Resilient Navigation for Autonomous Farm Robots by Leveraging Jerk-Augmented Models with IMU-Only Disturbance Rejection
Authors: Batu Candan, Mohammed Atallah, Simone Servadio, Saeed Arabi
Organizations: Batu Candan, PhD Candidate, Iowa State University, Ames, IA 50011, USA · Mohammed Atallah, PhD Candidate, Iowa State University, Ames, IA 50011, USA · Dr. Simone Servadio, Assistant Professor, Iowa State University, Ames, IA 50011, USA · Saeed Arabi is the founder and software lead, Salin247, Boone, IA 50036, USA
Precise state estimation for navigation of autonomous agricultural robots is often compromised by sensor outages (GNSS/LiDAR/Visual) and high-frequency vibrations inherent in off-road environments. This paper proposes a robust navigation algorithm based on a jerk-augmented Extended Kalman Filter (EKF) integrated with a Multiple Tuning Factor (MTF) adaptation method. Unlike standard EKF approaches that assume constant measurement noise, our method dynamically adjusts the measurement covariance matrix in real-time, allowing the system to cope with sudden disturbances and sensor outliers. We evaluate the algorithm using real-world data from a Salin247 autonomous robot. Results demonstrate that jerk-augmentation combined with MTF adaptation significantly reduces 3D position Root Mean Square Error (RMSE) compared to baseline EKF models, providing superior dead-reckoning capabilities.