Cloud‐based Real‐time Outsourcing Localization for a Ground Mobile Robot in Large‐scale Outdoor Environments

Cloud robotics is the application of cloud computing concepts to robotic systems. It utilizes modern cloud computing infrastructure to distribute computing resources and datasets. Cloud‐based real‐time outsourcing localization architecture is proposed in this paper to allow a ground mobile robot to identify its location relative to a road network map and reference images in the cloud. An update of the road network map is executed in the cloud, as is the extraction of the robot‐terrain inclination (RTI) model as well as reference image matching. A particle filter with a network‐delay‐compensation localization algorithm is executed on the mobile robot based on the local RTI model and the recognized location both of which are sent from the cloud. The proposed methods are tested in different challenging outdoor scenarios with a ground mobile robot equipped with minimal onboard hardware, where the longest trajectory was 13.1 km. Experimental results show that this method could be applicable to large‐scale outdoor environments for autonomous robots in real time.     

Multiple‐Robot Simultaneous Localization and Mapping: A Review

Simultaneous localization and mapping (SLAM) in unknown GPS‐denied environments is a major challenge for researchers in the field of mobile robotics. Many solutions for single‐robot SLAM exist; however, moving to a platform of multiple robots adds many challenges to the existing problems. This paper reviews state‐of‐the‐art multiple‐robot systems, with a major focus on multiple‐robot SLAM. Various issues and problems in multiple‐robot SLAM are introduced, current solutions for these problems are reviewed, and their advantages and disadvantages are discussed.     

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.     

Dual‐tracked mobile robot for motion in challenging terrains

In this paper we present a novel design for a dual‐tracked mobile robot. The robot is designed for safe, stable, and reliable motion in challenging terrains, tunnels, and confined spaces. It consists of two tracked platforms connected with a semipassive mechanism. Sensors attached to the connecting mechanism provide redundant localization data that improve the vehicle's autonomous dead reckoning. Each tracked platform mechanically backs up the other platform, resulting in a more robust and reliable operation. The load share between the platforms enables a high payload‐to‐weight ratio even on soft and slippery terrains. The robot's configurations make it suitable for motion in confined spaces such as underground tunnels, collapsed structures, pipes, and caves. The paper presents the mechanical design of the robot, its kinematic model, stability analysis, and a motion planner. Experimental results conducted on prototype models in various types of environments verify the robot capabilities to operate successfully on challenging terrains. The dual‐tracked robot is capable of climbing slopes 50% steeper than a single robot can. Moreover, the improved odometry system shows high accuracy with 2% error of the total travel distance. © 2011 Wiley Periodicals, Inc.     

A New Mobile Robot Toolbox for Matlab

In this study, a wheeled mobile robot navigation toolbox for Matlab is presented. The toolbox includes algorithms for 3D map design, static and dynamic path planning, point stabilization, localization, gap detection and collision avoidance. One can use the toolbox as a test platform for developing custom mobile robot navigation algorithms. The toolbox allows users to insert/remove obstacles to/from the robot’s workspace, upload/save a customized map and configure simulation parameters such as robot size, virtual sensor position, Kalman filter parameters for localization, speed controller and collision avoidance settings. It is possible to simulate data from a virtual laser imaging detection and ranging (LIDAR) sensor providing a map of the mobile robot’s immediate surroundings. Differential drive forward kinematic equations and extended Kalman filter (EKF) based localization scheme is used to determine where the robot will be located at each simulation step. The LIDAR data and the navigation process are visualized on the developed virtual reality interface. During the navigation of the robot, gap detection, dynamic path planning, collision avoidance and point stabilization procedures are implemented. Simulation results prove the efficacy of the algorithms implemented in the toolbox.     

The study of path error for an omnidirectional home care mobile robot

The first objective of this research was to develop an omnidirectional home care mobile robot. A PC-based controller controls the mobile robot platform. This service mobile robot is equipped with an “indoor positioning system” and an obstacle avoidance system. The indoor positioning system is used for rapid and precise positioning and guidance of the mobile robot. The obstacle avoidance system can detect static and dynamic obstacles. In order to understand the stability of a three-wheeled omnidirectional mobile robot, we carried out some experiments to measure the rectangular and circular path errors of the proposed mobile robot in this research. From the experimental results, we found that the path error was smaller with the guidance of the localization system. The mobile robot can also return to its starting point. The localization system can successfully maintain the robot’s heading angle along a circular path.     

Reconstruction of 3D contour with an active laser‐vision robotic system

This paper presents a 3D contour reconstruction approach employing a wheeled mobile robot equipped with an active laser‐vision system. With observation from an onboard CCD camera, a laser line projector fixed‐mounted below the camera is used for detecting the bottom shape of an object while an actively‐controlled upper laser line projector is utilized for 3D contour reconstruction. The mobile robot is driven to move around the object by a visual servoing and localization technique while the 3D contour of the object is being reconstructed based on the 2D image of the projected laser line. Asymptotical convergence of the closed‐loop system has been established. The proposed algorithm also has been used experimentally with a Dr Robot X80sv mobile robot upgraded with the low‐cost active laser‐vision system, thereby demonstrating effective real‐time performance. This seemingly novel laser‐vision robotic system can be applied further in unknown environments for obstacle avoidance and guidance control tasks. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Implementing distributed control system for intelligent mobile robot

The control system of a mobile robot has a number of issues to deal with in real time, including motion control, mapping, localization, path planning, and sensor processing. Intelligent reasoning, task-oriented behaviors, human–robot interfaces, and communications add more tasks to be solved. This naturally leads to a complex hierarchical control system where various tasks have to be processed concurrently. Many low-level tasks can be handled by a robots onboard (host) computer. However, other tasks, such as speech recognition or vision processing, are too computationally intensive for one computer to process. In this case, it is better to consider a distributed design for the control system in networked environments. In order to achieve maximum use of the distributed environment, it is important to design and implement the distributed system and its communication mechanisms in an effective and flexible way. This article describes our approach to designing and implementing a distributed control system for an intelligent mobile robot. We present our implementation of such a distributed control system for a prototype mobile robot. We focus our discussion on the system architecture, distributed communication mechanisms, and distributed robot control.This work was presented, in part, at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003     

基于超声波传感器的移动机器人定位研究

为了解决超声波传感器在感知环境的过程中的不确定性问题和在定位过程中存在的噪音,以Pioneer 3-AT机器人为实验平台,运用概率算法解决对象本身和对象之间的不确定性关系,理出各种算法之间的内在联系,对移动机器人的定位算法作了相关分析与研究,并利用Mobilesim平台在自建的现场全局地图上进行实验。实验表明:使用改进蒙特—卡罗算法的移动机器人有着较好的定位效果,能够满足实用要求。     

多移动机器人路径规划仿真系统的设计与实现

该文提出了一种多移动机器人路径规划仿真系统的设计方案,并在设计的基础上实现了仿真系统OpenSim。该仿真系统由GUI、业务处理层和存储层组成。GUI是图形用户界面,将仿真系统的各种功能提供给用户;业务处理层进行仿真数据的处理,由仿真平台和各个COM组件服务器组成,机器人传感器、控制器和协调器在COM组件中实现;存储层存储仿真过程中的数据,程序通过在存储层数据库中读写数据来获得和发布信息。该系统具有很好的通用性、分布式运行能力和强大的与用户的交互能力,极大地方便了对多移动机器人路径规划算法的研究。     

A multilevel relaxation algorithm for simultaneous localization and mapping

This paper addresses the problem of simultaneous localization and mapping (SLAM) by a mobile robot. An incremental SLAM algorithm is introduced that is derived from multigrid methods used for solving partial differential equations. The approach improves on the performance of previous relaxation methods for robot mapping, because it optimizes the map at multiple levels of resolution. The resulting algorithm has an update time that is linear in the number of estimated features for typical indoor environments, even when closing very large loops, and offers advantages in handling nonlinearities compared with other SLAM algorithms. Experimental comparisons with alternative algorithms using two well-known data sets and mapping results on a real robot are also presented.     

Development of a sweet pepper harvesting robot

This paper presents the development, testing and validation of SWEEPER, a robot for harvesting sweet pepper fruit in greenhouses. The robotic system includes a six degrees of freedom industrial arm equipped with a specially designed end effector, RGB‐D camera, high‐end computer with graphics processing unit, programmable logic controllers, other electronic equipment, and a small container to store harvested fruit. All is mounted on a cart that autonomously drives on pipe rails and concrete floor in the end‐user environment. The overall operation of the harvesting robot is described along with details of the algorithms for fruit detection and localization, grasp pose estimation, and motion control. The main contributions of this paper are the integrated system design and its validation and extensive field testing in a commercial greenhouse for different varieties and growing conditions. A total of 262 fruits were involved in a 4‐week long testing period. The average cycle time to harvest a fruit was 24 s. Logistics took approximately 50% of this time (7.8 s for discharge of fruit and 4.7 s for platform movements). Laboratory experiments have proven that the cycle time can be reduced to 15 s by running the robot manipulator at a higher speed. The harvest success rates were 61% for the best fit crop conditions and 18% in current crop conditions. This reveals the importance of finding the best fit crop conditions and crop varieties for successful robotic harvesting. The SWEEPER robot is the first sweet pepper harvesting robot to demonstrate this kind of performance in a commercial greenhouse.     

Building ears for robots: Sound localization and separation

This paper describes our research on bio-mimetic robot audition. Among the many binaural and monaural sound localization cues in the human auditory system, the interaural time difference cue is selected as it can easily be obtained by omnidirectional microphones. We have used a three-microphone system to remove the anterior-posterior ambiguity which occurs in two-microphone (or ear) systems. The echo-avoidance model of the precedence effect is used to cope with the echoes and reverberations of real environments. We mimicked the cocktail party effect by perceptual grouping of continuous components according to the spatial information obtained by the sound localization method. A wheel-based mobile robot equipped with an auditory system was developed. The auditory system has two sound processing parts. One is a DSP-based realtime system; the other is an off-line system composed of remote computers. Experiments of localizing and separating multiple sound sources and robot navigation were conducted to demonstrate the system's ability and potential applications.     

Uneven terrain negotiable mobile platform with passively adaptive double tracks and its application to rescue missions

This paper presents the design and integration of ROBHAZ-DT3, a newly developed mobile robot system with a chained double-track mechanism. It is designed to carry out military and civilian missions in various hazardous environments. A passive adaptation mechanism equipped between the front and rear body enables ROBHAZ-DT3 to have good adaptability to uneven terrains, including stairs. The passive adaptation mechanism reduces energy consumption when moving on uneven terrain. It has a simple design and remote control, since no actuators are equipped for adaptation. Based on this new design concept, dynamic analysis and simulations were conducted to verify the mobility of the double-track mechanism, and to obtain significant design parameters such as the optimal track size and allowable attack angle. Also, dynamic effects in vehicle turning were investigated to assess the proper driving torque. Based on this novel mobile mechanism, a rescue version of ROBHAZ-DT3 with appropriate sensors and a semi-autonomous mapping and localization algorithm was developed to participate in the RoboCup 2004 US Open (Urban Search and Rescue Competition). From the various experiments in the realistic arena, we verified that ROBHAZ-DT3 is reliable when traveling over rugged terrain, and that the proposed mapping and localization algorithm are effective in unstructured environments with uneven ground like the rescue area.     

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.     

传感器网络中利用反演集合估计的机器人定位方法

针对大型协作环境中移动机器人的全局定位问题,提出根据机器人车载传感器、环境传感器以及其他机器人的实时数据估计移动机器人的位置。首先,提出的方法整合大量不同类型传感器,从最简单传感器到最复杂传感器;然后,考虑了测量值数量可变、通用测角测量、受容错约束的测量统计知识等约束条件,将非线性边界误差估计问题看作一种反演集合。最后处理特定类型的异常值和不精确环境下的模型误差。完成了误差和异常值的处理,就基本上获得了定位图,解决了移动机器人的定位问题。提出的方法利用实物实验进行验证。测试区域装备有多个传感器、固定在墙顶部的摄像机以及位于机器人上的可见标记。实验结果表明提出的方法在协作环境中具有明显优势,处理异常值更加可靠。     

Into the dirt: Datasets of sewer networks with aerial and ground platforms

This paper presents an unprecedented set of data in a challenging underground environment: the visitable sewers of Barcelona. To the best of our knowledge, this is the first data set involving ground and aerial robots in such scenario: the sewer inspection autonomous robot (SIAR) ground robot and the autonomous robot for sewer inspection aerial platform. These platforms captured data from a great variety of sensors, including sequences of red green blue‐depth (RGB‐D) images with their onboard cameras. The set consists of 14 logs of experiments that were obtained in more than 10 different days and in four different locations. The complete length of the experiments in the data set exceeds 5 km. In addition, we provide the users with a partial ground‐truth and baselines of the localization of the platforms, which can be used for testing their localization and simultaneous localization and mapping (SLAM) algorithms. We also provide details on the setup and execution of each mission and a partial labeling of the elements found in the sewers. All the data were recorded by using the rosbag tool from robot operating system framework. Our goal is to make the data available to the scientific community as a benchmark to test localization, SLAM and classification algorithms in underground environments. The data set are available at https://robotics.upo.es/datasets/echord .     

Real-time appearance-based Monte Carlo localization

A new technique for vision processing is presented which lets a mobile robot equipped with an omnidirectional camera perform appearance-based global localization in real time. The technique is applied directly to the omnidirectional camera images, producing low-dimensional rotation invariant feature vectors without any training or set-up phase. Using the feature vectors, particle filters can accurately estimate the location of a continuously moving real robot, processing 5000 simultaneous localization hypotheses on-line. Estimated body positions overlap the actual ones in over 95% of the time steps. The feature vectors show a graceful degradation against increasing levels of simulated noise and occlusion.     

Sustainable polymorphic colonial robotics and large scale off-world construction

Self-reliant mobile robots have the potential to perform useful and productive work in remote and hostile environments. A colonial architecture, comprising a population of both static operations robots and modular mobile robots, enables self-reliance. Sustainability extends operational life and enables continued operation beyond disabling events. Polymorphism enables performance of multiple roles at optimal efficiency. In this paper, we present algorithms enabling fault detection, identification and rectification whilst achieving and maintaining mobile robot build orders within an error prone environment. Modules that exhibited faulty behaviour are identified and replaced. When a robot is not required then it is disassembled for storage or polymorphed into a new role. Near optimal physical graceful degradation is demonstrated on a robot module scale. The application described in this research is the automated deployment of a large scale off-world human habitat for 10 to 50 personnel from site clearing to radiation shielding.     

Localization With Limited Sensing

Localization is a fundamental problem for many kinds of mobile robots. Sensor systems of varying ability have been proposed and successfully used to solve the problem. This paper probes the lower limits of this range by describing three extremely simple robot models and addresses the active localization problem for each. The robot, whose configuration is composed of its position and orientation, moves in a fully-known, simply connected polygonal environment. We pose the localization task as a planning problem in the robot's information space, which encapsulates the uncertainty in the robot's configuration. We consider robots equipped with: 1) angular and linear odometers; 2) a compass and contact sensor and; 3) an angular odometer and contact sensor. We present localization algorithms for models 1 and 2 and show that no algorithm exists for model 3. An implementation with simulation examples is presented.