Tire wear aware trajectory tracking control for Multi-axle Swerve-drive Autonomous Mobile Robots

Multi-axle Swerve-drive Autonomous Mobile Robots (MS-AMRs) equipped with independently steerable wheels are commonly used for high-payload transportation. In this work, we present a novel Model Predictive Control (MPC) method for MS-AGV trajectory tracking that takes tire wear minimization consideration in the objective function. To speed up the problem-solving process, we propose a hierarchical controller design and simplify the dynamic model by integrating the magic formula tire model and simplified tire wear model. In the experiment, the proposed method can be solved by simulated annealing in real-time on a normal personal computer and by incorporating tire wear into the objective function, tire wear is reduced by 19.19% while maintaining the tracking accuracy in curve-tracking experiments. In the more challenging scene: the desired trajectory is offset by 60 degrees from the vehicle’s heading, the reduction in tire wear increased to 65.20% compared to the kinematic model without considering the tire wear optimization.