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

2025-06-01

Journal of Automation and Intelligence

AI Summary

To address trajectory tracking degradation and motion instability in multi-axis swerve-drive AMRs caused by uneven tire wear, this work introduces, for the first time, real-time tire wear state modeling into the motion control closed loop, proposing a wear-aware adaptive trajectory tracking framework. The method integrates tire mechanics modeling, online wear estimation, model predictive control (MPC), and wheel-level torque coordination allocation to achieve dynamic motion optimization under wear constraints. Experimental validation on a physical swerve robot platform demonstrates a 37% reduction in trajectory tracking error and a 2.1× extension of tire lifespan under turning maneuvers. This work breaks from conventional open-loop wear compensation paradigms, establishing a novel, closed-loop-embeddable approach for high-precision, long-lifetime AMR motion control.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Computer Vision: Motion & Tracking

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Problem

• Minimizing tire wear in multi-axle swerve-drive robots
• Real-time trajectory tracking with predictive control
• Balancing tracking accuracy and tire wear reduction

Innovation

• MPC method minimizes tire wear
• Hierarchical controller speeds up solving
• Magic formula simplifies tire model