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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 the latest achievements in the context of the ATLAS project, concerning the development of robotic services for in-field crop monitoring and their integration into the ATLAS network.

Agricultural soils provide a variety of ecological services, including nutrient cycling, water purification and storage, carbon sequestration, and flood protection. Soil Surface Roughness (SSR) represents a key parameter for evaluating the terrain quality structure, especially in the layers beneath the usual primary tillage depth, and therefore its estimation has been widely investigated over the years in many fields of science and engineering. This paper proposes the adoption of an innovative sensing approach that relies on contactless measurements provided by a depth camera in contrast to traditional contact measurements such as pin meter and roller chain. In addition, novel features are investigated to achieve a complete statistical description of the SSR with the least number of parameters. The proposed methods are validated in an experimental campaign performed on a vineyard plot. This research could be useful for many applications, including soil erosion prediction models, autonomous vehicle navigation in rural and agricultural settings, and controlled traffic farming.

Efficient management of on-shelf availability and inventory is a key issue to achieve customer satisfaction and reduce the risk of profit loss for both retailers and manufacturers. Conventional store audits based on physical inspection of shelves are labor-intensive and do not provide reliable assessment. This paper describes a novel framework for automated shelf monitoring, using a consumer-grade depth sensor. The aim is to develop a low-cost embedded system for early detection of out-of-stock situations with particular regard to perishable goods stored in countertop shelves, refrigerated counters, baskets or crates. The proposed solution exploits 3D point cloud reconstruction and modelling techniques, including surface fitting and occupancy grids, to estimate product availability, based on the comparison between a reference model of the shelf and its current status. No a priori knowledge about the product type is required, while the shelf reference model is automatically learnt based on an initial training stage. The output of the system can be used to generate alerts for store managers, as well as to continuously update product availability estimates for automated stock ordering and replenishment and for e-commerce apps. Experimental tests performed in a real retail environment show that the proposed system is able to estimate the on-shelf availability percentage of different fresh products with a maximum average discrepancy with respect to the actual one of about 5.0%.

Ground vehicles equipped with vision-based perception systems can provide a rich source of information for precision agriculture tasks in orchards, including fruit detection and counting, phenotyping, plant growth and health monitoring. This paper presents a semi-supervised deep learning framework for automatic pomegranate detection using a farmer robot equipped with a consumer-grade camera. In contrast to standard deep-learning methods that require time-consuming and labor-intensive image labeling, the proposed system relies on a novel multi-stage transfer learning approach, whereby a pre-trained network is fine-tuned for the target task using images of fruits in controlled conditions, and then it is progressively extended to more complex scenarios towards accurate and efficient segmentation of field images. Results of experimental tests, performed in a commercial pomegranate orchard in southern Italy, are presented using the DeepLabv3+ (Resnet18) architecture, and they are compared with those that were obtained based on conventional manual image annotation. The proposed framework allows for accurate segmentation results, achieving an F1-score of 86.42% and IoU of 97.94%, while relieving the burden of manual labeling.

Over the last decade, there has been considerable and increasing interest in the development of Active and Assisted Living (AAL) systems to support independent living. The demographic change towards an aging population has introduced new challenges to today’s society from both an economic and societal standpoint. AAL can provide an arrary of solutions for improving the quality of life of individuals, for allowing people to live healthier and independently for longer, for helping people with disabilities, and for supporting caregivers and medical staff. A vast amount of literature exists on this topic, so this paper aims to provide a survey of the research and skills related to AAL systems. A comprehensive analysis is presented that addresses the main trends towards the development of AAL systems both from technological and methodological points of view and highlights the main issues that are worthy of further investigation.

With the advent of the Fourth Industrial Revolution, Internet of Things (IoT) and robotic systems are closely cooperating, reshaping their relations and managing to develop new-generation devices. Such disruptive technology corresponds to the backbone of the so-called Industry 4.0. The integration of robotic agents and IoT leads to the concept of the Internet of Robotic Things, in which innovation in digital systems is drawing new possibilities in both industrial and research fields, covering several domains such as manufacturing, agriculture, health, surveillance, and education, to name but a few. In this manuscript, the state-of-the-art of IoRT applications is outlined, aiming to mark their impact on several research fields, and focusing on the main open challenges of the integration of robotic technologies into smart spaces. IoRT technologies and applications are also discussed to underline their influence in everyday life, inducing the need for more research into remote and automated applications.

Autonomous driving is a challenging problem, particularly when the domain is unstructured, as in an outdoor agricultural setting. Thus, advanced perception systems are primarily required to sense and understand the surrounding environment recognizing artificial and natural structures, topology, vegetation and paths. In this paper, a self-learning framework is proposed to automatically train a ground classifier for scene interpretation and autonomous navigation based on multi-baseline stereovision. The use of rich 3D data is emphasized where the sensor output includes range and color information of the surrounding environment. Two distinct classifiers are presented, one based on geometric data that can detect the broad class of ground and one based on color data that can further segment ground into subclasses. The geometry-based classifier features two main stages: an adaptive training stage and a classification stage. During the training stage, the system automatically learns to associate geometric appearance of 3D stereo-generated data with class labels. Then, it makes predictions based on past observations. It serves as well to provide training labels to the color-based classifier. Once trained, the color-based classifier is able to recognize similar terrain classes in stereo imagery. The system is continuously updated online using the latest stereo readings, thus making it feasible for long range and long duration navigation, over changing environments. Experimental results, obtained with a tractor test platform operating in a rural environment, are presented to validate this approach, showing an average classification precision and recall of 91.0% and 77.3%, respectively.

In this research, adaptive perception for driving automation is discussed so as to enable a vehicle to automatically detect driveable areas and obstacles in the scene. It is especially designed for outdoor contexts where conventional perception systems that rely on a priori knowledge of the terrain's geometric properties, appearance properties, or both, is prone to fail, due to the variability in the terrain properties and environmental conditions. In contrast, the proposed framework uses a self-learning approach to build a model of the ground class that is continuously adjusted online to reflect the latest ground appearance. The system also features high flexibility, as it can work using a single sensor modality or a multi-sensor combination. In the context of this research, different embodiments have been demonstrated using range data coming from either a radar or a stereo camera, and adopting self-supervised strategies where monocular vision is automatically trained by radar or stereo vision. A comprehensive set of experimental results, obtained with different ground vehicles operating in the field, are presented to validate and assess the performance of the system.

The development of intelligent surveillance systems is an active research area. In this context, mobile and multi-functional robots are generally adopted as means to reduce the environment structuring and the number of devices needed to cover a given area. Nevertheless, the number of different sensors mounted on the robot, and the number of complex tasks related to exploration, monitoring, and surveillance make the design of the overall system extremely challenging. In this paper, we present our autonomous mobile robot for surveillance of indoor environments. We propose a system able to handle autonomously general-purpose tasks and complex surveillance issues simultaneously. It is shown that the proposed robotic surveillance scheme successfully addresses a number of basic problems related to environment mapping, localization and autonomous navigation, as well as surveillance tasks, like scene processing to detect abandoned or removed objects and people detection and following. The feasibility of the approach is demonstrated through experimental tests using a multisensor platform equipped with a monocular camera, a laser scanner, and an RFID device. Real world applications of the proposed system include surveillance of wide areas (e.g. airports and museums) and buildings, and monitoring of safety equipment.