Current Issue : January - March Volume : 2018 Issue Number : 1 Articles : 5 Articles
The grid strapdown inertial navigation system (SINS) used in polar navigation also includes\nthree kinds of periodic oscillation errors as common SINS are based on a geographic coordinate\nsystem. Aiming ships which have the external information to conduct a system reset regularly,\nsuppressing the Schuler periodic oscillation is an effective way to enhance navigation accuracy.\nThe Kalman filter based on the grid SINS error model which applies to the ship is established in this\npaper. The errors of grid-level attitude angles can be accurately estimated when the external velocity\ncontains constant error, and then correcting the errors of the grid-level attitude angles through\nfeedback correction can effectively dampen the Schuler periodic oscillation. The simulation results\nshow that with the aid of external reference velocity, the proposed external level damping algorithm\nbased on the Kalman filter can suppress the Schuler periodic oscillation effectively. Compared with\nthe traditional external level damping algorithm based on the damping network, the algorithm\nproposed in this paper can reduce the overshoot errors when the state of grid SINS is switched from\nthe non-damping state to the damping state, and this effectively improves the navigation accuracy of\nthe system....
Multisensors (LiDAR/IMU/CAMERA) integrated Simultaneous Location and Mapping (SLAM) technology for navigation and\nmobile mapping in a GNSS-denied environment, such as indoor areas, dense forests, or urban canyons, becomes a promising\nsolution. An online (real-time) version of such system can extremely extend its applications, especially for indoor mobile mapping.\nHowever, the real-time response issue of multisensors is a big challenge for an online SLAM system, due to the different sampling\nfrequencies and processing time of different algorithms. In this paper, an online ExtendedKalman Filter (EKF) integrated algorithm\nof LiDAR scan matching and IMU mechanization for Unmanned Ground Vehicle (UGV) indoor navigation system is introduced.\nSince LiDAR scan matching is considerably more time consuming than the IMU mechanism, the real-time synchronous issue\nis solved via a one-step-error-state-transition method in EKF. Stationary and dynamic field tests had been performed using a\nUGV platform along typical corridor of office building. Compared to the traditional sequential postprocessed EKF algorithm, the\nproposed method can significantly mitigate the time delay of navigation outputs under the premise of guaranteeing the positioning\naccuracy, which can be used as an online navigation solution for indoor mobile mapping....
For meeting the demands of cost and size for micronavigation system, a combined attitude determination approach with\nsensor fusion algorithm and intelligent Kalman filter (IKF) on low cost Micro-Electro-Mechanical System (MEMS) gyroscope,\naccelerometer, and magnetometer and single antenna Global Positioning System (GPS) is proposed. The effective calibration\nmethod is performed to compensate the effect of errors in low cost MEMS Inertial Measurement Unit (IMU). The different\ncontrol strategies fusing the MEMS multisensors are designed. The yaw angle fusing gyroscope, accelerometer, and magnetometer\nalgorithm is estimated accurately under GPS failure and unavailable sideslip situations. For resolving robust control and characters\nof the uncertain noise statistics influence, the high gain scale of IKF is adjusted by fuzzy controller in the transition process and\nsteady state to achieve faster convergence and accurate estimation.The experiments comparing differentMEMS sensors and fusion\nalgorithms are implemented to verify the validity of the proposed approach...
Human-friendly interactive features are preferred for domestic service robots. Humans\nprefer to use verbal communication in order to convey instructions to peers. Those voice instructions\noften include uncertain terms such as ââ?¬Å?littleââ?¬Â and ââ?¬Å?farââ?¬Â. Therefore, the ability to quantify such\ninformation is mandatory for human-friendly service robots. The meaning of such voice instructions\ndepends on the environment and the intention of the user. Therefore, this paper proposes a method in\norder to interpret the ambiguities in user instructions based on the environment and the intention of\nthe user. The actual intention of the user is identified by analyzing the pointing gestures accompanied\nwith the voice instructions since pointing gestures can be used in order to express the intention\nof the user. A module called the motion intention switcher (MIS) has been introduced in order to\nswitch the intention of the robot based on the arrangement of the environment and the point referred\nby the gesture. Experiments have been carried out in an artificially-created domestic environment.\nAccording to the experimental results, the behavior of the MIS is effective in identifying the actual\nintention of the user and switching the intention of the robot. Moreover, the proposed concept is\ncapable of enhancing the uncertain information evaluation ability of robots....
Mobile robot should be able to perceive changes in the surrounding environment and in accordance with changes in the\nenvironment appropriate to adjust their action path and behavioral strategies [1]. In the field of military, mobile robot technology\nhas been applied to a variety of advanced unmanned early warning aircraft, demining robots; In the civil field, domestic mobile,\nentertainment, medical and other types of mobile robots more and more people in the field of vision. In short, the mobile robot has\na very broad space for development and application prospects. However, navigation is a necessary problem to be solved by the\nmobile robot, which determines the action set of the mobile robot from the initial point to the target point, and avoids the collision\nwith the obstacle [2,3]. The existing algorithms include grid method, potential force method and fuzzy control method. These\nalgorithms must be designed by the professionals according to the surrounding environment of the robot, and the environment\nchanges will affect the navigation and obstacle avoidance of the mobile robot. And even the need to rewrite the control procedures\nby experts, bringing expensive human and material resources [4,5]. Aiming at the existing navigation algorithms of mobile robots,\nA navigation controller for mobile robot based on batch demonstration learning is proposed. According to the frame of\ndemonstration and the actual situation of the mobile robot, a mobile robot model based on demonstration learning is designed. And\nthe neural network learning algorithm is used to compensate the non-linear term between the environment state and the action in\nthe model. Using the control method proposed in this paper, a two-wheeled mobile robot is used to simulate an arbitrary path in an\nobstacle-free environment in order to realize autonomous navigation....
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