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An observation algorithm for key motion states of skid-steered wheeled unmanned vehicle
Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering ( IF 1.7 ) Pub Date : 2021-11-12 , DOI: 10.1177/09544070211058353
Xing Zhang 1 , Weiya Pei 1 , Xufeng Yin 1 , Shihua Yuan 1
Affiliation  

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.



中文翻译:

一种滑移轮式无人车关键运动状态观测算法

随着军民对智能车辆需求的不断增加,滑移转向理论已广泛应用于无人地面车辆,特别是无人军用车辆和无人监视平台。由于其行驶环境复杂多变,需要更严格的动态控制系统。为了提高滑移无人车的主动安全性能,发展行为决策规划技术、路径跟踪、动态控制技术等关键技术,需要开发基于滑移转向的动态参数观测系统。转向理论。在本文中,使用具有噪声矩阵自适应的强轨迹外部卡尔曼滤波器理论的观察旨在估计基于 6 × 6 滑移转向无人驾驶车辆的车辆运动学参数。首先,建立运动学和动力学模型来分析滑移轮式车辆的特性。然后提出了一种基于动力学模型的轮胎力估计方法来观测轮胎的经度和垂直力。Dugoff 非线性模型也使用轮胎力数据。然后设计外部卡尔曼滤波器理论来估计车辆运动学参数。为了提高观测器的准确性和鲁棒性,设计了强跟踪EKF(STEKF)和噪声自适应调整。最后,使用 TruckSim 和 Simulink 的组合模拟以及使用 6 × 6 滑移转向无人驾驶车辆的实验验证了观察者的效率。结果表明观察者能够估计滑移转向轮式车辆的状态,并且还表明偏航率导致每个轮胎之间的侧偏角差异。

更新日期:2021-11-13
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