Evaluation of laser range-finder mapping for agricultural spraying vehicles

In this paper, we present a new application of laser range-finder sensing to agricultural spraying vehicles. The current generation of spraying vehicles use automatic controllers to maintain the height of the sprayer booms above the crop. However, these control systems are typically based on ultrasonic sensors mounted on the booms, which limits the accuracy of the measurements and the response of the controller to changes in the terrain, resulting in a sub-optimal spraying process. To overcome these limitations, we propose to use a laser scanner, attached to the front of the sprayer's cabin, to scan the ground surface in front of the vehicle and to build a scrolling 3d map of the terrain. We evaluate the proposed solution in a series of field tests, demonstrating that the approach provides a more detailed and accurate representation of the environment than the current sonar-based solution, and which can lead to the development of more efficient boom control systems

Motorcycles that see: Multifocal stereo vision sensor for advanced safety systems in tilting vehicles

Advanced driver assistance systems, ADAS, have shown the possibility to anticipate crash accidents and effectively assist road users in critical traffic situations. This is not the case for motorcyclists, in fact ADAS for motorcycles are still barely developed. Our aim was to study a camera-based sensor for the application of preventive safety in tilting vehicles. We identified two road conflict situations for which automotive remote sensors installed in a tilting vehicle are likely to fail in the identification of critical obstacles. Accordingly, we set two experiments conducted in real traffic conditions to test our stereo vision sensor. Our promising results support the application of this type of sensors for advanced motorcycle safety applications

Procedure for the Identification of Existing Roads Alignment from Georeferenced Points Database

The aim of this research is to look for an automated, economical and fast method able to identify the elements of an existing road layout, whose original geometric design could date back to distant ages and could have undergone major modifications over the years. The analysis has been directed towards the Italian two-lane rural roads; the national public company ANAS made available its graph, obtained from high-performance surveys, that represents about 90% of these roads’ network. The graph is made up of a collection of georeferenced points but does not recognize or describe the geometric elements making up the roadway. Consequently, it has been necessary to design and develop an original procedure, subsequently implemented in a programming platform, able to identify the characteristics of the several parts, which constitute the reference axes of the existing roads. This research focuses on the horizontal geometry assessing the coherence, consistency and homogeneity of the roads’ layout, through the ex post application of the regulatory model for the design verification. If road sections are identified in which some conditions are not significantly met, further investigation should be conducted in order to ensure road safety and to plan any road upgrading activities

Road geometry identification with mobile mapping techniques

Durante il mio dottorato mi sono occupato di Tecniche e Tecnologie innovative per la ricostruzione della geometria dei tracciati stradali esistenti, quali ad esempio Mobile Mapping, analisi immagini e dati GIS; a fronte degli elevatissimi costi oggi richiesti per l’utilizzo di veicoli strumentati già reperibili in commercio per il raggiungimento di tali scopi, il valore aggiunto del lavoro di dottorato riguarda l’uso di strumenti a basso costo che comportano un rilevante lavoro di analisi, trattamento e correzione del dato che risente in maniera decisiva della medio/bassa qualità della strumentazione in uso. L’obiettivo della ricerca è consistito nella realizzazione di un algoritmo di riconoscimento (in ambiente MATLAB) che sia in grado di restituire la geometria as-built di una strada esistente. Parte del lavoro è stata svolta nell’analisi e nell’estrazione delle curvature locali con approcci differenti (successive circonferenze locali, funzioni polinomiali di fitting locale di vario grado e con ampiezza di analisi variabile), nonché sullo studio degli angoli di deviazione locali. Usando questi parametri, nel resto del lavoro, si è prima ricercata una metodologia d’identificazione dei diversi elementi che compongono la geometria stradale, e poi si è lavorato su procedure di fitting con svariate tecniche (minimi quadrati, metodi robusti e altri algoritmi) cercando di estrarre informazioni di carattere geometrico, quali raggi di curvatura e relativi centri, lunghezza e orientamento dei rettifili, fattori di scala delle curve di transizione

Automated manufacturing of smart tunnel segments

Tunnels, essential infrastructures, require regular inspections and maintenance to ensure their prolonged service life. While conventional methods heavily rely on expert human manpower, modern tunnel structural monitoring techniques, such as sensor-based Structural Health Monitoring (SHM), are increasingly utilized in both existing and newly constructed tunnels. Despite providing valuable insights into post-construction structural behaviour, these methods often overlook the behaviour of individual precast elements, such as tunnel segments, before their installation. This thesis explores the concept of smart tunnel segments instrumented by robotic means to address this gap. In this project lab-scale tunnel segments were instrumented using a 6-axis robotic arm making them smart enabling their properties to be tracked from manufacturing through the operational phase of the tunnel. The research involves a comprehensive review of current tunnel instrumentation practices, identifying structural strains as the most monitored parameters. Vibrating Wire Strain Gauges (VWSGs) were identified as the most suitable sensors for this application due to their compatibility with a modular system and superior long-term properties, especially when embedded in concrete. Furthermore, the study identifies untapped potential in fully automated precast factories and proposes repurposing certain features of industrial robots to deploy VWSGs nodes via robotic pick-and-place. Through a novel evaluation framework, the research demonstrates the effectiveness of automated sensor deployment by robots. This includes the robotic installation of a pair of embedded VWSGs in lab-scale tunnel segments, thereby rendering them "smart," and subjecting them to repetitive flexural loadings to evaluate their performance and accuracy. The calculated strain transfer exhibits consistent and repeatable behaviour across segments. Finally, the thesis outlines the economic justification for smart segments, which outperform traditional on-site wired and wireless alternatives, thereby contributing to a more comprehensive and cost-effective tunnel maintenance strategyTunnels, essential infrastructures, require regular inspections and maintenance to ensure their prolonged service life. While conventional methods heavily rely on expert human manpower, modern tunnel structural monitoring techniques, such as sensor-based Structural Health Monitoring (SHM), are increasingly utilized in both existing and newly constructed tunnels. Despite providing valuable insights into post-construction structural behaviour, these methods often overlook the behaviour of individual precast elements, such as tunnel segments, before their installation. This thesis explores the concept of smart tunnel segments instrumented by robotic means to address this gap. In this project lab-scale tunnel segments were instrumented using a 6-axis robotic arm making them smart enabling their properties to be tracked from manufacturing through the operational phase of the tunnel. The research involves a comprehensive review of current tunnel instrumentation practices, identifying structural strains as the most monitored parameters. Vibrating Wire Strain Gauges (VWSGs) were identified as the most suitable sensors for this application due to their compatibility with a modular system and superior long-term properties, especially when embedded in concrete. Furthermore, the study identifies untapped potential in fully automated precast factories and proposes repurposing certain features of industrial robots to deploy VWSGs nodes via robotic pick-and-place. Through a novel evaluation framework, the research demonstrates the effectiveness of automated sensor deployment by robots. This includes the robotic installation of a pair of embedded VWSGs in lab-scale tunnel segments, thereby rendering them "smart," and subjecting them to repetitive flexural loadings to evaluate their performance and accuracy. The calculated strain transfer exhibits consistent and repeatable behaviour across segments. Finally, the thesis outlines the economic justification for smart segments, which outperform traditional on-site wired and wireless alternatives, thereby contributing to a more comprehensive and cost-effective tunnel maintenance strateg

Evaluating the accuracy of vehicle tracking data obtained from Unmanned Aerial Vehicles

Abstract This paper presents a methodology for tracking moving vehicles that integrates Unmanned Aerial Vehicles with video processing techniques. The authors investigated the usefulness of Unmanned Aerial Vehicles to capture reliable individual vehicle data by using GPS technology as a benchmark. A video processing algorithm for vehicles trajectory acquisition is introduced. The algorithm is based on OpenCV libraries. In order to assess the accuracy of the proposed video processing algorithm an instrumented vehicle was equipped with a high precision GPS. The video capture experiments were performed in two case studies. From the field, about 24,000 positioning data were acquired for the analysis. The results of these experiments highlight the versatility of the Unmanned Aerial Vehicles technology combined with video processing technique in monitoring real traffic data