Open positions
Master thesis
Application deadline: 31/01/2023
PIC4SeR Thesis
Sensor fusion as a solution to the Kidnapped Robot Problem
Supervisor: Prof. Chiaberge, Ostuni, Eirale, Ambrosio, Boretti
Skills: C++, Python, ROS2, Statistics
Code: 2023PIC01
Description: In robotics, the kidnapped robot problem is the situation where an autonomous robot in operation is carried to an arbitrary location. It is currently one of the most difficult and unsolved problems in the robotics field. This thesis project aims to develop a system able to relocalize the robot by exploiting several sensors (cameras, LiDAR, IMUs, Encoders, etc.) and techniques (traditional methods and learning-based) to solve the kidnapped robot problem.
Complex environment exploration
Supervisor: Prof. Chiaberge, Eirale, Boretti, Martini
Skills: C++/Python, ROS2
Code: 2023PIC02
Description: In order for the robotic platform to properly operate, the knowledge of the working environment is a fundamental aspect. Often this information is not previously provided and could be difficult or dangerous to retrieve. This thesis aims to develop a policy-based agent to fully explore a complex, unknown environment.
Edge Novelty Recognition
Supervisor: Prof. Chiaberge, Albertin, Dara
Skills: C, Python
Code: 2023PIC03
Description: Novelty recognition is a “parallel branch” of predictive maintenance that recognizes novelties from the trained data. It can be useful when no historical data about failures is collected (e.g., a new machine). The thesis concerns the study and development of a novelty detection algorithm to be deployed in an embedded device for predictive maintenance purposes.
Terrain Traversability Analysis for (Planetary Exploration) Rovers
Supervisor: Prof. Chiaberge, Franchini, Messina
Skills: C++/Python, ROS2
Code: 2023PIC04
Description: Novelty recognition is a “parallel branch” of predictive maintenance that recognizes novelties from the trained data. It can be useful when no historical data about failures is collected (e.g., a new machine). The thesis concerns the study and development of a novelty detection algorithm to be deployed in an embedded device for predictive maintenance purposes.
Deep Computer Vision for Social Robotics
Supervisor: Prof. Chiaberge, Angarano, Martini, Eirale, Boretti
Skills: Python, Deep Learning, Linear Algebra
Code: 2023PIC05
Description: Human-Robot Interaction (HRI) is a fundamental component of intelligent robots for well-being which aims to live beside us in the society. However, the nature of HRI can be different according to the context and the human behaviour. This thesis aims at investigating Deep Learning based robotic perception methods for extracting environmental features, in order to build a semantic representation of the human condition (available to interact/busy) and predict the best way of interacting with him/her (visually, talking, physically).
Navigation and Obstacle Avoidance in Vineyards
Supervisor: Prof. Chiaberge, Martini, Mazzara, Ostuni
Skills: Python, Deep Learning, ROS2
Code: 2023PIC06
Description: Low-cost position-agnostic navigation is the solution to GPS-denied conditions in outdoor precision agriculture applications. The two main tasks of a mobile robot in vineyard rows are following the plant rows and avoiding obstacles (boxes, people, etc.) on its trajectory. Multi-class semantic segmentation and multi-objective control strategy can be combined to control the robot’s motion and obtain the resulting final behavior. This thesis aims to improve existing visual-based navigation for vineyard rows.
Industrial Thesis
Optimised time domain response using motion planning innovative strategy
Supervisor: Prof. Chiaberge, Albertin, Lacagnina (Etel supervisor)
Collaboration: Etel SA
Skills: Python, System Control Theory
Code: 2023PIC07
Description: The motion systems provide different response while submitted at diverse motion profiles (stroke, acceleration, speed, jerk, snap). The level of vibration and energy during motion change as a function of the those parameters inducing a different response of its elements (cables, mechanical parts, etc.). The goal of the thesis is to identify possible “strategies” (motion profile, frequency rejection technics, learning algorithm…) which limit the energy induced and optimise the motion systems responses.
Heat flow management in motion systems for accuracy optimisation
Supervisor: Prof. Chiaberge, Albertin, Lacagnina (Etel supervisor)
Collaboration: Etel SA
Skills: CAD modelling
Code: 2023PIC08
Description: The accuracy of motion systems is affected by the thermal transient and gradient during operation. Those elements generate a “non linear” distortion of the structure which create accuracy losses at machine level. The capability to reduce or avoid such effect is then a key element to improve the motion systems performances. The goal of the thesis is to identify possible strategy of thermal management and/or evacuation (e.g. heat pipe) to minimize thermal gradient and transient of the systems during operation from cold to steady state condition.
Apply AI technics to optimise motion systems feed-forward
Supervisor: Prof. Chiaberge, Albertin, Lacagnina (Etel supervisor)
Collaboration: Etel SA
Skills: Python, System Control Theory
Code: 2023PIC09
Description: The motion systems performances are closely related to the feed-forward capabilities. The systems characteristics are not “always” constant and linear. For instance, the moving “apparent” mass of dynamic cable changes as a function of the position. In addition, non linearity, such as friction, could present different responses over time and based on specific positions and boundary conditions. The goal of the thesis is to analyse in details those behaviours and apply Artificial Intelligence technics to optimised and/or adapt the feed-forward of our advanced motion systems.