The complex slip characteristics between the tracks and the terrain make it difficult to build an accurate model for intelligent tracked vehicles. Both the path planning and lateral control, however, are highly dependent on the accurate tracked vehicle model. To overcome the issue, a slip model based on the instantaneous centers of rotation (ICRs) of tracks and a dual-layer adaptive unscented Kalman filter (DAUKF) are used to estimate the ICR locations in real-time without requiring prior knowledge of terrain parameters. First, the historical trajectory information is used to estimate ICR locations by the upper-layer AUKF estimator preliminarily. The ICR locations estimated in the upper-layer and the current vehicle state are then imported into the model to predict the future trajectory which can be used to estimate the offset as compensation of preliminarily estimated ICR locations by the lower-layer AUKF estimator. The proposed DAUKF is verified by simulations on MATLAB/Simulink. In order to further verify the effectiveness and practicability of the algorithm, a large number of experiments under different terrains and road conditions are implemented on the electric-drive tracked vehicle. The simulation and experimental results illustrate that the proposed DAUKF can estimate the ICR locations efficiently and accurately, which can improve the accuracy of the tracked vehicle model compared with those of extended Kalman filter (EKF), UKF, and AUKF.

中文翻译：

---

一种改进的使用前向轨迹预测补偿的自主履带车辆实时滑移模型识别方法

履带与地形之间复杂的滑动特性使得为智能履带车辆建立准确的模型变得困难。然而，路径规划和横向控制都高度依赖于准确的履带车辆模型。为了克服这个问题，基于轨道瞬时旋转中心 (ICR) 的滑动模型和双层自适应无迹卡尔曼滤波器 (DAUKF) 用于实时估计 ICR 位置，而无需事先了解地形参数. 首先，上层AUKF估计器利用历史轨迹信息初步估计ICR位置。然后将上层估计的 ICR 位置和当前车辆状态导入模型以预测未来轨迹，该轨迹可用于估计偏移量，作为下层 AUKF 估计器初步估计的 ICR 位置的补偿。提议的 DAUKF 通过 MATLAB/Simulink 上的仿真得到验证。为了进一步验证算法的有效性和实用性，在电驱动履带车上进行了不同地形和路况下的大量实验。仿真和实验结果表明，所提出的 DAUKF 可以高效准确地估计 ICR 位置，与扩展卡尔曼滤波器（EKF）、UKF 和 AUKF 相比，可以提高履带车辆模型的精度。