An observation algorithm for key motion states of skid-steered wheeled unmanned vehicle

With the increasing demand of military and civilian in the intelligent vehicles, the skid-steering theory has been widely used in unmanned ground vehicles, especially in unmanned military vehicles and unmanned surveillance platforms. Due to its driving environment complex and variable, which requires stricter dynamic control system. In order to improve the active safety performance of the skid-steering unmanned vehicle and develop the key technologies such as behavior decision planning technology, path tracking, and dynamic control technology, it is necessary to develop the dynamic state parameter observation system based on skid-steering theory. In this paper, an observation using Strong Track External Kalman Filter theory with noise matrix adaptive is designed to estimate vehicle kinematic parameters based on a 6 × 6 skid-steered unmanned vehicle. First, kinematic and dynamic model is built to analyze the characters of a skid-steered wheeled vehicle. Then a tire force estimation method based on dynamic model is presented to observe the tire longitude and vertical force. The tire force data is also used by Dugoff nonlinear model. Then an External Kalman Filter theory is designed to estimate vehicle kinematic parameters. To increase the accuracy and the robustness of the observer, the Strong Tracking EKF (STEKF) and noise adaptive adjustment is designed. Finally, a combined simulation using TruckSim and Simulink and the experiment using a 6 × 6 skid-steered unmanned vehicle verifies the efficiency of the observer. Results show that the observer is able to estimate the skid-steered wheeled vehicle states, and it also shows that the yaw rate result in the slip angle difference between each tire.

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2 DOF lateral dynamic model with force input of skid steering wheeled vehicle

2014 IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific) ◽

10.1109/itec-ap.2014.6941009 ◽

2014 ◽

Cited By ~ 1

Author(s):

Zengxiong Peng ◽

Gongtao Ning

Keyword(s):

Dynamic Model ◽

Wheeled Vehicle ◽

Skid Steering

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Sensor Noise Compensation for a Remote Controlled Sprayer Using EKF

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.694-697.1025 ◽

2013 ◽

Vol 694-697 ◽

pp. 1025-1029

Author(s):

Juh Yun An ◽

In Nam Lee ◽

Ki Ho Kim ◽

Kwan Ho You

Keyword(s):

Kalman Filter ◽

Dynamic Model ◽

Extended Kalman Filter ◽

State Equation ◽

Sensor Noise ◽

Localization Accuracy ◽

Noise Compensation ◽

Precision Tracking ◽

Skid Steering

The dynamic model of a remote controlled sprayer using skid-steering method is presented as a state equation. The precision tracking of the remote controlled sprayer is difficult to realize due to sensor noise. In this paper, we propose the extended Kalman filter (EKF) algorithm to compensate for the odometric sensor noise. To demonstrate the performance of the proposed algorithm, simulations which represent a real working sprayer in a greenhouse are performed. The results show the improved localization accuracy obtained by using the proposed algorithm.

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State Estimation of a Robotic Vehicle With Six In-Wheel Drives Using Kalman Filter

ASME 2020 29th Conference on Information Storage and Processing Systems ◽

10.1115/isps2020-1937 ◽

2020 ◽

Author(s):

Hussein F. M. Ali ◽

Se-Woong Oh ◽

Youngshik Kim

Keyword(s):

Kalman Filter ◽

Dynamic Model ◽

Angular Acceleration ◽

Estimation Algorithm ◽

Slip Angle ◽

Test Environment ◽

Slip Ratio ◽

Tire Force ◽

Robotic Vehicle ◽

Tire Forces

Abstract This paper describes an estimation algorithm for a robotic vehicle with articulated suspension (RVAS) to estimate the vehicle velocity and acceleration states, and the tire forces. The RVAS is an unmanned ground vehicle based on a skid steering using an independent in-wheel motor at each wheel. The estimation algorithm consists of five parts. In the first part, a wheel state estimator estimates the wheel rotational speed and its angular acceleration using Kalman filter, which is used to estimate the longitudinal tire force distribution in the second part. The third part is to estimate respective longitudinal, lateral, and vertical speeds of the vehicle and wheels. Based on these speeds, the slip ratio and slip angle are estimated in the fourth part. In the fifth part, the vertical tire force is then estimated. For a simulation test environment, the RVAS dynamic model is developed using Matlab and Simulink. The RVAS model consists of five main parts which include in-wheel motor model, wheel dynamic model, Fiala tire model, arm dynamic model, and the sprung mass dynamic model. The estimation algorithm is then validated using the vehicle test data and different test scenarios. It is found from simulation results that the proposed estimation algorithm can estimate the vehicle states, longitudinal tire forces, and vertical tire forces efficiently.

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Vertical Tire Forces Estimation of Multi-Axle Trucks Based on an Adaptive Treble Extend Kalman Filter

Chinese Journal of Mechanical Engineering ◽

10.1186/s10033-021-00559-2 ◽

2021 ◽

Vol 34 (1) ◽

Author(s):

Buyang Zhang ◽

Ting Xu ◽

Hong Wang ◽

Yanjun Huang ◽

Guoying Chen

Keyword(s):

Kalman Filter ◽

Sliding Mode ◽

Dynamic Control ◽

Force Model ◽

Vertical Force ◽

Extend Kalman Filter ◽

Tire Force ◽

Initial Setting ◽

Estimation System ◽

Tire Forces

AbstractVertical tire forces are essential for vehicle modelling and dynamic control. However, an evaluation of the vertical tire forces on a multi-axle truck is difficult to accomplish. The current methods require a large amount of experimental data and many sensors owing to the wide variation of the parameters and the over-constraint. To simplify the design process and reduce the demand of the sensors, this paper presents a practical approach to estimating the vertical tire forces of a multi-axle truck for dynamic control. The estimation system is based on a novel vertical force model and a proposed adaptive treble extend Kalman filter (ATEKF). To adapt to the widely varying parameters, a sliding mode update is designed to make the ATEKF adaptive, and together with the use of an initial setting update and a vertical tire force adjustment, the overall system becomes more robust. In particular, the model aims to eliminate the effects of the over-constraint and the uneven weight distribution. The results show that the ATEKF method achieves an excellent performance in a vertical force evaluation, and its performance is better than that of the treble extend Kalman filter.

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