Modeling and Identification of an Open-frame Underwater Vehicle: The Yaw Motion Dynamics

A semi-autonomous unmanned underwater vehicle (UUV), named LAURS, is being developed at the Laboratory of Sensors and Actuators at the University of Sao Paulo. The vehicle has been designed to provide inspection and intervention capabilities in specific missions of deep water oil fields. In this work, a method of modeling and identification of yaw motion dynamic system model of an open-frame underwater vehicle is presented. Using an on-board low cost magnetic compass sensor the method is based on the utilization of an uncoupled 1-DOF (degree of freedom) dynamic system equation and the application of the integral method which is the classical least squares algorithm applied to the integral form of the dynamic system equations. Experimental trials with the actual vehicle have been performed in a test tank and diving pool. During these experiments, thrusters responsible for yaw motion are driven by sinusoidal voltage signal profiles. An assessment of the feasibility of the method reveals that estimated dynamic system models are more reliable when considering slow and small sinusoidal voltage signal profiles, i.e. with larger periods and with relatively small amplitude and offset.

Autonomous Vehicle and Internet on Vehicles

In questa tesi mi occupo di spiegare come si comportano i veicoli autonomi per prendere tutte le decisioni e come i dati dei sensori di ogni auto vengono condivisi con la flotta di veicoli

Rapid prototyping framework for visual control of autonomous micro aerial vehicles

Rapid prototyping environments can speed up the research of visual control algorithms. We have designed and implemented a software framework for fast prototyping of visual control algorithms for Micro Aerial Vehicles (MAV). We have applied a combination of a proxy-based network communication architecture and a custom Application Programming Interface. This allows multiple experimental configurations, like drone swarms or distributed processing of a drone’s video stream. Currently, the framework supports a low-cost MAV: the Parrot AR.Drone. Real tests have been performed on this platform and the results show comparatively low figures of the extra communication delay introduced by the framework, while adding new functionalities and flexibility to the selected drone. This implementation is open-source and can be downloaded from www.vision4uav.com/?q=VC4MAV-FW

Online Learning-based Robust Visual Tracking for Autonomous Landing of Unmanned Aerial Vehicles

Autonomous landing is a challenging and important technology for both military and civilian applications of Unmanned Aerial Vehicles (UAVs). In this paper, we present a novel online adaptive visual tracking algorithm for UAVs to land on an arbitrary field (that can be used as the helipad) autonomously at real-time frame rates of more than twenty frames per second. The integration of low-dimensional subspace representation method, online incremental learning approach and hierarchical tracking strategy allows the autolanding task to overcome the problems generated by the challenging situations such as significant appearance change, variant surrounding illumination, partial helipad occlusion, rapid pose variation, onboard mechanical vibration (no video stabilization), low computational capacity and delayed information communication between UAV and Ground Control Station (GCS). The tracking performance of this presented algorithm is evaluated with aerial images from real autolanding flights using manually- labelled ground truth database. The evaluation results show that this new algorithm is highly robust to track the helipad and accurate enough for closing the vision-based control loop.

Online Learning-based Robust Visual Tracking for Autonomous Landing of Unmanned Aerial Vehicles

Autonomous landing is a challenging and important technology for both military and civilian applications of Unmanned Aerial Vehicles (UAVs). In this paper, we present a novel online adaptive visual tracking algorithm for UAVs to land on an arbitrary field (that can be used as the helipad) autonomously at real-time frame rates of more than twenty frames per second. The integration of low-dimensional subspace representation method, online incremental learning approach and hierarchical tracking strategy allows the autolanding task to overcome the problems generated by the challenging situations such as significant appearance change, variant surrounding illumination, partial helipad occlusion, rapid pose variation, onboard mechanical vibration (no video stabilization), low computational capacity and delayed information communication between UAV and Ground Control Station (GCS). The tracking performance of this presented algorithm is evaluated with aerial images from real autolanding flights using manually- labelled ground truth database. The evaluation results show that this new algorithm is highly robust to track the helipad and accurate enough for closing the vision-based control loop.

Autonomous Vehicles and Commercial Real Estate

Hardly a day goes by without the release of a handful of news stories about autonomous vehicles (or AVs for short). The proverbial “tipping point” of awareness has been reached in the public consciousness as AV technology is quickly becoming the new focus of firms from Silicon Valley to Detroit and beyond. Automation has, and will continue to have far-reaching implications for many human activities, but for driving, the technology is here. Google has been in talks with automaker Ford (1), Elon Musk has declared that Tesla will have the appropriate technology in two years (2), GM is paired-up with Lyft (3), Uber is in development-mode (4), Microsoft and Volvo have announced a partnership (5), Apple has been piloting its top-secret project “Titan” (6), Toyota is working on its own technology (7), as is BMW (8). Audi (9) made a splash by sending a driverless A7 concept car 550 miles from San Francisco to Las Vegas just in time to roll-into the 2016 Consumer Electronics Show. Clearly, the race is on.

Motion Control of an Advanced Electric Implement for Agricultural Autonomous Vehicles

Nowadays, the development of intelligent and autonomous vehicles used to perform agricultural activities is essential to improve quantity and quality of agricultural productions. Moreover, with automation techniques it is possible to reduce the usage of agrochemicals and minimize the pollution. The University of Bologna is developing an innovative system for orchard management called ORTO (Orchard Rapid Transportation System). This system involves an autonomous electric vehicle capable to perform agricultural activities inside an orchard structure. The vehicle is equipped with an implement capable to perform different tasks. The purpose of this thesis project is to control the vehicle and the implement to perform an inter-row grass mowing. This kind of task requires a synchronized motion between the traction motors and the implement motors. A motion control system has been developed to generate trajectories and manage their synchronization. Two main trajectories type have been used: a five order polynomial trajectory and a trapezoidal trajectory. These two kinds of trajectories have been chosen in order to perform a uniform grass mowing, paying a particular attention to the constrains of the system. To synchronize the motions, the electronic cams approach has been adopted. A master profile has been generated and all the trajectories have been linked to the master motion. Moreover, a safety system has been developed. The aim of this system is firstly to improve the safety during the motion, furthermore it allows to manage obstacle detection and avoidance. Using some particular techniques obstacles can be detected and recovery action can be performed to overcome the problem. Once the measured force reaches the predefined force threshold, then the vehicle stops immediately its motion. The whole project has been developed by employing Matlab and Simulink. Eventually, the software has been translated into C code and executed on the TI Lauchpad XL board.

Crowd-shipping and autonomous vehicles: an optimization model for the last mile delivery

Il seguente elaborato propone un modello innovativo per la gestione della logistica distributiva nell’ultimo miglio, congiungendo l’attività di crowd-shipping con la presenza di Autonomous Vehicles, per il trasporto di prodotti all’interno della città. Il crowd-shipping utilizza conducenti occasionali, i quali deviano il loro tragitto in cambio di una ricompensa per il completamento dell’attività. Dall’altro lato, gli Autonomous Vehicles sono veicoli elettrici a guida autonoma, in grado di trasportare un numero limitato di pacchi e dotati di un sistema di sicurezza avanzato per garantire la fiducia nel trasporto. In primo luogo, nel seguente elaborato verrà mostrato il modello di ottimizzazione che congiunge i due attori principali in un unico ambiente, dove sono presenti un numero determinato di prodotti da muovere. Successivamente, poiché il problema di ottimizzazione è molto complesso e il numero di istanze valutabili è molto basso, verranno presentate due soluzioni differenti. La prima riguarda la metaeuristica chiamata Ant System, che cerca di avvicinarsi alle soluzioni ottime del precedente modello, mentre la seconda riguarda l’utilizzo di operatori di Local Search, i quali permettono di valutare soluzioni per istanze molto più grandi rispetto alla metaeuristica. Infine, i due modelli euristici verranno utilizzati per analizzare uno scenario che cerca di riprodurre una situazione reale. Tale scenario tenta di allocare strategicamente le risorse presenti e permette di dimostrare che gli Autonomous Vehicles riescono a supportare gli Occasional Drivers anche quando il numero di prodotti trasportabili è elevato. Inoltre, le due entità proposte riescono a soddisfare la domanda, garantendo un servizio che nel futuro potrebbe sostituire il tradizionale sistema di logistica distributiva last mile.

Smart freight transport in urban areas: a crowd-shipping model using autonomous vehicles

L'avanzamento dell'e-commerce e l'aumento della densità abitativa nel centro città sono elementi che incentivano l'incremento della richiesta merci all'interno dei centri urbani. L'attenzione all'impatto ambientale derivante da queste attività operative è un punto focale oggetto di sempre maggiore interesse. Attraverso il seguente studio, l'obiettivo è definire attuali e potenziali soluzioni nell'ambito della logistica urbana, con particolare interesse alle consegne dell'ultimo miglio. Una soluzione proposta riguarda la possibilità di sfruttare la capacità disponibile nei flussi generati dalla folla per movimentare merce, pratica nota sotto il nome di Crowd-shipping. L'idea consiste nella saturazione di mezzi già presenti nella rete urbana al fine di ridurre il numero di veicoli commerciali e minimizzare le esternalità negative annesse. A supporto di questa iniziativa, nell'analisi verranno considerati veicoli autonomi elettrici a guida autonoma. La tesi è incentrata sulla definizione di un modello di ottimizzazione matematica, che mira a designare un network logistico-distributivo efficiente per le consegne dell'ultimo miglio e a minimizzare le distanze degli attori coinvolti. Il problema proposto rappresenta una variante del Vehicle Routing Problem con time windows e multi depots. Il problema è NP-hard, quindi computazionalmente complesso per cui sarà necessario, in fase di analisi, definire un approccio euristico che permetterà di ottenere una soluzione sub-ottima in un tempo di calcolo ragionevole per istanze maggiori. L'analisi è stata sviluppata nell'ambiente di sviluppo Eclipse, attraverso il risolutore Cplex, in linguaggio Java. Per poterne comprendere la validità, è prevista un'ultima fase in cui gli output del modello ottimo e dell'euristica vengono confrontati tra loro su parametri caratteristici. Bisogna tuttavia considerare che l' utilizzo di sistemi cyber-fisici a supporto della logistica non può prescindere da un costante sguardo verso il progresso.

The operation of autonomous mobile robot assistants in the environment of care facilities adopting a user-centered development design

The participation of the Fraunhofer Institute for Manufacturing Engineering and Automation IPA (Stuttgart, Germany) and the companies User Interface Design GmbH (Ludwigsburg, Germany) plus MLR System GmbH (Ludwigsburg, Germany) enabled the research and findings presented in this paper; we would like to namely mention Birgit Graf and Theo Jacobs (Fraunhofer IPA) furthermore Peter Klein and Christiane Hartmann (User Interface Design GmbH).

Towards Active Course Marks for Autonomous Sailing Competitions

This paper describes the environmental monitoring / regatta beacon buoy under development at the Laboratory of Autonomous Systems (LSA) of the Polytechnic Institute of Porto. On the one hand, environmentalmonitoring of open water bodies in real or deferred time is essential to assess and make sensible decisions and, on the other hand, the broadcast in real time of position, water and wind related parameters allows autonomous boats to optimise their regatta performance. This proposal, rather than restraining the boats autonomy, fosters the development of intelligent behaviour by allowing the boats to focus on regatta strategy and tactics. The Nautical and Telemetric Application (NAUTA) buoy is a dual mode reconfigurable system that includes communications, control, data logging, sensing, storage and power subsystems. In environmental monitoring mode, the buoy gathers and stores data from several underwater and above water sensors and, in regatta mode, the buoy becomes an active course mark for the autonomous sailing boats in the vicinity. During a race, the buoy broadcasts its position, together with the wind and the water current local conditions, allowing autonomous boats to navigate towards and round the mark successfully. This project started with the specification of the requirements of the dual mode operation, followed by the design and building of the buoy structure. The research is currently focussed on the development of the modular, reconfigurable, open source-based control system. The NAUTA buoy is innovative, extensible and optimises the on board platform resources.

Sistema de mapeamento e localização simultânea para inspecção subaquática

A sequente dissertação resulta do desenvolvimento de um sistema de navegação subaquático para um Remotely Operated Vehicle (ROV). A abordagem proposta consiste de um algoritmo em tempo real baseado no método de Mapeamento e Localização Simultâneo (SLAM) a partir de marcadores em ambientes marinhos não estruturados. SLAM introduz dois principais desafios: (i) reconhecimento dos marcadores provenientes dos dados raw do sensor, (ii) associação de dados. Na detecção dos marcadores foram aplicadas técnicas de visão artificial baseadas na extracção de pontos e linhas. Para testar o uso de features no visual SLAM em tempo real nas operações de inspecção subaquáticas foi desenvolvida uma plataforma modicada do RT-SLAM que integra a abordagem EKF SLAM. A plataforma é integrada em ROS framework e permite estimar a trajetória 3D em tempo real do ROV VideoRay Pro 3E até 30 fps. O sistema de navegação subaquático foi caracterizado num tanque instalado no Laboratório de Sistemas Autónomos através de um sistema stereo visual de ground truth. Os resultados obtidos permitem validar o sistema de navegação proposto para veículos subaquáticos. A trajetória adquirida pelo VideoRay em ambiente controlado é validada pelo sistema de ground truth. Dados para ambientes não estruturados, como um gasoduto, foram adquiridos e obtida respectiva trajetória realizada pelo robô. Os dados apresentados comprovam uma boa precisão e exatidão para a estimativa da posição.

Study on the development of an autonomous mobile robot

Dissertação para obtenção do Grau de Mestre em Engenharia Eletrotécnica e de Computadores

Vision-based localization of an underwater robot in a structured environment

This paper presents a vision-based localization approach for an underwater robot in a structured environment. The system is based on a coded pattern placed on the bottom of a water tank and an onboard down looking camera. Main features are, absolute and map-based localization, landmark detection and tracking, and real-time computation (12.5 Hz). The proposed system provides three-dimensional position and orientation of the vehicle along with its velocity. Accuracy of the drift-free estimates is very high, allowing them to be used as feedback measures of a velocity-based low-level controller. The paper details the localization algorithm, by showing some graphical results, and the accuracy of the system

An approach to vision-based station keeping for an unmanned underwater vehicle

This paper presents an automatic vision-based system for UUV station keeping. The vehicle is equipped with a down-looking camera, which provides images of the sea-floor. The station keeping system is based on a feature-based motion detection algorithm, which exploits standard correlation and explicit textural analysis to solve the correspondence problem. A visual map of the area surveyed by the vehicle is constructed to increase the flexibility of the system, allowing the vehicle to position itself when it has lost the reference image. The testing platform is the URIS underwater vehicle. Experimental results demonstrating the behavior of the system on a real environment are presented