scholarly journals Dual-Rate Extended Kalman Filter Based Path-Following Motion Control for an Unmanned Ground Vehicle: Realistic Simulation

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7557
Author(s):  
Rafael Carbonell ◽  
Ángel Cuenca ◽  
Vicente Casanova ◽  
Ricardo Pizá ◽  
Julián J. Salt Llobregat
Keyword(s):  
Kalman Filter ◽  
Motion Control ◽  
Extended Kalman Filter ◽  
Fast Rate ◽  
Path Following ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle ◽  
Realistic Simulation ◽  
The Real ◽  
Position And Orientation

In this paper, a two-wheel drive unmanned ground vehicle (UGV) path-following motion control is proposed. The UGV is equipped with encoders to sense angular velocities and a beacon system which provides position and orientation data. Whereas velocities can be sampled at a fast rate, position and orientation can only be sensed at a slower rate. Designing a dynamic controller at this slower rate implies not reaching the desired control requirements, and hence, the UGV is not able to follow the predefined path. The use of dual-rate extended Kalman filtering techniques enables the estimation of the fast-rate non-available position and orientation measurements. As a result, a fast-rate dynamic controller can be designed, which is provided with the fast-rate estimates to generate the control signal. The fast-rate controller is able to achieve a satisfactory path following, outperforming the slow-rate counterpart. Additionally, the dual-rate extended Kalman filter (DREKF) is fit for dealing with non-linear dynamics of the vehicle and possible Gaussian-like modeling and measurement uncertainties. A Simscape Multibody™ (Matlab®/Simulink) model has been developed for a realistic simulation, considering the contact forces between the wheels and the ground, not included in the kinematic and dynamic UGV representation. Non-linear behavior of the motors and limited resolution of the encoders have also been included in the model for a more accurate simulation of the real vehicle. The simulation model has been experimentally validated from the real process. Simulation results reveal the benefits of the control solution.

Performance Comparison of Inertial Measurement Units Fused With Odometry in Extended Kalman Filter for Dead-Reckoning Navigation

2019 ◽  
Author(s):  
Kevin Carey ◽  
Benjamin Abruzzo ◽  
David P. Harvie ◽  
Christopher Korpela
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Autonomous Vehicle ◽  
Dead Reckoning ◽  
Ground Truth ◽  
Performance Comparison ◽  
Inertial Measurement Units ◽  
Ground Vehicle ◽  
Inertial Measurement ◽  
Measurement Units

Abstract This paper aims to aid robot and autonomous vehicle designers by providing a comparison between four different inertial measurement units (IMUs) which could be used to aid in vehicle navigation in a GPS-denied or inertial-only scenario. A differential-drive ground vehicle was designed to carry the multiple different IMUs, mounted coaxially, to enable direct comparison of performance in a planar environment. The experiments focused on the growth of pose error of the ground vehicle originating from the odometry senors and the IMUs. An extended Kalman Filter was developed to fuse the odometry and inertial measurements for this comparison. The four specific IMUs evaluated were: CNS 5000, Xsens 300, Microstrain GX5-35, and Phidgets 1044 and the ground truth for experiments was provided by an Optitrack motion capture system (MCS). Finally, metrics for choosing IMUs, merging cost and performance considerations, are proposed and discussed. While the CNS 5000 has the best objective error specifications, based on these metrics the Xsens 300 exhibits the best absolute performance while the Phidgets 1044 provides the best performance-per-dollar.

scholarly journals Improvement of position and orientation of Unmanned Arial Vehicle (UAV) with INS/ GPS

2018 ◽  
Vol 7 (2.7) ◽  
pp. 642
Author(s):  
V Appala Raju ◽  
P Vasundhara ◽  
V ChandraKanth Reddy ◽  
A Sai Aiswarya
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Reference System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
State Estimate ◽  
Simulation Results ◽  
Position And Orientation

This paper deals with the methods performing state estimation .that is position and orientation of Unmanned Arial Vehicle (UAV) using GPS, gyro, accelerometers and magnetometer sensors. Various methods are designed for position and orientation measurements of UAV. In this paper we proposed extended kalman filter based inertial navigation system using quaternions and 3D magnetometer. Initially we load UAV truth data from a file ,generate noisy UAV sensor measurements and perform UAV state estimation and display UAV state estimate results with proposed method compares with previously exited method extended  kalman filter based altitude and heading reference system using quaternion and 3D magnetometer simulation .Results shows that EKF-INS method gives better position and orientation of UAV.  

Path Planning Optimization and Motion Control of a Greenhouse Unmanned Ground Vehicle

2021 ◽  
pp. 5145-5156
Author(s):  
Shubo Wang ◽  
Wenhao Dou ◽  
Tongshu Li ◽  
Yu Han ◽  
Zichao Zhang ◽  
...  
Keyword(s):  
Path Planning ◽  
Motion Control ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle ◽  
Planning Optimization

scholarly journals Nonlinear observers design for multivariable ship motion control

2012 ◽  
Vol 19 (Special) ◽  
pp. 50-56 ◽  
Author(s):  
Mirosław Tomera
Keyword(s):  
Mathematical Model ◽  
Kalman Filter ◽  
Comparative Analysis ◽  
Motion Control ◽  
Extended Kalman Filter ◽  
Computer Model ◽  
Ship Motion ◽  
Nonlinear Mathematical Model ◽  
Nonlinear Observers ◽  
Ship Handling

ABSTRACT This paper presents the designs of two observers, which are: the extended Kalman filter and the nonlinear passive observer. Based on the measured values of ship position and heading, the observers estimate the surge, sway and yaw velocities of the ship motion. The observers make use of the simplified nonlinear mathematical model of ship motion in which the neglected ship dynamics and disturbances are modelled using bias. The designed observers firstly have been simulated on a computer model where their parameters were calibrated, and then were implemented on the physical model of the training ship “Blue Lady” in the ship handling centre in Ilawa-Kamionka. The comparative analysis was done with respect to the estimated variables describing the ship motion in three directions: surge, sway and yaw

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