Sensor planning with Bayesian decision theory

In this paper ongoing work on an approach for planning sensing actions and controlling intelligent, purposive robotic systems is presented. The method uses Bayesian decision analysis (BDA) for deciding what sensing actions should be performed. This offers a probabilistic framework that provides a more dynamic and modular behaviour than traditional rule based planners. Experiments show that the Bayesian sensor planning strategy is capable of controlling an autonomous mobile robot operating in partly known environments.     

基于轨迹重构与贝叶斯推理的空中机器人灯塔距离测绘技术

灯塔在目前仍然发挥着重大作用,可以在导航和定位过程中提供重要的参考信息;传统方法通常采用无人机导航,但受多路径以及动态环境的干扰,导航误差较大;为精准灯塔的工作范围,提出基于轨迹重构与贝叶斯推理的空中机器人灯塔距离测绘技术;采用捷联式惯性导航重构惯性轨迹,对空中机器人惯性导航;采集灯塔灯光距离测绘数据,构建基于贝叶斯推理的多线索视觉注意模型,获取灯塔距离显著图,搭建航拍摄像机成像模型,计算灯塔灯光照射距离;测试结果表明,该技术平均导航误差最终仅为0.132 mm,且测绘时间较短,通过多次测试后平均灯塔灯光距离测绘误差仅为27.41 cm,整体测绘误差小.     

Visual Navigation for Mobile Robots: A Survey

Mobile robot vision-based navigation has been the source of countless research contributions, from the domains of both vision and control. Vision is becoming more and more common in applications such as localization, automatic map construction, autonomous navigation, path following, inspection, monitoring or risky situation detection. This survey presents those pieces of work, from the nineties until nowadays, which constitute a wide progress in visual navigation techniques for land, aerial and autonomous underwater vehicles. The paper deals with two major approaches: map-based navigation and mapless navigation. Map-based navigation has been in turn subdivided in metric map-based navigation and topological map-based navigation. Our outline to mapless navigation includes reactive techniques based on qualitative characteristics extraction, appearance-based localization, optical flow, features tracking, plane ground detection/tracking, etc... The recent concept of visual sonar has also been revised. This work is partially supported by DPI 2005-09001-C03-02 and FEDER funding.     

Drift-free attitude estimation for accelerated rigid bodies

In this paper we study the attitude estimation problem for an accelerated rigid body using gyros and accelerometers. The application in mind is that of a walking robot and particular attention is paid to the large and abrupt changes in accelerations that can be expected in such an environment. We propose a state estimation algorithm that fuses data from rate gyros and accelerometers to give long-term drift free attitude estimates. The algorithm does not use any local parameterization of the rigid body kinematics and can thus be used for a rigid body performing any kind of rotations. The algorithm is a combination of two non-standard, but in a sense linear, Kalman filters between which a trigger based switching takes place. The kinematics representation used makes it possible to construct a linear algorithm that can be shown to give convergent estimates for this nonlinear problem. The state estimator is evaluated in simulations demonstrating how the estimates are long-term stable even in the presence of gyro drift.     

轮式移动机器人组合导航方法及试验研究

该文提出了以惯性导航为基础,磁感应器修正的移动机器人组合导航方法。该方法以陀螺仪、磁感应器和里程计作为导航信息的检测器件,每隔一定的距离,利用磁感应器检测到的信息对陀螺仪和里程计进行修正,使得移动机器人能够精确定位、长时间稳定运行。一方面,消除了纯惯性导航随时间增长累积的误差;另一方面,对外界环境有较强的抗干扰能力。试验结果验证了该组合导航方法是有效、可行的,适于在线实时应用,能融合其它导航传感器信息,具有较强可扩展性。     

用于高速旋转载体的惯性导航系统设计

针对高速旋转载体的实际应用情况,采用了无陀螺捷联惯性导航技术方案,快速精确测量载体的姿态、速度和位置信息。无陀螺捷联惯导系统采用加速度计来解算载体相对惯性系的角速度,从而代替角速度陀螺仪,提高了系统的高过载的适应能力。首先推导了一种基于六加速度计配置方式力学编排方程,分析了加速度计精度、杆臂长度以及时间等因素对角速度误差产生影响,最后仿真分析了加速度计精度对惯导系统的定位精度的影响,为系统设计提供理论依据。     

Mobile robot localization based on effective combination of vision and range sensors

Most localization algorithms are either range-based or vision-based, but the use of only one type of sensor cannot often ensure successful localization. This paper proposes a particle filter-based localization method that combines the range information obtained from a low-cost IR scanner with the SIFT-based visual information obtained from a monocular camera to robustly estimate the robot pose. The rough estimation of the robot pose by the range sensor can be compensated by the visual information given by the camera and the slow visual object recognition can be overcome by the frequent updates of the range information. Although the bandwidths of the two sensors are different, they can be synchronized by using the encoder information of the mobile robot. Therefore, all data from both sensors are used to estimate the robot pose without time delay and the samples used for estimating the robot pose converge faster than those from either range-based or vision-based localization. This paper also suggests a method for evaluating the state of localization based on the normalized probability of a vision sensor model. Various experiments show that the proposed algorithm can reliably estimate the robot pose in various indoor environments and can recover the robot pose upon incorrect localization. Recommended by Editorial Board member Sooyong Lee under the direction of Editor Hyun Seok Yang. This research was conducted by the Intelligent Robotics Development Program, one of the 21st Century Frontier R&D Programs funded by the Ministry of Knowledge Economy of Korea. Yong-Ju Lee received the B.S. degree in Mechanical Engineering from Korea University in 2004. He is now a Student for Ph.D. of Mechanical Engineering from Korea University. His research interests include mobile robotics. Byung-Doo Yim received the B.S. degree in Control and Instrumentation Engineering from Seoul National University of Technology in 2005. Also, he received the M.S. degree in Mechatroncis Engineering from Korea University in 2007. His research interests include mobile robotics. Jae-Bok Song received the B.S. and M.S. degrees in Mechanical Engineering from Seoul National University in 1983 and 1985, respectively. Also, he received the Ph.D. degree in Mechanical Engineering from MIT in 1992. He is currently a Professor of Mechanical Engineering, Korea University, where he is also the Director of the Intelligent Robotics Laboratory from 1993. His current research interests lie mainly in mobile robotics, safe robot arms, and design/control of intelligent robotic systems.     

Ultrasonic navigation for a wheeled nonholonomic vehicle

In this paper, we demonstrate a reliable and robust system for localization of mobile robots in indoors environments which are relatively consistent to a priori known maps. Through the use of an Extended Kalman Filter combining dead-reckoning, ultrasonic, and infrared sensor data, estimation of the position and orientation of the robot is achieved. Based on a thresholding approach, unexpected obstacles can be detected and their motion predicted. Experimental results from implementation on our mobile robot, Nomad-200, are also presented.     

A robust optical/inertial data fusion system for motion tracking of the robot manipulator

We present an optical/inertial data fusion system for motion tracking of the robot manipulator, which is proved to be more robust and accurate than a normal optical tracking system（OTS）. By data fusion with an inertial measurement unit（IMU）, both robustness and accuracy of OTS are improved. The Kalman filter is used in data fusion. The error distribution of OTS pro-vides an important reference on the estimation of measurement noise using the Kalman filter. With a proper setup of the system and an effective method of coordinate frame synchronization, the results of experiments show a significant improvement in terms of robustness and position accuracy.     

基于DSmT的移动机器人地图构建及传感器管理

针对智能移动机器人探测未知环境的问题,引入了一种新的信息融合方法DSmT(Dezert-Smarandache Theo-ry),采用栅格地图,并根据声纳在DSmT框架下的数学模型,利用经典DSm模型构造了一组能自动调节误差范围的声纳基本信度赋值函数(gbbaf),以处理未知环境下声纳获取的不确定和不精确信息,甚至于高冲突信息。提出了简单有效的传感器管理方法,完全消除了复杂环境下声波的多次反射和串扰现象。最后,用Pioneer 2-DX机器人分别进行了DSmT和DST(Dempster-Shafer Theory)两种算法的地图构建实验,并绘制了相应的二维基本信度赋值地图。将DSmT与DST构建出的环境地图做比较,充分验证了DSmT及提出的传感器管理方法在未知环境下的有效性,为处理动态高冲突信息提供了有力的理论依据。