EKF-FOGS Enhanced Observer For Autonomous Tracked Vehicle Control In Slippery Terrain

Abstract

Autonomous tracked vehicles play a crucial role in precision agriculture, automated construction, and emergency rescue operations. However, maintaining positioning accuracy under slippery terrain conditions remains a significant challenge. This paper proposes the EKF-FOGS (Extended Kalman Filter with Focused Observation for Guided Steering) observer—a state estimation solution capable of maintaining high accuracy when vehicles operate in environments with high slip ratios. The system integrates data from encoders, IMU, and depth cameras while applying an adaptive mechanism that automatically adjusts to changing terrain conditions. The Bekker-Wong soil mechanics model is employed to simulate track-terrain interaction, providing a realistic testing environment. This method addresses the error accumulation problem of odometry by incorporating absolute position references independent of inertial and distance measurements. MATLAB simulations demonstrate that EKF-FOGS significantly improves position estimation accuracy compared to traditional EKF, achieving average errors below one meter in various high-slip scenarios. These results confirm the method's potential for widespread application in GPS-denied positioning systems, particularly for high-precision outdoor tasks.