Marco Lupia

Marine operations traditionally rely on human intervention, a costly and disruptive method. Autonomous surface vehicles (ASVs) offer a powerful alternative, capable of operating autonomously, for extended periods, and with various sensors for various missions. However, the use of a single ASV face limitations, such as lacking the flexibility and fault tolerance needed for complex tasks, particularly in applications requiring rapid exploration of large areas. Therefore, recent research has highlighted the growing interest in swarms of ASVs for rapid and robust exploration of large ocean workspaces. These offer advantages in terms of data measurement accuracy, precision, and consistency. However, conventional ASVs often face limitations due to their size and turning radius, particularly when operating in confined environments. This paper addresses these challenges by presenting the design, construction, linear physical modeling, identification, and control of a novel small, low‐cost, and modular ASV suitable for swarming applications. This omnidirectional vehicle offers a near‐zero turning radius, enabling efficient maneuvering within confined spaces. Additionally, its modular design facilitates the seamless integration of various sensors, allowing for adaptation to a wide range of missions. Under specific assumptions and through experimental validation, a linear time‐invariant mathematical model can effectively capture the vehicle's dynamics. This is in contrast with the usual practice of deriving physical‐based nonlinear vehicle models, that are overcomplicated, difficult to tune appropriately and unnecessary for the planned operative regimes of the vehicle and for control design purposes. The simple linear model here considered is particularly beneficial for reducing the computational burden associated with developing predictive control strategies.

In this paper, we present a prototype for the remote monitoring of underwater archaeological sites. The system aims to provide autonomous and real-time data collection from environmental probes and also includes biofouling detection based on image processing. The monitoring system is equipped with sensors to measure various parameters of interest, including temperature, pH, conductivity, dissolved oxygen, and oxidation-reduction potential. To detect biofouling, a camera is integrated into the system, and an analysis is performed on the captured images utilizing indices such as the Normalized Difference Vegetation Index (NDVI) and the PYM method. The system consists of a subsea node placed near the monitoring site, connected to a surface buoy through an umbilical cable. The surface buoy contains an LTE router for data transmission to a remote web platform for data storage and visualization. The hardware of the underwater node includes an embedded computer, responsible for providing functionalities for image processing and interfacing with the web platform, and a micro-controller board to read the sensor measurements. The developed remote monitoring system has been tested in a marine environment and offers a comprehensive solution for long-term environmental parameter analysis, biofouling detection, and real-time data access. It holds great potential for applications in marine research, environmental monitoring, and ecosystem studies.

This paper presents the design and implementation of BAICal (Intelligent Autonomous Buoy by the University of Calabria), an autonomous surface vehicle (ASV) developed at the Autonomous Systems Lab (LASA) of the Department of Computer Science, Modeling, Electronics, and Systems Engineering (DIMES), University of Calabria. The basic project was born as a research program in marine robotics with multiple applications, either in the sea or in lake/river environments, for data monitoring, search and rescue operations and diver support tasks. Mechanical and hardware designs are discussed by considering a three-degree-of-freedom (3DoF) dynamical model of the vehicle. An extension to the typical guidance, navigation, and control (GNC) software architecture is presented. The software design and the implementation of a manager module (M-GNC architecture) that allows the vehicle to autonomously coordinate missions are described. Indeed, autonomous guidance and movement are only one of several more complex tasks that mobile robots have to perform in a real scenario and that allow a long-term life cycle. Module-based software architecture is developed by using the Robot Operating System (ROS) framework that is suitable for different kinds of autonomous vehicles, such as aerial, ground, surface or underwater drones.

Over the last decade, the increased use of information and communication technology (ICT) in agriculture applications has led to the definition of the concept of precision farming or equivalently smart agriculture. In this respect, the latest progress in connectivity, automation, images analysis and artificial intelligence allow farmers to monitor all production phases and, due to the help of automatic procedures, determine better treatments for their farms. One of the main objectives of a smart agriculture system is to improve the yield of the field. From this point of view, the Internet of Things (IoT) paradigm plays a key role in precision farming applications due to the fact that the use of IoT sensors provides precise information about the health of the production. In this paper, the results of the recently concluded R&D project ENOTRIA TELLUS are reported. The project aimed at the development of all hardware/software components for implementing a precision farming architecture allowing the farmers to manage and monitor the vineyards’ health status. The smart architecture combines various sub-systems (web application, local controllers, unmanned aerial vehicles, multi-spectral cameras, weather sensors etc.) and electronic devices, each of them in charge of performing specific operations: remote data analysis, video processing for vegetation analysis, wireless data exchanges and weather and monitoring data evaluation. Two pilot sites were built where the smart architecture was tested and validated in real scenarios. Experimental activities show that the designed smart agriculture architecture allowed the farmers to properly schedule the various phases of cultivation and harvesting.

In underwater localization systems several sources of error may impact in different ways the accuracy of the final position estimates. Through simulations and statistical analysis it is possible to identify and characterize such sources of error and their relative importance. This is especially of use when an accurate localization system has to be designed within required accuracy prescriptions. This approach allows one to also investigate how much these sources of error influence the final position estimates achieved by an Extended Kalman Filter (EKF). This paper presents the results of experiments designed in a virtual environment used to simulate real acoustic underwater localization systems. The paper intends to analyze the main parameters that significantly influence the position estimates achieved by a Short Baseline (SBL) system. Specifically, the results of this analysis are presented for a proprietary localization system constituted by a surface platform equipped with four acoustic transducers used for the localization of an underwater target. The simulator here presented has the purpose of simulating the hardware system and modifying some of its design parameters, such as the base-line length and the errors on the GPS and Inertial Measurement Unit (IMU) units, in order to understand which parameters have to modify for improving the accuracy of the entire positioning system. It is shown that statistical analysis techniques can be of help in determining the best values of these parameters that permit to improve the performance of a real hardware system.

This paper reports the results of a recently concluded R&D project, SCALS (Smart Cities Adaptive Lighting System), which aimed at the development of all hardware/software components of an adaptive urban smart lighting architecture allowing municipalities to manage and control public street lighting lamps. The system is capable to autonomously adjust street lamps’ brightness on the basis of the presence of vehicles (busses/trucks, cars, motorcycles and bikes) and/or pedestrians in specific areas or segments of the streets/roads of interest to reduce the energy consumption. The main contribution of this work is to design a low cost smart lighting system and, at same time, to define an IoT infrastructure where each lighting pole is an element of a network that can increase their amplitude. More generally, the proposed smart infrastructure can be viewed as the basis of a wider technological architecture aimed at offering value-added services for sustainable cities. The smart architecture combines various sub-systems (local controllers, motion sensors, video-cameras, weather sensors) and electronic devices, each of them in charge of performing specific operations: remote street segments lamp management, single street lamp brightness control, video processing for vehicles motion detection and classification, wireless and wired data exchanges, power consumptions analysis and traffic evaluation. Two pilot sites have been built up in the project where the smart architecture has been tested and validated in real scenarios. Experimental results show that energy savings of up to 80% are possible compared to a traditional street lamp system.