Rocco Galati

1. Tracked mobile robots are increasingly employed in missions that require reliable locomotion over rough and unstructured terrain, such as search and rescue, infrastructure inspection, planetary ...

Accurate torque prediction for excavating drums is critical for the design and autonomous control of planetary rovers operating in loose, unstructured regolith. Existing terramechanics models may fail to capture the full dynamics of rotating excavation drums. In this paper, we present a unified, physics‐based torque prediction model with two novel dynamic effects: a longitudinal, velocity‐sensitive bulldozing resistance term that grows logarithmically with forward speed, and a regolith‐accumulation torque that evolves as the drum buckets fill. We validate our approach on a custom four‐wheel holonomic rover excavating Silica Sand No. 5 across multiple test sequences spanning different angular and translational velocities. The model achieves a normalized RMSE generally below 12%, an MAE under 0.15 Nm, and , in nearly all cases, accurately reproducing the temporal evolution toward steady‐state torque plateaus. Its modular structure and reliance on minimal empirical tuning make it suitable for real‐time torque estimation in future autonomous planetary missions.

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.

Robotic and multi-sensor technologies are increasingly being adopted in a number of agricultural applications, including seeding, weeding, harvesting, fertilization, and crop monitoring and analysis. However, the lack of interoperability and the predominance of manufacturer-specific closed solutions demand a careful choice of devices, sensors and data processing platforms and hinder the flexible adaption of these systems to the individual farmer’s needs and knowledge exchange. The Horizon 2020 Agriculture Interoperability and Analysis System (ATLAS) project is aimed at overcoming these issues through an open, flexible and distributed interoperability network, which enables the seamless interconnection of sensor systems, machines and data analysis tools. This paper presents latest achievements in the context of the ATLAS project, concerning the development of robotic services for in-field crop monitoring and their integration in the ATLAS network.