Ultra-Wideband Localization Strategies for Autonomous Transport Vehicle Docking Operations

Autonomous transport vehicles require robust navigation solutions to operate seamlessly across diverse weather conditions. Traditional satellite-based navigation (GNSS), however, is limited under extreme conditions and does not work well without line-of-sight satellite connection. We addressed this problem by implementing a stationary ultra-wideband (UWB) localization system to enhance data acquisition of vehicle position at docking ramps in a hub-to-hub material transport solution. We devised an algorithmic approach-based on an extended Kalman filter (EKF) for simultaneous estimation of synchronization and localization states-to explore four configurations for tracking a vehicle's 2D pose (i.e., position and orientation). Our measurement results demonstrate that estimation accuracy is significantly enhanced by both geometrically interlocking the tags and incorporating a vehicle motion model into the localization algorithm.