基于声纳的移动机器人环境建图的设计与实现*

针对未知环境中声纳传感器定位与地图创建时传感数据不确定性高、可靠性低的问题,提出了一种新的室内环境建图方法。该方法建立容忍函数以判断噪声和镜面反射,同时借鉴了ArcTransversal Median Algorithm的思想和栅格概率估计并采用贝叶斯法则进行两次数据融合以减小声纳传感器信息的不确定性。在MORCS2机器人平台上实时创建地图实验表明,这种方法能快速实现从局部地图到全局地图的更新且有较好的精确性与鲁棒性。     

基于不确定网格地图的移动机器人导航

研究了在未知环境下的移动机器人导航问题．在分析超声传感器不确定性模型的基础上,根据模糊集理论创建网格地图来描述机器人工作环境,使用模糊隶属度表示网格占用状态．通过网格信息融合来减弱传感器测量误差,提高网格地图的精度．提出基于模糊网格地图的路径规划算法,利用重复局部优化路径搜索来实现全局路径规划．机器人通过交替进行创建地图和路径规划两个基本过程来完成导航任务．仿真结果表明创建的地图能较精确地表示环境信息。规划的路径可以使机器人安全地到达目的地．     

A mobile robot iconic position estimator using a radial laser scanner

Position determination for a mobile robot is an important part of autonomous navigation. In many cases, dead reckoning is insufficient because it leads to large inaccuracies over time. Beacon- and landmark-based estimators require the emplacement of beacons and the presence of natural or man-made structure respectively in the environment. In this paper, we present a new algorithm for efficiently computing accurate position estimates based on a radially-scanning laser rangefinder that does not require structure in the environment. The algorithm employs a connected set of short line segments to approximate the shape of any environment and can easily be constructed by the rangefinder itself. We describe techniques for efficiently managing the environment map, matching the sensor data to the map, and computing the robot's position. We present accuracy and runtime results for our implementation.A portion of this paper was presented at the IEEE Int. Conference on Robotics and Automation, Nice, France, May 1992.This work was carried out during the stay of the first and third authors in the Field Robotics Center, The Robotics Institute, Carnegie Mellon University, under grant of the Spanish Government. The research was sponsored in part by the United States Bureau of Mines, contract No. 358021.     

Adaptive robot speed control by considering map and motion uncertainty

This paper describes an adaptive robot speed control method for safe and efficient navigation in unknown environments. Safety and efficiency are usually in a trade-off relationship. Moving quickly increases efficiency but decreases safety due to low reliability in localization and environment recognition; moving slowly decreases efficiency but increases safety. Speed control considering this trade-off is important in the following two cases. (1) When the robot enters a narrow free space, it needs to control the speed to avoid any collisions by considering the motion uncertainty. (2) When the robot enters a region whose vacancy (i.e., being free) has not been decided yet, it needs to control the speed so that it can observe the region sufficiently to be confident with the vacancy of the region. This paper proposes a simple but effective strategy for such a speed control that the robot selects the safest fast speed. To adopt this strategy, we define criteria for judging whether a speed is safe for the above two cases. The proposed method successfully makes, the robot move around in unknown static environments by adaptively controlling the speed.     

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.     

基于信息融合的移动机器人侧向定位的研究

超声波传感器的入射角对输出有很大的影响,通过实验分析了入射角对输出的影响。采用引入误差补偿因子的方法,标定了传感器的测量模型,使得测量精度大大提高。在此基础上,基于多源信息融合设计了3种移动机器人侧向定位模型。经实验比较证实,引入误差补偿因子的融合模型定位精度高,可以使得距离误差控制在±2.4mm,姿态角误差控制在±0.32°以内。最后,将该融合模型应用于移动机器人的实际控制中,距离误差为±3.7mm、姿态角误差为±0.58°,满足移动机器人定位精度的要求。     

Base position optimization for mobile painting robot manipulators with multiple constraints

This paper proposes an efficient base position (BP) optimization method for mobile painting robot manipulators (MPRMs). An approximate decoupled model is first established to overcome the coupling problem of painting robots. And the manipulating characteristics are summarized as three constraints: positioning, orientation and singularity avoidance constraints. Then, joint-level performance criteria of one manipulating point and a painting path, which reflect the manipulability and dexterity, were constructed successively. Considering multiple constraints, the BP optimization problem is translated into a standard inequality constrained optimization problem of the path criterion. Two algorithms are designed to solve this problem: one is based on the internal penalty function method used to obtain an initial BP; the other is based on the generalized Lagrange multiplier method used to get the near-optimal BP. This method was applied to a real MPRM system painting three typical surfaces: flat, cylindrical and truncated conical surfaces. Application results demonstrate the effectiveness as well as the availability of the approximate decoupled model. Simultaneously, compared with previous methods, the efficiency is improved by hundreds of times.     

Navigation with uncertain position estimation in the RAM-1 mobile robot

This paper studies the uncertainty in mobile robot navigation. The paper presents a model to propagate the uncertanty in position and orientation when tracking a given path. The model assumes normal distribution with zero means and a covariance matrix which can be computed recursively using the kinematics. The paper also presents the application to the mobile robot RAM-1 designed and built for navigation in outdoor and indoor industrial environments. The proposed method can be used in the navigation system to know the position uncertainty before the vehicles executes a given path. Furthermore, the method is also useful to plan the execution of computationally intensive vision and other external perception functions which are required to avoid the uncertainty growing during navigation.     

Inverse kinematics of a mobile robot

The problem of kinematics is to describe the motion of the robotic system without consideration of the forces and torques causing the motion. This paper presents two methods to obtain the inverse kinematics of a mobile robot. In the first method, two rows of the forward kinematics are selected, the inverse of these two rows is obtained, and later the inverse matrix is combined with the third row of the forward kinematics. In the second method, the pseudo-inverse matrix of the forward kinematics matrix is obtained. The comparison result of the two proposed methods is presented. Two simulations show the effectiveness of the proposed inverse kinematics algorithm.     

Fuzzy control of a mobile robot

This paper describes the design of a new fuzzy logic-based navigation algorithm for autonomous robots. This design effectively achieves correct environment modeling and noisy and uncertain sensory data processing on low-cost hardware equipment. A hierarchical control strategy is presented in which three different reactive behaviors are fused in a single control law by means of a fuzzy supervisor guaranteeing robot safety and task accomplishment. Due to the inherent transparency of fuzzy logic, the proposed algorithm is computationally light, easily reconfigurable, and well-performing in a wide range of differing operating conditions and environments     

Response time of a mobile robot

The response time of an autonomous mobile robot is presented as an internal performance index that characterizes a robot regardless of a particular task. The response time is defined as the time between the occurrence of an event, its detection by a robot, its recognition, the decision making, and the response. For fully autonomous robots and robots that are controlled by and interact with a human, analytical expressions for estimating the response time are presented. An analysis of the robotreconfiguration time when overcoming obstacles is presented. The analytical expressions are supported by numerical examples.     

Path planning for an autonomous mobile robot considering a region with a velocity constraint in a real environment

Recently, many research projects and competitions have attempted to find an autonomous mobile robot that can drive in the real world. In this article, we consider a path-planning method for an autonomous mobile robot that would be safe in a real environment. In such a case, it is very important for the robot to be able to identify its own position and orientation in real time. Therefore, we applied a localization method based on a particle filter. Moreover, in order to improve the safety of such autonomous locomotion, we improved the path-planning algorithm and the generation of the trajectory so that it can consider a region with a limited maximum velocity. In order to demonstrate the validity of the proposed method, we participated in the Real World Robot Challenge 2010. The experimental results are given.     

Development of a separable search-and-rescue robot composed of a mobile robot and a snake robot

Abstract

In this study, we propose a new robot system consisting of a mobile robot and a snake robot. The system works not only as a mobile manipulator but also as a multi-agent system by using the snake robot's ability to separate from the mobile robot. Initially, the snake robot is mounted on the mobile robot in the carrying mode. When an operator uses the snake robot as a manipulator, the robot changes to the manipulator mode. The operator can detach the snake robot from the mobile robot and command the snake robot to conduct lateral rolling motions. In this paper, we present the details of our robot and its performance in the World Robot Summit.     

基于VSLAM的自主移动机器人三维同时定位与地图构建

移动机器人在探索未知环境且没有外部参考系统的情况下,面临着同时定位和地图构建（SLAM）问题。针对基于特征的视觉SLAM（VSLAM）算法构建的稀疏地图不利于机器人应用的问题,提出一种基于八叉树结构的高效、紧凑的地图构建算法。首先,根据关键帧的位姿和深度数据,构建图像对应场景的点云地图;然后利用八叉树地图技术进行处理,构建出了适合于机器人应用的地图。将所提算法同RGB-D SLAM（RGB-Depth SLAM）算法、ElasticFusion算法和ORB-SLAM（Oriented FAST and Rotated BRIEF SLAM）算法通过权威数据集进行了对比实验,实验结果表明,所提算法具有较高的有效性、精度和鲁棒性。最后,搭建了自主移动机器人,将改进的VSLAM系统应用到移动机器人中,能够实时地完成自主避障和三维地图构建,解决稀疏地图无法用于避障和导航的问题。     

A mobile robot based system for fully automated thermal 3D mapping

It is hard to imagine living in a building without electricity and a heating or cooling system these days. Factories and data centers are equally dependent on a continuous functioning of these systems. As beneficial as this development is for our daily life, the consequences of a failure are critical. Malfunctioning power supplies or temperature regulation systems can cause the close-down of an entire factory or data center. Heat and air conditioning losses in buildings lead to a large waste of the limited energy resources and pollute the environment unnecessarily. To detect these flaws as quickly as possible and to prevent the negative consequences constant monitoring of power lines and heat sources is necessary. To this end, we propose a fully automatic system that creates 3D thermal models of indoor environments. The proposed system consists of a mobile platform that is equipped with a 3D laser scanner, an RGB camera and a thermal camera. A novel 3D exploration algorithm ensures efficient data collection that covers the entire scene. The data from all sensors collected at different positions is joined into one common reference frame using calibration and scan matching. In the post-processing step a model is built and points of interest are automatically detected. A viewer is presented that aids experts in analyzing the heat flow and localizing and identifying heat leaks. Results are shown that demonstrate the functionality of the system.     

Navigation control for a mobile robot

This article describes a navigation method of a mobile robot which uses a single camera and a guide mark. A travel path is instructed to the robot by means of path drawn on a monitor screen. The image of the guide mark provides information regarding the robot's position and heading direction. The heading direction is adjusted while moving if any deviation from the specified path is detected. The proposed method has been implemented in a mobile robot which runs at the average speed of 2.5 ft/s. without deviating more than one foot from the specified path in an indoor environment. © 1994 John Wiley & Sons, Inc.     

无线传感器网络环境下基于粒子滤波的移动机器人SLAM算法

定位问题是移动机器人研究领域中最基本的问题,在Bayes的框架下研究了机器人与无线传感器网络(WSN)组成系统中的同时建图与定位问题(SLAM).针对该系统中只存在距离测量信息可用的情况提出了一种基于粒子滤波的SLAM算法.该方法将机器人状态和节点位置估计设置为一组全局估计粒子,通过对粒子及其权重的更新来计算整个系统的状态.算法将WSN节点的位置估计在机器人的路径上分解为相互独立的估计,从而将全局粒子的计算转化为使用一个机器人状态滤波器和对应于每个机器人粒子的节点位置滤波器进行计算.针对观测信息低维的特点,设计了处理低维观测信息的方法,使得观测信息可以在滤波阶段得到合理利用.并且详细介绍了提出的SLAM算法原理和计算过程,并通过仿真实验证明了算法的有效性和实用性.     

Adaptive behavior navigation of a mobile robot

Describes a neural network model for the reactive behavioral navigation of a mobile robot. From the information received through the sensors the robot can elicit one of several behaviors (e.g., stop, avoid, stroll, wall following), through a competitive neural network. The robot is able to develop a control strategy depending on sensor information and learning operation. Reinforcement learning improves the navigation of the robot by adapting the eligibility of the behaviors and determining the linear and angular robot velocities