一种基于抗差EKF的移动机器人定位技术

自主机器人作业的关键问题是自身的定位问题。卡尔曼滤波可用于对系统位置进行估计。首先介绍了移动机器人同步定位与地图构建(Simultaneous Localization and Mapping,SLAM)的一般模型及关键技术,然后介绍了扩展卡尔曼滤波(Extended Kalman Filter,EKF)的原理,通过分析粗差对EKF模型的影响,提出了抗差EKF模型。该模型根据多余观测分量及预测残差统计,构造抗差等价EKF增益矩阵,通过迭代解算给出抗差解。最后分别实现了加入粗差后的标准EKF-SLAM解决方案以及加入粗差后的抗差EKF-SLAM解决方案;模拟了自主机器人运动轨迹,并对比了两种模型对机器人定位的精确度,结果显示了抗差EKF模型的优越性。

Application of Kalman filter on mobile robot self-localization

Self-localization is a fundamental requirement for the mobile robot. Robot usually contains a large number of dif- ferent sensors, which provide the information of robot localization, and all the sensor information should be considered for the optimal location. Kalman filter is efficient to realize the information fusion. Used as an efficient sensor fusion algorithm, Kalman filter is an advanced filtering technique which can reduce errors of the position and orientation of the sensors. Kalman filter has been paied much attention to robot automation and solutions to solve uncertainties such as robot localization, navigation, following, tracking, motion control, estimation and prediction. The paper briefly describes Kalman filter theory, and establishes a simple mathematical model based on muti-sensor mobile robot. Meanwhile, Kalman filter is used in robot self-localization by simulations, and it is demonstrated by simulations that Kalman filter is effective.

Investigation on mobile robot navigation based on Kinect sensor

Mobile robot navigation in unknown environment is an advanced research hotspot.Simultaneous localization and mapping(SLAM)is the key requirement for mobile robot to accomplish navigation.Recently,many researchers study SLAM by using laser scanners,sonar,camera,etc.This paper proposes a method that consists of a Kinect sensor along with a normal laptop to control a small mobile robot for collecting information and building a global map of an unknown environment on a remote workstation.The information(depth data)is communicated wirelessly.Gmapping(a highly efficient Rao-Blackwellized particle filer to learn grid maps from laser range data)parameters have been optimized to improve the accuracy of the map generation and the laser scan.Experiment is performed on Turtlebot to verify the effectiveness of the proposed method.

Development of an Accurate Low-cost Ultrasonic Localization System for Autonomous Mobile Robots in Indoor Environments

An accurate low-cost ultrasonic localization system is de- veloped for automated mobile robots in indoor environments, which is essential for automatic navigation of mobile robots with various tasks. Although ultrasenic sensors are more cost-effective than other sensors such as Laser Range Finder （LRF） and vision, but they are inaccurate and directionally ambiguons. First, the matched filter is used to measure the distance accurately. For resolving the computational complexity of the matched filter, a new matched filter algorithm with simple compution is proposed. Then, an ultrasonic localization system is proposed which consists of three ultrasonic receivers and two or mote transmitters for improving position and orientation accuracy was developed. Finally, an extended Kalman filter is designed to estimate both the static and dynamic positions and orientations. Various simu lations and experimental results show that the proposed system is effective.

Special Wide Area Path Planning Method of an Autonomous Mobile Robot Using GPS

Using sensor and GPS to make a trajectory planning for the stationary obstacle, autonommus mobile robot can asstmae that it is placed at the center of the map, and from the distance information between autonomous mobile robot and obstacles. But in case of active moving obstacle, many components and information need to process since their moving trace should be considered in real time. This paper mobile robot＇s driving algorithm of unknown dynamic envirormaent in order to drive intelligently to destination using ultrasonic and Global Positional Systern （GPS）. Sensors adjusted the placement dependment on driving of robot, and the robot plans the evasion method according to obstacle which are detected by sensors. The robot saves GPS coordinate of complex obstacle. If there are many repeated driving, robot creates new obstacles to the hr, ation by itself. And then it drives to the destination resolving a large range of local minirmnn point If it needs an intelligent circtmtantial decision, a proposed algorithm is suited for effective obstacle avoidance and arrival at the destination by performing simulations.