Swept volume aware trajectory planning and tire wear aware tracking for multi-steerable autonomous guided vehicle

Abstract

Multi-Steerable Autonomous Guided Vehicles (MS-AGVs) with independently steerable wheels are widely employed for high-payload transportation, where precise trajectory tracking and maneuverability in constrained spaces are critical. However, conventional control strategies often fail to account for the trade-off between minimizing swept volume and reducing tire wear, both of which are crucial for efficiency, safety, and long-term operational sustainability. In this work, we propose an innovative framework that integrates swept volume minimization with tire wear-aware trajectory tracking through a model predictive control (MPC) approach. Our method employs Signed Distance Field (SDF)-based path planning to generate collision-aware trajectories while actively minimizing swept volume in real-time. Simultaneously, we incorporate a hierarchical control structure that leverages a physics-informed tire model, integrating the Magic Formula tire model and a simplified tire wear model to reduce tire degradation during trajectory tracking. By predicting future states and optimizing individual axle control, our framework ensures precise maneuverability while significantly mitigating tire wear. Experimental validation demonstrates that our approach achieves a 19.19% reduction in tire wear under standard trajectory tracking conditions, while in more challenging scenarios—where the desired trajectory is offset by 60 degrees from the vehicle’s heading—tire wear reduction reaches 65.20% compared to kinematic models that neglect tire wear optimization. This work presents the first comprehensive solution that simultaneously optimizes swept volume and tire wear in MS-AGVs, enhancing both spatial efficiency and vehicle longevity.