Tire Wear Aware Trajectory Tracking Control for Multi-axle Swerve-drive Autonomous Mobile Robots

Multi-axle [1] Swerve-drive Autonomous Guided Vehicle (MS-AGV) is a type of heavy-duty vehicle equipped with multiple independently controlled steering wheels. This design provides MS-AGVs with a unique combination of high load capacity [2, 3] and exceptional maneuverability [4, 5], making them highly suitable for complex industrial environments [6, 7], such as automated warehouses and port logistics [8-12]. However, effectively controlling MS-AGVs presents several challenges. These include achieving accurate kino-dynamic modeling [13], ensuring precise trajectory tracking [14], and optimizing speed for operational efficiency [15, 16]. Recent works have explored prescribed performance control under uncertainties and faults, such as [17, 18], but they do not consider tire wear, which is critical in MS-AGV applications. Furthermore, practical concerns such as minimizing tire wear, which directly impacts maintenance costs, add complexity to the problem [19]. Despite significant advancements, no existing solution [20] comprehensively addresses these issues in an integrated manner, leaving a critical gap in MS-AGV planning and control strategies. Over the past several years, researchers have dedicated substantial effort to developing advanced control strategies to address the trajectory tracking problem in MS-AGV systems [21]. The core technical difficulty lies in managing the steering wheels, as the increased number of state variables [22] and the dynamic complexity [23] of the system make it challenging to predict and control [24] its behavior effectively.