兼顾轮胎磨损的多轴机器人轨迹控制，实测磨损降低超65%
Tire Wear Aware Trajectory Tracking Control for Multi-axle Swerve-drive Autonomous Mobile Robots
- 基于预测控制融合轮胎磨损模型，优化轨迹跟踪
- 实测曲线追踪中磨损减少19.19%，复杂场景下达65.20%
- 可在普通电脑实时求解，适合高负载移动机器人
配备独立转向轮的多轴旋翼自主移动机器人（MS-AGV）常用于大载重运输。本文提出一种新型模型预测控制（MPC）方法，将轮胎磨损最小化纳入目标函数。为加速求解，设计分层控制器，并结合 extit{魔力公式轮胎模型}与 extit{简化轮胎磨损模型}简化动态模型。实验表明，该方法在普通个人计算机上通过模拟退火可实时求解；引入磨损优化后，曲线追踪中磨损降低19.19%且保持跟踪精度；在更挑战性场景中（期望轨迹与车辆朝向偏移60度），磨损减少提升至65.20%。
原文摘要 · Abstract (English)
Multi-axle Swerve-drive Autonomous Mobile Robots (MS-AGVs) 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 \textit{magic formula tire model} and \textit{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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。