Autonomous Unmanned Aerial Vehicles (UAVs) have numerous applications due to their agility and flexibility. However, navigation algorithms are computationally demanding, and it is challenging to run them on-board of nano-scale UAVs (i.e., few centimeters of diameter).
This project focuses on the object tracking, (i.e., target following) on such nano-UAVs.
To do this, we will first train a Convolutional Neural Network (CNN) with data collected in simulation, and then
run the aforementioned network on a parallel ultra-low-power (PULP) processor, enabling flight with on-board sensing and computing only.
**Requirements**: Knowledge of python, cpp and embedded programming. Machine learning knowledge is a plus but it is not strictly required.
Autonomous Unmanned Aerial Vehicles (UAVs) have numerous applications due to their agility and flexibility. However, navigation algorithms are computationally demanding, and it is challenging to run them on-board of nano-scale UAVs (i.e., few centimeters of diameter). This project focuses on the object tracking, (i.e., target following) on such nano-UAVs.
To do this, we will first train a Convolutional Neural Network (CNN) with data collected in simulation, and then run the aforementioned network on a parallel ultra-low-power (PULP) processor, enabling flight with on-board sensing and computing only.
**Requirements**: Knowledge of python, cpp and embedded programming. Machine learning knowledge is a plus but it is not strictly required.