Hazardous area navigation with temporary beacons

A new area expansion algorithm for the localization scheme, using temporary beacons, is proposed in this paper. The effective area of the active beacons is limited by the strength of the ultrasonic signals in a noisy environment. When a mobile robot needs to move into a hazardous area or into an unstructured environment where the beacons with pre-specified position information are not available, the localization may solely rely on dead reckoning sensors such as encoders. To overcome the error accumulation by using dead-reckoning, a new scheme is developed, in this paper, in which the mobile robot carries a few temporary beacons which do not have any pre-stored position information. When the mobile robot encounters a dangerous or unstructured environment, it utilizes the temporary beacons to localize itself. An auto-calibration algorithm has been developed to provide the position information to the temporary beacons before they are used for the localization. With these temporary beacons and the auto-calibration algorithm, mobile robots can safely pass unstructured areas. The effectiveness of the temporary beacons and auto-calibration algorithm is verified through real experiments of mobile robot navigation.     

Methods for the Reduction of Odometry Errors in Over-Constrained Mobile Robots

This paper presents an analysis of odometry errors in over-constrained mobile robots, that is, vehicles that have more independent motors than degrees of freedom. Examples of over-constrained vehicles are the various 6-wheeled Mars Rovers like Rocky-7, Rocky-8, or Fido.Based on our analysis we developed two novel measures aimed at reducing odometry errors. We also developed a novel method that serves as a framework for the implementation of the two new measures, as well as for other, conventional error reducing measures.One of the two new measures, called Fewest Pulses Measure, makes use of the observation that most terrain irregularities, as well as wheel slip, result in an erroneous overcount of encoder pulses. The second new measure, called Cross-coupled Control Measure, optimizes the motor control algorithm of the robot to reduce synchronization errors that would otherwise result in wheel slip with conventional controllers.The novel method that serves as a framework for other measures is based on so-called Expert Rules. In this paper we formulate three expert rules aimed at reducing dead-reckoning errors. Two of these expert rules are related to the foregoing discussion on error reducing measures. The third expert rule adds a gyroscope to the system and we re-examine the effectiveness of the odometry error-reducing measures in the context of this addition.In the work described in this paper we modified a Pioneer AT skid-steer platform by providing it with four independent drive motors and encoders. We implemented our error-reducing measures and the expert rule method on this over-constrained platform and present experimental results.     

Mobile robot navigation using grid line patterns via probabilistic measurement modeling

Mobile robots are generally equipped with proprioceptive motion sensors such as odometers and inertial sensors. These sensors are used for dead-reckoning navigation in an indoor environment where GPS is not available. However, this dead-reckoning scheme is susceptible to drift error in position and heading. This study proposes using grid line patterns which are often found on the surface of floors or ceilings in an indoor environment to obtain pose (i.e., position and orientation) fix information without additional external position information by artificial beacons or landmarks. The grid lines can provide relative pose information of a robot with respect to the grid structure and thus can be used to correct the pose estimation errors. However, grid line patterns are repetitive in nature, which leads to difficulties in estimating its configuration and structure using conventional Gaussian filtering that represent the system uncertainty using a unimodal function (e.g., Kalman filter). In this study, a probabilistic sensor model to deal with multiple hypotheses is employed and an online navigation filter is designed in the framework of particle filtering. To demonstrate the performance of the proposed approach, an experiment was performed in an indoor environment using a wheeled mobile robot, and the results are presented.     

Image-based fuzzy trajectory tracking control for four-wheel steered mobile robots

A four-wheel steered mobile robot is fit for a higher power or improvement in the movement speed of a robot than a two-independent wheeled one. Since a steered mobile robot that slips very often cannot apply a popular dead-reckoning method using rotary encoders, it is desirable to use external sensors such as cameras. This paper describes a method to trace a straight line for four-wheel steered mobile robots using an image-based control method. Its controller is designed as a fuzzy controller and evaluated through some simulations and real robot.     

Optimal Trajectory Planning for Wheeled Mobile Robots Based on Kinematics Singularity

This research introduces a new optimality criterion for motion planning of wheeled mobile robots based on a cost index that assesses the nearness to singularity of forward and inverse kinematic models. Slip motions, infinite estimation error and impossible control actions are avoided escaping from singularities. In addition, high amplification of wheel velocity errors and high wheel velocity values are also avoided by moving far from the singularity. The proposed cost index can be used directly to complement path-planning and motion-planning techniques (e.g. tree graphs, roadmaps, etc.) in order to select the optimal collision-free path or trajectory among several possible solutions. To illustrate the applications of the proposed approach, an industrial forklift, equivalent to a tricycle-like mobile robot, is considered in a simulated environment. In particular, several results are validated for the proposed optimality criterion, which are extensively compared to those obtained with other classical optimality criteria, such as shortest-path, time-optimal and minimum-energy.     

一种移动机器人轮子打滑的实验校核方法

提出一种机器人不受磁场干扰情况下,利用编码器和电磁罗盘校核轮子打滑的方法．该方法定义机器人轮子打滑的模型,利用该模型能够判断轮子是否发生滑动．当机器人轮子打滑时,提出一种算法来判断哪个驱动轮打滑并校核轮对应的方向误差和位置误差．试验结果表明了本文算法的有效性．     

Closed-loop form quantization algorithm with bounded accumulative error

This paper proposes a closed-loop form quantization algorithm that guarantees the boundness of accumulative error. The algorithm is particularly useful for mobile robot navigation that is usually implemented on embedded systems. If the wheel command of the mobile robot is given by velocity or positional increments at every control instant that are quantized due to the finite word length of the controller's CPU, the quantization error accumulates to produce large position error. Such an error is critical for wheeled mobile robots or autonomous vehicles with non-holonomic constraints. To solve this problem, a non-error accumulative quantization algorithm with a closedloop form is presented. We can extend it to a generalized form corresponding to the nth-order accumulation. The boundness of the accumulative quantization error is proven via mathematic processes and verified by a series of computer simulations. The proposed method is effective to accurately control the autonomous mobile robot, particularly with embedded systems.     

Relative localization using path odometry information

All mobile bases suffer from localization errors. Previous approaches to accommodate for localization errors either use external sensors such as lasers or sonars, or use internal sensors like encoders. An encoder’s information is integrated to derive the robot’s position; this is called odometry. A combination of external and internal sensors will ultimately solve the localization error problem, but this paper focuses only on processing the odometry information. We solve the localization problem by forming a new odometry error model for the synchro-drive robot then use a novel procedure to accurately estimate the error parameters of the odometry error model. This new procedure drives the robot through a known path and then uses the shape of the resulting path to estimate the model parameters. Experimental results validate that the proposed method precisely estimates the error parameters and that the derived odometry error model of the synchro-drive robot is correct. Nakju Lett Doh received his BS, his MS, and his Ph.D. degree in Mechanical Engineering from Pohang University of Science and Technology (POSTECH), KOREA, in 1998, 2000, and 2005, respectively. Since then, he is a senior researcher in Intellgient Robot Reserarch Division, Electronics and Telecommunications Research Institute (ETRI), KOREA. He received the glod prize in Intelligent Robot Contest hosted by Northern KyoungSang Province at 2000 and the gold prize in Humantech Thesis Competition hosted by Samsung Electronics at 2005. In 2003, he got the best student paper award in IEEE International Conference on Robotics and Automation held in Taiwan. His research interests are the localization and navigation of mobile robots and ubiquitous robotic space for intelligent robot navigation. Howie Choset is an Associate Professor of Robotics at Carnegie Mellon University where he conducts research in motion planning and design of serpentine mechanisms, coverage path planning for de-mining and painting, mobile robot sensor based exploration of unknown spaces, and education with robotics. In 1997, the National Science Foundation awarded Choset its Career Award to develop motion planning strategies for arbitrarily shaped objects. In 1999, the Office of Naval Research started supporting Choset through its Young Investigator Program to develop strategies to search for land and sea mines. Recently, the MIT Technology Review elected Choset as one of its top 100 innovators in the world under 35. Choset directs the Undergraduate Robotics Minor at Carnegie Mellon and teaches an overview course on Robotics which uses series of custom developed Lego Labs to complement the course work. Professor Choset’s students have won best paper awards at the RIA in 1999 and ICRA in 2003. Finally, Choset is a member of an urban search and rescue response team using robots with the Center for Robot Assisted Search and Rescue. Now, he is active in extending the mechanism design and path planning work to medical mechatronics. Wan Kyun Chung received his BS degree in Mechanical Design from Seoul National University in 1981, his MS degree in Mechanical Engineering from KAIST in 1983, and his Ph.D. in Production Engineering from KAIST in 1987. He is Professor in the school of Mechanical Engineering, POSTECH (he joined the faculty in 1987). In 1988, he was a visiting professor at the Robotics Institute of Carnegie-Mellon University. In 1995 he was a visiting scholar at the university of California, Berkeley. His research interests include the localization and navigation for mobile robots, underwater robots and development of robust controller for precision motion control. He is a director of National Research Laboratory for Intelligent Mobile Robot Navigation. He is serving as an Associate Editor for IEEE Tr. on Robotics, international editorial board for Advanced Robotics.     

Design and Testing of a 2-DOF Ball Drive

Omnidirectional mobile robots offer interesting features for industrial and service applications, in particular, when operating in tight spaces. Compared to car-like nonholonomic vehicles, they provide a higher degree of maneuverability, and often require less complex path planning and control schemes. Three different types of holonomic wheels that enable omnidirectional motion have been proposed in literature: universal, Mecanum, and ball wheel mechanisms. A problem commonly associated with the first two wheel types is that they induce vibrations in the system due to the discontinuous contact points. In this article, a ball wheel mechanism with superior features including slip measurement, free-wheel modus and attrition sensing is presented. The first prototype was built using additive manufacturing. The requirements for the design and possible improvements for future versions are discussed. Based on the presented ball wheel drive, a design for an omnidirectional mobile robot platform driven by three redundant ball wheel units is proposed. The velocity kinematic model of this mobile base is also addressed. Moreover, motion planning for an individual ball drive is demonstrated by means of an online trajectory generation scheme. The pseudocode of the trajectory planning algorithm implemented in LabVIEW is then presented. Finally, the motion characteristics of the ball drive mechanism are tested and its functionality is evaluated in detail. Measurements obtained from these tests show that the slip between the ball wheel and the ground can be estimated quite accurately. Hence, it is expected that these improved dead-reckoning estimates will result in a higher positioning accuracy of the final base.     

Effects of Depth Perception on Performance of Simulated Materials Handling Tasks

Abstract

A series of experiments were performed using ft laboratory simulation of a fork-lift truck setting task. In general, the same effects were observed as found in an another study using fork-lift trucks except that the direction of errors was reversed, showing a reversal of movement relationships in the simulator. The visual angle between the drivers' direction of motion and his line of sight to the target had a large effect on performance times and error rates. When this angle was less than 12°, both times and errors increased sharply. Performance times at different movement amplitudes and target widths were a linear function of an Index of Difficulty except for target widths of less than 2% of the amplitude. Subject age and experience of fork-lift truck driving had a significant effect of performance. Augmentation of vision using a closed circuit television display was beneficial to performance, reducing times by 14% and errors by 52% showing that this is an economic proposition for materials handling vehicles.     

Simultaneous localization and map building for mobile robotnavigation

Inexpensive ultrasonic sensors, incremental encoders, and grid-based probabilistic modeling are used for improved robot navigation in indoor environments. For model-building, range data from ultrasonic sensors are constantly sampled and a map is built and updated immediately while the robot is travelling through the workspace. The local world model is based on the concept of an occupancy grid. The world model extracted from the range data is based on the geometric primitive of line segments. For the extraction of these features, methods such as the Hough transform and clustering are utilized. The perceived local world model along with dead-reckoning and ultrasonic sensor data are combined using an extended Kalman filter in a localization scheme to estimate the current position and orientation of the mobile robot, which is subsequently fed to the map-building algorithm. Implementation issues and experimental results with the Nomad 150 mobile robot in a real-world indoor environment (office space) are presented     

Bearing-only Cooperative Localization

In cooperative localization a group of robots exchange relative position measurements from their exteroceptive sensors and their motion information from interoceptive sensors to collectively estimate their position and heading. For the localization errors to be bounded, it is required that the system be observable, independent of the estimation technique being used. In this paper, we develop a test-bed of three ground robots, which are equipped with wheel encoders and omnidirectional cameras, to implement the bearing-only cooperative localization. The simulation and experimental results validate the observability conditions, derived in Sharma et al. (IEEE Trans Robot 28:2, 2011), for the complete observability of the bearing-only cooperative localization problem.     

A Method for Estimating Angular Separation in Mobile Wireless Sensor Networks

Resource-constrained mobile sensors require periodic position measurements for navigation around the sensing region. Such information is often obtained using GPS or onboard sensors such as optical encoders. However, GPS is not reliable in all environments, and odometry accrues error over time. Although several localization techniques exist for wireless sensor networks, they are typically time consuming, resource intensive, and/or require expensive hardware, all of which are undesirable for lightweight mobile devices. In this paper, we describe a technique for determining spatial relationships that is suitable for resource-constrained mobile sensors. Angular separation between multiple pairs of stationary sensor nodes is derived using wheel encoder data in conjunction with the measured Doppler shift of an RF interference signal. Our experimental results demonstrate that using this technique, a robot is able to determine the angular separation between four pairs of sensors in a 45 × 35 m sensing region with an average error of 0.28 rad. in 0.68 s.     

车载惯导里程仪组合导航系统安装误差标定研究

研究了捷联惯导、GPS、里程仪和气压高度计构成的组合导航系统中惯导安装误差角对里程仪航位推算精度的影响;提出了以GPS输出作为辅助信息对惯导安装误差进行标定的方法;设计了以里程仪航位推算误差传播方程为系统方程,以里程仪航位推算结果和GSP位置输出之差为量测,通过卡尔曼滤波估计惯导安装误差的标定方法;仿真结果表明,该方法对惯导安装误差的标定精度能达到角秒级。在调试过程中采用该方法标定补偿后的系统实际跑车实验航位推算精度达到5m+行程的0.15%,表明补偿后残余的惯导安装误差影响已经可以忽略。     

Sonar-based robot navigation using nonlinear robust observers

This paper addresses the sonar-based navigation of mobile robots. The extended Kalman filtering (EKF) technique is considered, but from a deterministic, nonstochastic, point of view. For this problem, new results are presented on the robustness of the nonlinear observation scheme. The original feature is that the region-of-convergence question is posed in its complete nonlinear framework, that is, considering the dynamics not only of the estimation error ζ(t), but also of the covariance matrix P(t). In this way the approach followed makes less conservative the treatment and improves the convergence analysis. The proposed ideas were tested successfully on simulation experiments of a mobile platform.     

Learning to select distinctive landmarks for mobile robot navigation

In landmark-based navigation systems for mobile robots, sensory perceptions (e.g., laser or sonar scans) are used to identify the robot’s current location or to construct internal representations, maps, of the robot’s environment. Being based on an external frame of reference (which is not subject to incorrigible drift errors such as those occurring in odometry-based systems), landmark-based robot navigation systems are now widely used in mobile robot applications.The problem that has attracted most attention to date in landmark-based navigation research is the question of how to deal with perceptual aliasing, i.e., perceptual ambiguities. In contrast, what constitutes a good landmark, or how to select landmarks for mapping, is still an open research topic. The usual method of landmark selection is to map perceptions at regular intervals, which has the drawback of being inefficient and possibly missing ‘good’ landmarks that lie between sampling points.In this paper, we present an automatic landmark selection algorithm that allows a mobile robot to select conspicuous landmarks from a continuous stream of sensory perceptions, without any pre-installed knowledge or human intervention during the selection process. This algorithm can be used to make mapping mechanisms more efficient and reliable. Experimental results obtained with two different mobile robots in a range of environments are presented and analysed.     

Fuzzy Motion Planning of Mobile Robots in Unknown Environments

A fuzzy algorithm is proposed to navigate a mobile robot from a given initial configuration to a desired final configuration in an unknown environment filled with obstacles. The mobile robot is equipped with an electronic compass and two optical encoders for dead-reckoning, and two ultrasonic modules for self-localization and environment recognition. From the readings of sensors at every sampling instant, the proposed fuzzy algorithm will determine the priorities of thirteen possible heading directions. Then the robot is driven to an intermediate configuration along the heading direction that has the highest priority. The navigation procedure will be iterated until a collision-free path between the initial and the final configurations is found. To show the feasibility of the proposed method, in addition to computer simulation, experimental results will be also given.     

Development of the remote-controlled platform truck for handling heavy object and the remote control human interface for the disaster response

General interest for robotic technology has been increased by the public and the media after Fukushima Daiichi Nuclear Power Plant (hereafter referred to as 1F) disaster. Especially, robots which can work under the very severe condition where personnel cannot access have demands for development. As to respond to such high demands, NEDO established ‘Disaster response unmanned systems development project’ in 2012.[1] This project is specialized in the development of various remote-controlled equipment, such as remote-controlled platform truck and remote control human interface under ‘Mobile Robot Development’. Remote-controlled platform truck is designed to safely and surely deliver robots and supplies, instead of using stairs and elevator, etc. in and out of building where it is too critical for personnel to work under. Remote control human interface for robots is designed to standardize the command and operation screen for operator, based on opinions from project members, and manned facility construction.[2] In this article, mechanical structure and development tasks for remote-controlled platform truck, and commonalization approach for operation and camera display of various remote-controlled equipment and robot for remote control human interface are stated. Background: due to hydrogen explosion triggered by The Great East Japan Earthquake on 11 March 2011, a reactor building at Fukushima Daiichi Nuclear Power Plant was severely damaged. It is required to reduce high radiation dose in the atmosphere of the reactor building to perform restoration. To pursue decontamination of the reactor building, equipment to lift and carry decontamination devices to upper floor are required.     

Elasto-geometrical calibration of a hybrid mobile robot considering gravity deformation and stiffness parameter errors

Hybrid mobile robots, which combine the advantages of serial and parallel robots and have the ability to realize processing in situ, have considerable application potential in the field of processing and manufacturing. In this paper, a hybrid mobile robot used for wind turbine blade polishing is presented. The robot combines an automated guided vehicle, a 2-DoF robotic arm, and a 3-RCU parallel module. To improve the accuracy, investigating the elasto-geometrical calibration of the robot is necessary. Considering that the 3-RCU parallel module has weak stiffness along the gravitational direction, the stiffness model was established to estimate the deformation caused by the gravity of the mobile platform, ball screws, and motors. Subsequently, a rigid-flexible coupling error model considering structural and stiffness parameter errors is established. Based on these, a parameter identification method for the simultaneous identification of structural and stiffness parameter errors is proposed herein. For the 2-DoF robotic arm with parallelogram mechanisms, an intuitive error model considering the posture error caused by the parallelogram mechanism errors is established. The regularized nonlinear least squares method was adopted for parameter identification. Thereafter, a compensation strategy for the hybrid mobile robot that comprehensively considers the pose errors of the 3-RCU parallel module and 2-DoF robotic arm is proposed. Finally, a verification experiment was performed on the prototype, and the results indicated that after elasto-geometrical calibration, the maximum/mean of the position and posture errors of the hybrid mobile robot decreased from 3.738 mm/2.573 mm to 0.109 mm/0.063 mm and 0.236°/0.179° to 0.030°/0.013°, respectively. Owing to the decrease in the robot pose errors, the quality of the polished surface was more uniform. The range and standard deviation of roughness distribution of the polished surface were reduced from 0.595 μm and 0.248 μm to 0.397 μm and 0.127 μm. The methods proposed herein have reference significance for elasto-geometrical calibration of other parallel or hybrid robots.     

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。