Integrated GPS and Inertial Navigation Framework for Precision Control of Autonomous Ground Vehicles

Abstract

Autonomous ground vehicles (AGVs) are rapidly emerging as critical components in logistics, defense, urban mobility, and industrial automation, where precise navigation and control are fundamental to operational success. The inherent challenges of AGV navigation stem from the limitations of conventional positioning systems: Global Positioning Systems (GPS) provide absolute positional data but are susceptible to signal blockage, multipath interference, and urban canyon effects, while Inertial Navigation Systems (INS) offer high-frequency motion tracking but suffer from cumulative drift over time. To address these challenges, this research presents a comprehensive GPS-INS integrated framework designed for precision control of AGVs operating in complex and dynamic environments. The framework leverages a tightly coupled sensor fusion approach, underpinned by Extended Kalman Filter (EKF) algorithms, to dynamically combine GPS measurements with INS-derived motion data. This integration enables real-time error correction, mitigation of sensor biases, and continuous state estimation even under partial GPS degradation. Simulation studies based on urban, semi-urban, and obstacle-dense scenarios demonstrate that the integrated framework significantly reduces positional errors, achieving a mean deviation of less than 0.5 meters from planned trajectories, and ensures robust waypoint navigation during GPS outages. Comparative analysis with standalone GPS or INS systems highlights improvements exceeding 60% in positional accuracy and trajectory fidelity. The study further examines the framework’s resilience, computational considerations, and operational limitations, providing insights into trade-offs between accuracy, processing overhead, and sensor calibration requirements. By synthesizing previous research on sensor integration, IMU bias correction, and vehicular navigation strategies, this work addresses a critical research gap in AGV deployment for real-world applications. The findings underscore the practical applicability of GPS-INS integration in autonomous systems, supporting enhanced reliability, mission success, and safety in both civilian and military domains. The framework offers a scalable and adaptable model for future AGV platforms, with potential extensions including multi-sensor fusion, adaptive control strategies, and field-level validation in dynamic operational settings.