Master's Thesis Completed Projects

Indoor SLAM and Room Recognition with Deep Learning at the edge

Candidate:  Andrea Eirale
Supervisor: Prof. Marcello Chiaberge
Date:  December 2020

In recent years, the development of technology has led to the emergence of increasingly complex and accurate localization and mapping algorithms. In the field of robotics, this has allowed the progressive integration alongside other programs with the most varied functions, from home care to space exploration, designed to provide a service, to support and improve people’s living conditions.
With this goal in mind, the PIC4SeR (PoliTo interdepartmental centre for service robotics) has developed the idea of integrating a SLAM algorithm, for simultaneous self localization and mapping, with a convolutional neural network for room recognition.

The main goal of this thesis project is the development of an algorithm able to lead an unmanned ground vehicle in an unknown, closed domestic environment, mapping it and classifying each room encountered in the process. Low cost sensors and free, open-source software are used to achieve the final result.

For localization and mapping, several techniques are considered, from the classic extended Kalman filter to the more advanced graph-based SLAM. The most adapted ones are further developed, to retrieve a first, raw representation of the environment.\\
The map is then processed with computer vision software in order to obtain a cleaner and clearer plot of the surroundings, and to setting it up for the recognition algorithm.

Finally, a convolutional neural network model is used, alongside to a series of frame images taken by the robot from the environment, to classify each room and provide predictions on the map.

The final algorithm is relatively efficient and lightweight, and opens up to a series of future implementations in the field of service robotics, in domestic environments and in the assistance to elderly and disabled users.

Adaptation of a path planning algorithm for UGV in precision agriculture

Candidate:  Francesco Messina
Supervisor: Prof. Marcello Chiaberge
Date:  December 2020

A great interest in self-driven vehicles has developed in the field of robotics and more generally in the industrial sector in the last few decades, and the number of applications where these vehicles are used is growing strongly. A clear example of this phenomenon are the efforts made by several automotive in terms of research and development, to manufacture increasingly competitive, safe and precise Autopilots. This is also due to cutting-edge on-board computers, the development of highly precise and performing sensors, and the advent of the 5G network, which is also aimed to ensure an unprecedented V2X (Vehicle-to-everything) communication, especially thanks to a new architecture called “network slicing”.
The robotics sector is now focused on providing solutions that may promote and improve the man-machine collaboration in agriculture as well as in other areas closely related to the civil sphere, therefore not only for military uses and space exploration, as it used to be. This process has seen an acceleration this year for the emergence of Covid-19, which has become a global pandemic. This situation has highlighted, now more than ever, the need to rely on autonomous instruments that, for example, transport medical equipment or essential commodities, without running the risk to get in contact with other people or the need for anyone to leave home.
In this context, the aim of this thesis work is the creation of a UGV (Unmanned Ground Vehicle) that can run independently, safely and efficiently through rough and uneven terrain, as for agricultural fields or unpaved roads, by using the RRT* (Rapidly-exploring Random Tree) path planner. The latter manages to accomplish its task also thanks to the collaboration with a UAV (Unmanned Aerial Vehicle)
that, by means of the aerial photogrammetric survey method, can generate highprecision georeferenced orthophotos and DTM (Digital Terrain Model), that are later processed by a GIS (Geographic Information System) software to generate the static binary mask.
In detail, the project is designed to cover different project phases. Starting from the design phase, when the criticalities of the problem are evaluated and possible solutions are identified, to the programming phase, in which a code has been developed that allows the planning of the route adapting to the problems that may arise in this type of terrain, up to the simulation and real test phase in the field.

Visual based local motion planner with Deep Reinforcement Learning

Candidate:  Mauro Martini
Supervisor: Prof. Marcello Chiaberge
Date:  September 2020

This thesis aims to develop an autonomous navigation system for indoor scenarios based on Deep Reinforcement Learning (DRL) technique. Autonomous navigation is a hot challenging task in the research area of robotics and control systems, which has been tackled with numerous contributions and different approaches. Among them, learning methods have been investigated in recent years due to the successful spreading of Deep Learning (DL). In particular, in Reinforcement Learning an agent learns by experience, i.e. through the interaction with the environment where it is placed, avoiding the need of a huge dataset for the training process. Service robotics is the main focus of the research at PIC4SeR (PoliTo Interdepartmental Centre for Service Robotics), where the idea of this thesis project is born as part of a broader project. Under the supervision of Professor M. Chiaberge (PoliTo), member of the group, the thesis embraces the vision of the centre, which is to develop high-tech solutions for peculiar fields such as precision agriculture, surveillance and security, in addition to assist people in their every-day life. As a matter of fact, an autonomous navigation system enables competitive advantages in a wide variety of the applications of interest. Deep Deterministic Policy Gradient (DDPG) is the specific DRL algorithm applied to train an agent in a simulated environment using ROS (Robot Operating System). Training simulations offer different types of scenarios presenting both static and moving obstacles. The main goal of the project is to provide a safe collision-free navigation in an unknown indoor environment. An Artificial Neural Network (ANN) is used to directly select suitable actions for the robot, expressed in terms of linear and angular velocity (ANN output). Input information is composed of robot pose and goal position, in addition to raw images provided by a depth camera. A great focus is also devoted to reduce the computational cost of the model in the training phase, as well as the energy consumption in a potential hardware implementation. For this reason, an efficient architecture of the CNN is studied, paying attention to both desired performances and costs. Firstly, a set of convolutional layers is needed to extract high-level features from depth images. Then fully-connected layers predict the action for the robot. Beside these aspects, also sensor data play a key role in a navigation system. From a research point of view, it is interesting to evaluate the performance of the algorithm when using depth images, compared to other popular implementations based on LiDAR sensor. On the one hand, a camera offers a rich depth information. On the other hand a simple 2D LiDAR is able to cover a wider field of view. The navigation system has been tested in a virtual environment with obstacles. Despite the difficulty of the challenge and the amount of resources required for the development, the system can be considered a good starting point for future works. The implementation of the algorithm on a real robot will be a natural next step for the project.

Deep Learning Methodologies for UWB Ranging Error Compensation

Candidate:  Simone Angarano
Supervisor: Prof. Marcello Chiaberge
Date:  September 2020

Ultra-Wideband (UWB) is being extensively introduced in various kinds of both human and robot positioning systems. From industrial robotic tasks to drones used for search and rescue operations, this high-accuracy technology allows locating a target with an error of just a few centimeters, outperforming other existing low-cost ranging methods like Bluetooth and Wi-Fi. This led Apple to equip the latest iPhone 11 with an UWB module specifically for precise localization applications. Unfortunately, this technology is proved to be very accurate only in Line-Of-Sight (LOS). Indeed, performances degrade significantly in Non-Line-Of-Sight (NLOS) scenarios, where walls, furniture or people obstruct the direct path between the antennas. Moreover, reflections constitute an additional source of error, causing the receiver to detect multiple signals with different delays. The aim of this thesis is to compensate NLOS and multi-path errors and to obtain a precise and reliable positioning system, allowing the development of several service robotics applications that are now limited by unsatisfactory accuracies. Another fundamental goal is to guarantee good scalability of the system to unseen scenarios, where even modern mitigation methods still fail. For this scope, a large dataset is built, taking both LOS and NLOS measurements in different environments and experimenting with different types of obstacles. Then, modern Deep Learning methods are used to design a Convolutional Neural Network that predicts the error of the range estimates directly from raw Channel Impulse Response (CIR) samples. Finally, a positioning test is conducted to verify the effectiveness of the method in a real scenario.

GPS-based autonomous navigation of unmanned ground vehicles in precision agriculture applications

Candidate:  Simone Cerrato
Supervisor: Prof. Marcello Chiaberge
Date:  September 2020

The global population is growing exponentially and the actual agricultural techniques and resources will not be able to feed every person on the Earth in a few years. To account for this serious problem, groups of research are focusing their attention on precision agriculture, because it looks for the improvement of the productivity and efficiency of both agricultural and farming production processes, while reducing the environmental impact, exploiting automation and robotics. The thesis aims to design and develop a solution, based on GPS, for the autonomous navigation problem in precision agriculture, using only few sensors: an Inertial Measurement Unit, a GPS receiver and a depth camera, in order to be cost effective. The proposed goal has been achieved through a system of inter-operating sub-components, that have to share information and collaborate each other in order to provide a complete autonomous navigation. In particular, the main involved entities are: a localization filter, a global and a local path planning algorithms and an obstacle avoidance approach, that have been developed and can cooperate each other by means of the Robot Operating System. Eventually, the proposed solution has been tested in a simulation environment, through different possible scenarios providing good results in each of them. However, it may be considered as a starting point for future improvement in the field of autonomous navigation for precision agriculture.

Deep Reinforcement Learning and Ultra-Wideband for autonomous navigation in service robotic applications

Candidate:  Enrico Sutera
Supervisor: Prof. Marcello Chiaberge
Date: December 2019

Autonomous navigation for service robotics is one the greatest challenges and there’s a huge effort from scientific community. This work is born at PIC4SeR (PoliTo Interdepartmental Centre for Service Robotics) with the idea of facing the aforementioned challenge merging rediscovered and promising technologies and techniques: Deep Reinforcement Learning and Ultra-Wideband technology. Over few past years the world has seen a huge advance in the field of Artificial Intelligence, especially thanks to Machine Learning techniques. The latter include a branch called Deep Reinforcement Learning (DRL) that involves the training of Artificial Neural Network (ANN) from experience, i.e. without the need of huge datasets. Here DRL has been used to train an agent able to perform goal reaching and obstacle avoidance. Ultra-wideband (UWB) is an emerging technology that can be used for short-range data transmission and localization. It can be used in GPS-denied environments, such as indoor ones. In this work UWB has been used for localization purposes. UWB is supposed to be a key technology in future: many giant companies are involved and Apple has already inserted an UWB chip in its latest product. It has been used a differential drive robot as implementation platform. The robot is controlled by an ANN (which has robot pose information, lidar information and goal information as input and linear and angular speeds as outputs) using ROS (Robot Operating System). The ANN is trained using a DRL algorithm called Deep Deterministic Policy Gradient (DDPG) in a simulated environment. The UWB has been used in testing phase only. The overall system has been tested in a real environment and compared with human performances, showing that it is able – in some tasks – to match or even outdo them. There have been satisfying results and it is believed that, although there are strong limitations given by the difficulty of the challenge, the system complies with expectations and constitutes a good baseline for future work.

Optimization of an Ultralight Autonomous Drone for Service Robotics

Candidate:  Simone Silvestro
Supervisor: Prof. Marcello Chiaberge
Date: December 2019

Drone industry is constantly growing and evolving in time. Federal Aviation Administration Aerospace Forecast predicts that drone market volume will be about three times the actual one in 2023. This because UAVs are becoming more and more fundamental in the most various application like agriculture, emergency response, urban planning and maintenance, entertainment, security and so many others. For all these tasks, in the near future a large number of drones will fly over our heads. From here the necessity to build safe, lightweight and autonomous UAVfor the drones colonization of the very lower part of the Earth atmosphere. Furthermore European Aviation Safety Agency fixed to 250 grams the limit of the C0 open UAS, category with quite few restrictions to fly. PIC4SeR (PoliTO Interdepartmental Centre for Service Robotics) wants to keep up with this incoming demand and enter in this market, deciding to invest time and resources on the optimization of an ultralight autonomous drone, with the future intention of full customization with the needs of the end-user for different and specific cases such as service robotics, smart city search and rescue and precision agriculture that are the four fields of action of the interdepartmental centre where this thesis took place. With the base of the first flying prototype and a deep research on the State Of The Art literature on drones, in particular lightweight and autonomous ones, the work done for this thesis was to optimize the drone with special attention to hardware and weight, changing the usual construction method from carbon fiber frame to a newborn design using PCB and 3D printed plastic, and finally to make it intelligent and usable by non-expert users with basic knowledge. The improvements made and overall stability of the new prototype are satisfactory, a solid point from which to develop many ultralight drone based technologies.

Navigation Algorithms for Unmanned Ground Vehicles in Precision Agriculture Applications

Candidate:  Diego Aghi
Supervisor: Prof. Marcello Chiaberge
Date: October 2019

With the rapid growth of the world population over the past years the agriculture industry is asked to respond properly to the exponential augmentation of global demand for food production. To do so, it is easy to infer that the agriculture output must be increased rapidly. However, due to environmental resources limitations, this task is not straightforward, therefore, new techniques aimed to maximize the eciency of every single land in a sustainable way are required. To this purpose, as days pass, more and more researchers are focusing their attention on the development of innovative smart farming and precision agriculture technologies by introducing automation and robotics into the agricultural processes.
This thesis is aimed to provide a low cost solution for an autonomous navigation in an unknown outdoor environment using only a camera sensor. More speci cally, it presents two algorithms able to successfully carry out an autonomous navigation in vineyards adopting machine learning and computer vision techniques. Such algorithms have been developed for applications in vineyards but they can also be employed in orchards and in any other analogous scenarios.
The work proposed in this thesis proved to be very reliable when performing motion planning by elaborating the images acquired by the camera. Nonetheless, it is to be considered as a starting point for further research in this sector.

Person tracking methodologies and algorithms in service robotic applications

Candidate:  Anna Boschi
Supervisor: Prof. Marcello Chiaberge
Date: October 2019

The vital statistics of the last century highlight a sharply increasement of the average life of the world population with a consequent growth of the number of elderly people. This scenario has caused new social needs that the research in the service robotics field is trying to fulfill. Particularly, the idea of this thesis is born at the PIC4SeR (PoliTo interdepartmental centre for service robotics) with the purpose of creating complex service robotics applications to support the autonomous and selfsufficient old people into their house in everyday life, avoiding the task of monitoring them by third parties. This work represents the first steps of a broad project in which many other service tasks will be integrated.
The main argument of this thesis is to develop algorithms and methodologies to detect, track and follow a person in an indoor environment using a small wheeled rover and low cost and available sensors to monitor the target person. Several techniques are explored showing the evolution of these methods along the years: from the classical Machine Learning algorithms to the Deep Neural Network ones. Since the main requirement to be respected is the necessity of real-time results, only few of the analysed algorithms are developed for this project scope and at the end are compared in order to find the best solution with optimal outcomes. The detection and localization are the basis of the person tracking application, done by the robot on which it has been implemented a movement control algorithm and at last it has been introduced an obstacle avoidance algorithm to prevent collisions.

Comparison of Stereo Visual Inertial Odometry Algorithms for Unmanned Ground Vehicles

Candidate:  Roberto Cappellaro
Supervisor: Prof. Marcello Chiaberge
Date: July 2019

PIC4SeR is the Interdepartmental center of PoliTO for service robotics. For its research it emerged the need to investigate indoor localization algorithms, in particular the visual-inertial type. This work aims to study different types of algorithms to assess which one is the best choice for indoor localization with the already available COTS hardware. Although the end application is meant to be UAV, a Jackal UGV is used instead, because it was the vehicle available and it lowered the risks of damages during the testing phase. A MYNTYEYE S stereo camera, with included IMU and IR projector, was available and mounted on the UGV. Three algorithms are considered: a light-weight filter-based VIO framework, ROVIO, and two optimization-based VIO frameworks, VINS-Fusion and OKVIS, that should better accommodate data asynchrony. The algorithms are tested in two different environments making the robot follow two paths multiple times: a linear path in a corridor and a pseudo-rectangular one in a room. The algorithms performance is evaluated by two parameters: the relative error on the total travelled distance and the difference between initial and final position of the UGV. The tests underlined no best algorithm, but a dependence on the environment. As future work, it would be interesting to test a camera using a tight hardware-synchronization with an IMU, since, according to the literature, should be a big source of error.

ROS-Based Data Structure for Service Robotics Applications

Candidate:  Simone Rapisarda
Supervisor: Prof. Marcello Chiaberge
Date: April 2019

The cooperation and communication between different robotic agents is a very powerful tool useful for a lot of implementations, from the mapping of areas (SLAM) to the exchange of data to achieve tasks in a shorter time or with better solutions that can allow to save resources. In particular, the possibility of having an Unmanned Ground Vehicle (UGV) communicating with an Unmanned Air Vehicle (UAV) is something that is in greater demand than ever before. For instance, in agriculture applications this cooperation is quite interesting since the mapping of vineyards can be accomplished by a rover which then shares the data with a drone that can use them to define the parameters needed for its mission planning. The purpose of this thesis is to build a common data structure able to receive information from an agent, regardless the nature of the robot (UGV or UAV), and make these data available for other robots that need to work together to achieve a common task. The first part is an introduction to the ROS environment and explains how to use this tool in order to program the robots and organize the data structure that will be used for the thesis. The second part discusses about UGVs, in particular about the two TurtleBot3 rovers which have been used for this project, the Waffle and the Burger models, and the Jackal rover. In this section it is also explained how these rovers can be controlled using the Pixhawk autopilot, which is usually used with drones and therefore needs to be properly programmed in order to be utilized with UGVs. Finally, the last part focuses on MAVROS and MAVLink protocol and explains how these two tools work and how they can be used as interface to allow the communication between the Pixhawk and the rover.

Remote Monitoring of Robots using Web Based Technologies and ROS Framework

Candidate:  Angelo Tartaglia 
Supervisor: Prof. Marcello Chiaberge
Date: October 2018

World population is growing faster than expected. Every year Earth resources are consumed faster, as proven by the early fall of the Earth Overshoot Day that claims the end of year resources. One of the simplest solutions to this problem would be investing in technologies and innovations towards a smart extraction of what population needs. In the last few years a lot of companies introduced automation and robots to improve production in terms of time and obviously cost. Agricultural world is not distant from this evolution; in fact many of the works attempted are now helped by a set of machines that simplifies human work. Hitherto the application in precision agriculture has been always under the human control; there are a lot of applications that see the participation of a worker and in parallel a machine used for a lot of tasks. In the future may an entire field will be managed by a group of automated robots that work together. Those machines will require a lot of specific features likes autonomous navigation, mapping, visual object recognition and many others. This thesis is part of that project and it regards the detection andclassificationoffruitsforfutureapplicationofauto-harvestingandhealthcontrol. Inamorespecificway apples will be considered in this thesis work for fruit application and methods and algorithms as Y.O.L.O. and Mask R-CNN to do the processing of images will be described.These techniques permits the detection of apples in post processing and in real-time with accuracies that range over 32% to 78%. The final result can be used in future applications for spatial localization of fruits and for the detection of possible diseases. It should be emphasised that, even if the thesis shows the results of the object class apple, the algorithms can be applied in a wide range of objects with the only requirement of a different training images dataset. 

Image processing algorithms for synthetic image resolution improvement

Candidate:  Francesco Salvetti
Supervisor: Prof. Marcello Chiaberge
Date: October 2019

The aim of this thesis is to develop a Machine Learning algorithm for Multi-image Super-resolution (MISR).
Super-resolution is a well known image processing problem, whose aim is to process low resolution (LR) images in order to obtain their high resolution (HR) version. The super-resolution process tries to infer the possible values of
missing pixels in order to generate high frequency information as coherently as possible with the original images.
In general, we can distinguish between two different superresolution approaches: Single-image Super-resolution (SISR) and Multi-image Super
Resolution (MISR). The former tries to build the best LR-HR mapping analysing the features of a single LR image, while the latter takes as input multiple LR images exploiting the information derived from the small differences between the images such as changes in the point of view position and orientation, in the lightening condition and
in the image exposition and contrast.
The thesis had as first objective to take part to the competition called “PROBA-V Super
Resolution”, organized by the Advanced Concept Team of the European Space Agency.
The goal of the challenge was to obtain High-resolution images from low resolution ones
from a dataset of pictures took from the PROBA-V satellite of the ESA.
The work has been developed under the direction of the PIC4SeR (PoliTO Interdepartmental Centre for Service Robotics), which aims to integrate it into its agricultural related projects, for the satellite monitoring of the fields status.
The analysis of the state of the art for what concerns super-resolution reveals that Machine Learning approaches outperform the classical algorithms proposed for this
problem. In particular, Neural Networks have been widely used in literature for Singleimage Super-resolution, while this approach for Multi-image Super-resolution is relatively new. An original model to deal with competition problem has been studied, trained and tested. The obtained results show that the multi-image approach can help in the improvement of existing algorithms for super-resolution. However, several issues can be further addressed to increase the model efficiency and performance, making this particular topic interesting for future work development.

Real-time Monitoring of Robotic Devices by using Smartphones Sensors and the ROS Framework

Candidate:  Davide Brau
Supervisor: Prof. Enrico Masala
Date: Aprile 2019

Nell’ambito della robotica, l’attività di monitoraggio risulta fondamentale per il funzionamento dell’intero sistema, sia esso controllato da un essere umano o da un algoritmo programmato per decidere in maniera autonoma. In questo contesto, con monitoraggio si intende quel processo continuo, o svolto a intervalli regolari, che ha lo scopo di acquisire le informazioni provenienti dalle varie parti del sistema robotico, nello specifico i sensori, utili per poter esercitare un controllo efficace su di esso. Durante questa attività di tesi, l’obbiettivo è stato quello di esplorare le possibilità e le soluzioni adatte a svolgere il monitoraggio, analizzando le funzionalità del frame-work: ROS. Per fare ciò, è stato necessario capire la logica utilizzata all’interno di quest’ultimo nell’interfacciarsi con i vari moduli, ponendo particolare enfasi sull’infrastruttura di comunicazione e sui formati utilizzati per i dati; focalizzandosi in seguito su quelli multimediali, in particolare le immagini. In seguito, si è cercato di mettere in pratica le nozioni acquisite, attraverso lo sviluppo di un’applicazione Android basata su ROS che ha lo scopo di fornire un supporto concreto al monitoraggio remoto. Essa consente di acquisire una grande varietà di informazioni, grazie ai vari sensori presenti sui dispositivi odierni, utili per fornire un supporto aggiuntivo e a basso costo per le operazioni di controllo, nel momento in cui lo smartphone viene messo a bordo di un qualsiasi tipo di drone o rover. Si è quindi cercato di evidenziare le varie criticità dovute sia ai limiti computazionali e di reattività tipici dei dispositivi mobili, sia alle caratteristiche della rete. Questi elementi possono determinare un aumento delle latenze o la perdita dei dati, tollerabili entro un certo limite, nel caso di controllo real-time. Attualmente, l’applicazione consente di acquisire informazioni relative alla cinematica utilizzando: accelerometro, giroscopio, magnetometro e il servizio di localizzazione. Tuttavia, il monitoraggio può anche coinvolgere l’ambiente circostante in cui il robot si muove, per tale motivo vengono acquisiti i dati riguardanti: temperatura, pressione e illuminamento; oltre ad informazioni relative alla rete cellulare, al Wi-Fi e alla batteria. L’applicazione consente anche la visualizzazione da remoto delle immagini acquisite dalle fotocamere utilizzando le codifiche: JPEG e H264. Inoltre, sono stati definiti i vari ROS Service che permettono di regolare i parametri associati ad esse. Durante la fase di sviluppo, sono stati introdotti nuovi tipi di messaggi ROS per il trasporto di informazioni associate alle classi Android, tra cui quelli specifici per il GNSS. È stato possibile sfruttare questi ultimi per la condivisione e l’elaborazione di un insieme di informazioni dettagliate ottenute dai satelliti, note con il nome di “raw measurements” (disponibili negli smartphone più recenti). Questi, se utilizzati in modo appropriato, potrebbero costituire una nuova frontiera nello sviluppo di applicazioni per il posizionamento ad alta precisione, dalle quali molti sistemi robotici potrebbero trarre grande vantaggio. Infine, l’ultima parte ha riguardato le prove con il rover Clearpath Jackal, queste si sono rivelate particolarmente utili per poter verificare in un contesto reale le difficoltà e i limiti che ci ritrova a dover affrontare nello sviluppo di questo tipo di applicazioni.

Machine Learning Algorithms for Service Robotics Applications in Precision Agriculture

Candidate:  Federico Barone 
Supervisor: Prof. Enrico Masala
Date: Aprile 2019

La robotica rappresenta uno dei settori disciplinari maggiormente in crescita nel mondo della ricerca tecnologica. In particolare, la robotica di servizio spicca tra i vari ambiti che studiano tale disciplina. Il presente elaborato si pone l’obiettivo di realizzare un’applicazione web che sia in grado di interagire con un robot da remoto. Questa permette sia di controllare i movimenti effettuati dal robot che le sue potenzialità. Di fatto, il robot presenta alcune caratteristiche dalle quali è possibile ricavare molteplici informazioni, tra cui, principalmente, la sua posizione. Inoltre, mediante l’utilizzo di un dispositivo android, è possibile utilizzare opportuni sensori presenti in esso gestendone i dati multimediali acquisiti. L’interfaccia web, nel suo complesso, ha lo scopo di monitorare da remoto l’ambiente circostante al robot, visualizzando i parametri in opportuni grafici bidimensionali. L’utilizzo del framework ROS (Robot Operating System) è alla base della programmazione robotica di servizio. Con l’emergere dell’Internet of Things (IoT), aumenta l’interesse nel fornire interfacce web che consentano agli utenti di monitorare i loro strumenti da remoto. Pertanto, sfruttando talune tecnologie Web Based è stato possibile realizzare uno strumento, composto dall’ambiente di configurazione ROS e dall’applicativo web, atto al monitoraggio remoto del robot. Usufruendo di uno o più dispositivi android posti sul robot, inoltre, è stato possibile monitorare e analizzare differenti parametri acquisiti dai vari sensori. Effettuando varie misurazioni mediante più dispositivi android, è stato possibile confrontare i dati acquisiti e trarne le dovute considerazioni.

Navigation Algorithms for Unmanned Ground Vehicles in Precision Agriculture Applications​

Candidate:  Jurgen Zoto 
Supervisor: Prof. Marcello Chiaberge
Date: October 2018

Robotics for agriculture can be considered a very recent application of one of the most ancient and important sectors, where the latest and most advanced innovations have been brought. Over the years, thanks to continous improvement in mechanization and automation, crop output has extremely increased, enabling a large growth in population and enhancing the quality of life around the world. Both these factors, as a consequence, are leading to a higher demand for agriculture and forestry output. Precision agriculture defined as the correct management of crops for increasing its productivity and maximizing harvest, is considered the answer to this issue. As a matter of fact, thanks to the development of portable sensors, the availability of satellite images and the use of drones, the collection of data is allowing a vast development in this field. This thesis adresses in general robotics for agriculture in the form of a solution to be applied in order to improve robot mobility, in particular automated path planning in agricultural fields, by proposing a method to classify different parcels of which they are composed and to assign a precise task to the terrestrial unmanned robot.

Obstacle Avoidance Algorithms for Autonomous Navigation System in Unstructured Indoor Areas

Candidate:  Lorenzo Galtarossa 
Supervisor: Prof. Marcello Chiaberge
Date: October 2018

This work aims to implement different autonomous navigation algorithms for Obstacle Avoidance that allow a robot to move and perform in an unknown and unstructured indoor environment.
The first step is the investigation and study of the platform, divided into software and hardware, available at the Mechatronics Laboratory (Laboratorio Interdisciplinare di Meccatronica, LIM) at the Politecnico di Torino, on which it is implemented the navigation algorithm. For what is concerned with the software platform, ROS has been used. The Robot Operating System is an open source framework to manage robots’ operations, tasks, motions. As hardware platform the TurtleBot3 (Waffle and Burger) has been used that is ROS-compatible.
The second step is the inspection of the different algorithms that are suitable and relevant for our purpose, goal and environment. Many techniques could be used to implement the navigation that is generally divided into global motion planning and local motion control. Often autonomous mobile robots work in an environment for which prior maps are incomplete or inaccurate. They need the safe trajectory that avoids the collision.
The algorithms presented in this document are related to the local motion planning; therefore, the robot, using the sensor mounted on it, is capable to avoid the obstacles by moving toward the free area.
Three different algorithms of Obstacle Avoidance are presented in this work, that address a complete autonomous navigation in an unstructured indoor environment. The algorithms grow in complexity taking into consideration the evolution and the possible different situations in which the robot will have to move, and all are tested on the TurtleBot3 robot, where only LiDAR was used as sensor to identify obstacles.
The third algorithm, “Autonomous Navigation”, can be considered the final work, the main advantage is the possibility to perform curved trajectory with an accurate choice of the selected path, combining the angular and the linear velocity (980 different motions), the LiDAR scans 180° in front of the robot to understand the correct direction. The last step is the automatic creation of the map. This map will be analysed and compared with the one defined using the RViz software that is the official software used in ROS environment. The tool is suitable to visualize the state of the robot and the performance of the algorithms, to debug faulty behaviours, and to record sensor data. The improvement of this reactive Obstacle Avoidance method is to successfully drive robots in Indoor troublesome areas. As conclusion we will show experimental results on TurtleBot3 in order to validate this research and provide an argumentation about the advantages and limitations.

Implementation of an Ultralight Autopilot Drone for Service Robotics

Candidate:  Salvatore Romano
Supervisor: Prof. Marcello Chiaberge
Date: December 2018

In this thesis, after a brief introduction on the regulation and classification of UAVs, the main sizing criteria of each component of a multirotor will be shown. Starting from the design constraints and a state of the art of the main flight controllers, the hardware and firmware components chosen for the implementation of an autopilot quadcopter under 250 grams will be described. The sizing of the components will be strongly nfluenced by the weight of each of them and will be flanked by a test on the motor / propeller coupling to evaluate the performance and then to choose the most suitable devices for the purpose. Once the Pixracer
has been identified as the best flight controller for the project, the PX4 firmware and related software for remote control (Mission Planner and QgraounControl) will be described. Finally, after the assembly phase, the evaluation tests of the performance of the aircraft, the problems encountered and the possible solutions
and improvements will be described.