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基于在线滑移估计的越野履带车辆纯追踪算法性能改进

Performance Improvement of Pure Pursuit Algorithm via Online Slip Estimation for Off-Road Tracked Vehicle.

作者信息

Çiloğlu Çağıl, Kutluay Emir

机构信息

Graduate School of Science and Engineering, Hacettepe University, Beytepe, 06800 Ankara, Türkiye.

出版信息

Sensors (Basel). 2025 Jul 8;25(14):4242. doi: 10.3390/s25144242.

DOI:10.3390/s25144242
PMID:40732370
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12300937/
Abstract

The motion control of a tracked mobile robot remains an important capability for autonomous navigation. Kinematic path-tracking algorithms are commonly used in mobile robotics due to their ease of implementation and real-time computational cost advantage. This paper integrates an extended Kalman filter (EKF) into a common kinematic controller for path-tracking performance improvement. The extended Kalman filter estimates the instantaneous center of rotation (ICR) of tracks using the sensor readings of GPS and IMU. These ICR estimations are then given as input to the motion control algorithm to generate the track velocity demands. The platform to be controlled is a heavyweight off-road tracked vehicle, which necessitates the investigation of slip values. A high-fidelity simulation model, which is verified with field tests, is used as the plant in the path-tracking simulations. The performance of the filter and the algorithm is also demonstrated in field tests on a stabilized road. The field results show that the proposed estimation increases the path-tracking accuracy significantly (about 44%) compared to the classical pure pursuit.

摘要

履带式移动机器人的运动控制仍然是自主导航的一项重要能力。运动学路径跟踪算法因其易于实现和实时计算成本优势，在移动机器人技术中被广泛使用。本文将扩展卡尔曼滤波器（EKF）集成到一个常见的运动学控制器中，以提高路径跟踪性能。扩展卡尔曼滤波器利用GPS和IMU的传感器读数估计履带的瞬时旋转中心（ICR）。然后将这些ICR估计值作为输入提供给运动控制算法，以生成履带速度需求。待控制的平台是一辆重型越野履带式车辆，因此有必要研究滑移值。一个经过现场测试验证的高保真仿真模型被用作路径跟踪仿真中的被控对象。滤波器和算法的性能也在稳定道路上的现场测试中得到了验证。现场测试结果表明，与传统的纯跟踪方法相比，所提出的估计方法显著提高了路径跟踪精度（约44%）。

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