共查询到10条相似文献,搜索用时 62 毫秒
1.
Henrik Rehbinder Author Vitae 《Automatica》2004,40(4):653-659
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. 相似文献
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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. 相似文献
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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. 相似文献
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Mobile robot localization based on effective combination of vision and range sensors 总被引:2,自引:0,他引:2
Yong-Ju Lee Byung-Doo Yim Jae-Bok Song 《International Journal of Control, Automation and Systems》2009,7(1):97-104
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. 相似文献
7.
针对基于图像的无人机运动跟踪方法存在因图像退化带来的错检和漏检问题,提出一种基于手机和无人机多传感器数据融合的运动目标跟踪方法。将手机IMU(Inertial Measurement Unit,惯性测量单元)数据与无人机的IMU和图像数据作为扩展卡尔曼滤波的输入,其中IMU数据用于滤波器的状态估计,并通过将ORB(Oriented FAST and Rotated BRIEF)方法得到的运动目标图像坐标作为卡尔曼滤波的测量更新部分,再将扩展卡尔曼滤波之后的数据用于校正状态估计,进一步提高无人机运动目标跟踪的准确性。设计实验通过实测数据集来模拟无人机跟踪场景,验证该方法的可行性。实验表明,采用多传感器数据融合的无人机运动目标跟踪方法能够达到0.67m的定位误差,相比于基于图像的方法的精度高,验证了该方法的有效性。 相似文献
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Two methods are available for the estimation of the angular velocity of a rigid body from point-acceleration measurements:
(i) the time-integration of the angular acceleration and (ii) the square-rooting of the centripetal acceleration. The inaccuracy
of the first method is due mainly to the accumulation of the error on the angular acceleration throughout the time-integration
process, which does not prevent that it be used successfully in crash tests with dummies, since these experiments never last
more than one second. On the other hand, the error resulting from the second method is stable through time, but becomes inaccurate
whenever the rigid body angular velocity approaches zero, which occurs in many applications. In order to take advantage of
the complementarity of these two methods, a fusion of their estimates is proposed. To this end, the accelerometer measurements
are modeled as exact signals contaminated with bias errors and Gaussian white noise. The relations between the variables at
stake are written in the form of a nonlinear state-space system in which the angular velocity and the angular acceleration
are state variables. Consequently, a minimum-variance-error estimate of the state vector is obtained by means of extended
Kalman filtering. The performance of the proposed estimation method is assessed by means of simulation. Apparently, the resulting
estimation method is more robust than the existing accelerometer-only methods and competitive with gyroscope measurements.
Moreover, it allows the identification and the compensation of any bias error in the accelerometer measurements, which is
a significant advantage over gyroscopes. 相似文献
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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。 相似文献
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In this paper, a mobile robot control law for corridor navigation and wall-following, based on sonar and odometric sensorial information is proposed. The control law allows for stable navigation avoiding actuator saturation. The posture information of the robot travelling through the corridor is estimated by using odometric and sonar sensing. The control system is theoretically proved to be asymptotically stable. Obstacle avoidance capability is added to the control system as a perturbation signal. A state variables estimation structure is proposed that fuses the sonar and odometric information. Experimental results are presented to show the performance of the proposed control system. 相似文献