Year: 2025
Type: article
Abstract: To address the requirements for automation and intelligence of agricultural robots, this paper develops a 14degree-of-freedom dynamic model for wheeled agricultural robots. The model aims to provide a dynamic modeling foundation under the framework of modern control theory for the automation and intelligence of wheeled agricultural robots. It incorporates the Ackermann steering mechanism, MacPherson independent suspension system, tire model, and deformable soil model based on Bekker's formula. The vertical tire pressure is calculated using the deformable soil model via Bekker's formula, while tire forces are predicted by combining the tire slip angle and slip ratio with the Magic Formula Tire Model. By analyzing the force transmission effect of the suspension system, integrating the center-of-mass coupling effect analysis and the robot body model equations, the precise prediction of the attitude and motion trajectory of the wheeled agricultural robot is achieved. A co-simulation experiment using MATLAB and CarSim under the double lane change (DLC) condition is designed for validation. Experimental results demonstrate that the proposed model exhibits high consistency with the CarSim simulation results. The mean absolute errors (MAE) are 0.327° for steering wheel angle, 0.677°/s for yaw rate, 0.691° for body roll angle, and 0.944 m/s² for lateral acceleration. All errors are less than 1.5, meeting the requirements of dynamic simulation. This model can effectively predict the body attitude of wheeled agricultural robots and lay a foundation for the subsequent development of optimal control algorithms for agricultural robots.
Source: INMATEH Agricultural Engineering
Authors: Mengmeng NI, Fanjun MENG, Fa SUN, Zhisheng ZHAO, Lili YI, Fanxia KONG
Institution: Shandong University of Technology
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: diamond
Funder: Texas Space Grant Consortium
OpenAlex ID: W7110417994