Thesis Code: 20013

Thesis Type: Thesis in Mechatronic/Telecommunication/Electronic Engineering, Computer Science or equivalent

Research Area: IOT & Pervasive Technologies

• Good knowledge of MATLAB
• Basic skill in software development (C/C++ and Python)
• Good knowledge in mathematical derivation
• Proactive mindset, problem-solving oriented
• Data processing skills
• Familiarity with embedded platforms (Raspberry PI) and Linux environment

Recently, autonomous navigation of UAVs for indoor environments has received increasing attention from the research community. In fact, UAVs are going to be adopted also in these environments to support various applications, for instance, for logistic operations, to perform some inspection and monitoring tasks in industrial plants as well as in greenhouses. In these scenarios, it is fundamental to know not only the UAV’s position but also its attitude.

Regarding the position estimation, usually the Ultra-Wideband (UWB) technology is employed providing accurate Time of Arrival (ToA) measurements while for the attitude estimation, IMU sensors are typically used allowing to estimate roll and pitch of the UAV. However, the estimation of the yaw angle employing a compass sensor is too inaccurate because the Earth’s magnetic field in indoor environments is heavily affected by electric and electronic devices as well as surrounding metallic objects and structures.

To overcome this limitation, the UWB technology is becoming also a promising solution to estimate the angle with which the transmitted UWB signal arrives at the receiver, thus, exploiting this type of measurement, it is possible to estimate the yaw angle of the UAV. In particular, the UWB technology can be used to perform Angle of Arrival (AoA) measurements employing antenna array at the receiver.

 The goal of this thesis is to design a hybrid localization algorithm based on UWB enabling autonomous operations of UAVs in indoor environments. The hybrid algorithm will combine both ToA and AoA measurements to estimate both position and attitude of the UAV by using Bayesian methods like Extended Kalman Filter (EKF).

Firstly, the designed algorithm will be tested via computer simulations and iteratively optimized. After that, the optimized algorithm will be implemented in UWB devices and the performance evaluated in real indoor environments.

Contact: send a resume with attached the list of exams passed during the Bachelor of Science and Master of Sciences to or specifying the thesis title.