Master's Thesis Completed Projects

Machine Learning Algorithms for Service Robotics Applications in Precision Agriculture

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. 

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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.

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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.

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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.

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