Solid State Parametric Modeling and Trajectory Tracking Control of the MegBot-T800 AGV Robot

Abstract

To address the inefficiencies in developing Automated Guided Vehicles (AGVs) with similar chassis structures and the challenges of maintaining control accuracy under dynamic parameter changes, this study focuses on the MegBot-T800 AGV robot, conducting systematic research on its solid-state parametric modeling and trajectory tracking control. First, a kinematic model tailored to the MegBot-T800’s six-wheeled configuration (4 omnidirectional wheels + 2 rubber wheels) is established, and speed decomposition/synthesis methods for different wheel types are derived to address limitations of traditional modeling approaches. Second, three trajectory tracking control schemes are designed to mitigate issues of parameter uncertainty and external interference: an ASMCFR-based controller, a filter-integrated sliding mode controller, and an adaptive parameter-adjusted controller. Finally, MATLAB-based simulations of straight-line and circular trajectory tracking are performed. Results demonstrate that the proposed model accurately reflects the robot’s motion characteristics, while the control schemes effectively suppress chattering, enhance tracking accuracy, and ensure stable operation under complex conditions. This research provides a technical reference for parametric modeling and control design of AGVs with similar structures, reducing development and maintenance costs.