Toward Digital Twin of Off-Road Vehicles Using Robot Simulation Frameworks

Digital twins provide a powerful tool for testing and maintaining products and processes in several application fields, including manufacturing, smart cities, healthcare, and agriculture, aiming to optimize operational efficiency, resource usage, and planning accuracy. Research presented in this paper deals with the development of the digital version of off-road vehicles. Two different robot simulation frameworks are investigated. The first one is based on Gazebo, an open-source 3D robotics simulator, to test and validate the algorithms developed in the ROS framework; the second one adopts the vehicle mechanical assembly in MSC Adams, a multibody modeling software used to study the dynamics of complex mechanical systems. Both models are developed for a tracked robot that uses an innovative articulated passive suspension system on either side that allows each ground wheel to move independently with respect to the vehicle body, providing remarkable adaptability to irregular terrain. In addition, a Gazebo model is developed for a four-wheel drive/steering robot, including robot sensors such as GNSS, IMU, and visual sensors and a model of a typical agricultural environment (i.e., a vineyard). The paper presents the details of model design and implementation while investigating the best choice in developing the digital twin of off-road vehicles operating in the field. Additionally, an agricultural scenario has been selected as a use case to facilitate the evaluation of the analyzed frameworks. Our findings demonstrate that the Gazebo framework could serve as a suitable robot simulation framework for creating digital twins of vehicles, provided it incorporates real-time sensor measurements designed for identifying soil-wheel interaction dynamics. In contrast, the multibody model provides a higher-fidelity dynamic model, including track-terrain interaction. Despite its advantages, this model has substantially higher computational costs, which limits its applicability to real-time simulations, making it less feasible for practical use in the field.