthetic and real environments.     

Autonomous mobile robot navigation and learning

Research focused on the development and experimental validation of intelligent control techniques for autonomous mobile robots able to plan and perform a variety of assigned tasks in unstructured environments is presented. In particular, an autonomous mobile robot, HERMIES-IIB intelligence experiment series, is described. It is a self-powered, wheel-driven platform containing an onboard 16-node Ncube hypercube parallel processor interfaced to effectors and sensors through a VME-based system containing a Motorola 68020 processor, a phased sonar array, dual manipulator arms, and multiple cameras. Research on navigation and learning is examined     

一种机器人导航中自然路标的匹配与跟踪方法

提出了一种基于SIFT和KLT算法的自然路标匹配与跟踪方法。该方法利用SIFT算子提取图像中自然路标的特征点集作为模板,然后将机器人采集图像中的SIFT特征点集与模板特征点集进行匹配,获取二者之间仿射关系,并解算自然路标在视野中的位置,为机器人自定位提供参考信息。机器人在运行过程中,将KLT算法与SIFT算法相结合对成功匹配的自然路标进行跟踪,较好地解决了SIFT算法效率低下的问题。实验结果表明该方法对自然路标具有较好的匹配和跟踪效果。     

用于移动机器人自定位的视觉路标设计

为了实现室内移动机器人的自定位,提出了一种简易美观的新型视觉人工路标以及基于对数极坐标系投影直方图的路标识别方法,并用基于共面四点的位姿估计算法计算机器人位姿.实验结果说明,路标检测具有很高的鲁棒性;路标识别方法抗噪声和形变的能力强;位姿精度足够满足室内移动机器人自定位的需要.     

Using occupancy grids for mobile robot perception and navigation

An approach to robot perception and world modeling that uses a probabilistic tesselated representation of spatial information called the occupancy grid is reviewed. The occupancy grid is a multidimensional random field that maintains stochastic estimates of the occupancy state of the cells in a spatial lattice. To construct a sensor-derived map of the robot's world, the cell state estimates are obtained by interpreting the incoming range readings using probabilistic sensor models. Bayesian estimation procedures allow the incremental updating of the occupancy grid, using readings taken from several sensors over multiple points of view. The use of occupancy grids from mapping and for navigation is examined. Operations on occupancy grids and extensions of the occupancy grid framework are briefly considered     

A neuro-fuzzy controller for mobile robot navigation and multirobotconvoying

A Neural integrated Fuzzy conTroller (NiF-T) which integrates the fuzzy logic representation of human knowledge with the learning capability of neural networks is developed for nonlinear dynamic control problems. NiF-T architecture comprises of three distinct parts: (1) Fuzzy logic Membership Functions (FMF), (2) a Rule Neural Network (RNN), and (3) an Output-Refinement Neural Network (ORNN). FMF are utilized to fuzzify sensory inputs. RNN interpolates the fuzzy rule set; after defuzzification, the output is used to train ORNN. The weights of the ORNN can be adjusted on-line to fine-tune the controller. In this paper, real-time implementations of autonomous mobile robot navigation and multirobot convoying behavior utilizing the NiF-T are presented. Only five rules were used to train the wall following behavior, while nine were used for the hall centering. Also, a robot convoying behavior was realized with only nine rules. For all of the described behaviors-wall following, hall centering, and convoying, their RNN's are trained only for a few hundred iterations and so are their ORNN's trained for only less than one hundred iterations to learn their parent rule sets.     

Target recognition by components for mobile robot navigation

This paper presents a vision-based technique for detecting targets of the environment which have to be reached by an autonomous mobile robot during its navigational tasks. The targets the robot has to reach are the doors of the authors' office building. The detection of the door has been performed by detecting its most significant components in the image and it is based on data classification. Two neural classifiers have been trained for recognizing single components of the door. Then a combining algorithm, based on heuristic considerations, checks that they are in the proper geometric configuration of the structure of the door. The novelty of this work is to use together colour and shape information for identifying features and for detecting the components of the target. The approach, based on learning by components, is able to cleverly solve the problems of scale changes, perspective variations and partial occlusions. The obtained detecting system has been tested on a large test set of real images showing a high reliability and robustness: doors of different rooms, under different illumination conditions and by different viewpoints have been successfully recognized. Results in terms of door detection rate and false positive rate are presented throughout the paper.     

Self-calibration of environmental camera for mobile robot navigation

An environmental camera is a camera embedded in a working environment to provide vision guidance to a mobile robot. In the setup of such robot systems, the relative position and orientation between the mobile robot and the environmental camera are parameters that must unavoidably be calibrated. Traditionally, because the configuration of the robot system is task-driven, these kinds of external parameters of the camera are measured separately and should be measured each time a task is to be performed. In this paper, a method is proposed for the robot system in which calibration of the environmental camera is rendered by the robot system itself on the spot after a system is set up. Specific kinds of motion patterns of the mobile robot, which are called test motions, have been explored for calibration. The calibration approach is based upon executing certain selected test motions on the mobile robot and then using the camera to observe the robot. According to a comparison of odometry and sensing data, the external parameters of the camera can be calibrated. Furthermore, an evaluation index (virtual sensing error) has been developed for the selection and optimization of test motions to obtain good calibration performance. All the test motion patterns are computed offline in advance and saved in a database, which greatly shorten the calibration time. Simulations and experiments verified the effectiveness of the proposed method.     

Vision for mobile robot navigation: a survey

Surveys the developments of the last 20 years in the area of vision for mobile robot navigation. Two major components of the paper deal with indoor navigation and outdoor navigation. For each component, we have further subdivided our treatment of the subject on the basis of structured and unstructured environments. For indoor robots in structured environments, we have dealt separately with the cases of geometrical and topological models of space. For unstructured environments, we have discussed the cases of navigation using optical flows, using methods from the appearance-based paradigm, and by recognition of specific objects in the environment     

农业自主行走机器人视觉导航技术研究

对农业自主行走机器人的单目视觉导航技术展开了研究,包括行走路径的提取方法和机器人的自定位方法.对复杂农田场景和道路场景进行了描述和合理的假设,通过对图像信息的处理和理解,采用改进的Hough变换的方法提取出导航路径,并根据导航路径信息对农业自主行走机器人的自定位技术进行了研究,最终求得机器人相对于导航路径的横向偏离和角度偏差.实验结果表明,该方法能够满足农业机器人自主行走的要求.     

A mobile robot employing insect strategies for navigation

The ability to navigate in a complex environment is crucial for both animals and robots. Many animals use a combination of different strategies to return to significant locations in their environment. For example, the desert ant Cataglyphis is able to explore its desert habitat for hundreds of meters while foraging and return back to its nest precisely and on a straight line. The three main strategies that Cataglyphis is using to accomplish this task are path integration, visual piloting and systematic search. In this study, we use a synthetic methodology to gain additional insights into the navigation behavior of Cataglyphis. Inspired by the insect’s navigation system we have developed mechanisms for path integration and visual piloting that were successfully employed on the mobile robot Sahabot 2. On the one hand, the results obtained from these experiments provide support for the underlying biological models. On the other hand, by taking the parsimonious navigation strategies of insects as a guideline, computationally cheap navigation methods for mobile robots are derived from the insights gained in the experiments.     

Empirical learning in mobile robot navigation

The purpose of this study is to improve the locomotion performance for autonomous mobile robots in outdoor environments. In this paper improvement of an environment model is called empirical locomotion performance leaming. A system avoids wasting time of observations and actions by analyzing data from the last run. We propose a method of empirical learning. The method is expressed by rewriting the rules on the trajectory data. Brief route information for navigating a robot is represented with motion directions at intersections and metric distances between intersections. The behavior of our robot is based on a locomotion strategy 'sign pattern-based stereotyped motion'. The behaviors are implemented on our mobile robot HARUNOBU-4 and tested at our university campus. Experimental results show a robustness of our proposed behaviors under dynamic environments with existing obstacles. Furthermore, they showed that our proposed rewriting rules improved the locomotion performance. In particular, searching time was shortened by 87% (from 453 to 61 s) and the travel distance was shortened by 10% (from 173.8 to 157.5 m).     

基于角点聚类的移动机器人自然路标检测与识别

针对未知环境中机器人视觉导航的自然路标检测,提出了一种基于角点聚类的自然路标局部特征提取、不变性表示及其匹配算法.用SUSAN算子提取左右视图中的角点,在极线约束下对左右视图的角点进行匹配,消除遮挡或噪声引起的角点;同时应用立体视觉计算角点视差,进一步筛选角点.根据角点聚类策略提取自然路标局部特征,并提出不随距离、角度变化的局部特征不变性表示及匹配方法.理论分析和实验结果表明,该算法具有较好的鲁棒性,在一定距离和角度变换下能够对路标进行正确识别.     

Sensing error for a mobile robot using line navigation

The use of a contrasting line for the visual navigation of autonomous mobile robots in a factory environment is developed. Minimum and maximum linewidths are determined analytically by considering sensor geometry, field of view, and error conditions present in the system. The effects of these error conditions on the width of the line, as seen in the image plane, determines the optimal linewidth. Numerical examples using typical sensor parameters are given.