Michael Mugnai

Michael Mugnai


Research Focus & Metrics

Academic Career Length

4Years (2023-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
18 July 2023 in Drones
Verified
Journal Article
Open access
PDF file
MMMichael Mugnai
Michael Mugnai
MLMassimo Teppati Losè
Massimo Teppati Losè
EHEdwin Paúl Herrera-Alarcón
Edwin Paúl Herrera-Alarcón
GBGabriele Baris
Gabriele Baris
Massimo SatlerMassimo Satler
Massimo Satler
Carlo Alberto AvizzanoCarlo Alberto Avizzano
Carlo Alberto Avizzano
Nowadays, multirotors are versatile systems that can be employed in several scenarios, where their increasing autonomy allows them to achieve complex missions without human intervention. This paper presents a framework for autonomous missions with low-cost Unmanned Aerial Vehicles (UAVs) in Global Navigation Satellite System-denied (GNSS-denied) environments. This paper presents hardware choices and software modules for localization, perception, global planning, local re-planning for obstacle avoidance, and a state machine to dictate the overall mission sequence. The entire software stack has been designed exploiting the Robot Operating System (ROS) middleware and has been extensively validated in both simulation and real environment tests. The proposed solution can run both in simulation and in real-world scenarios without modification thanks to a small sim-to-real gap with PX4 software-in-the-loop functionality. The overall system has competed successfully in the Leonardo Drone Contest, an annual competition between Italian Universities with a focus on low-level, resilient, and fully autonomous tasks for vision-based UAVs, proving the robustness of the entire system design.

13 January 2024 in Journal of Intelligent & Robotic Systems
Verified
Journal Article
Open access
PDF file
MMMichael Mugnai
Michael Mugnai
MLMassimo Teppati Losè
Massimo Teppati Losè
Massimo SatlerMassimo Satler
Massimo Satler
Carlo Alberto AvizzanoCarlo Alberto Avizzano
Carlo Alberto Avizzano
Drone technology is advancing rapidly and represents significant benefits during firefighting operations. This paper presents a novel approach for autonomous firefighting missions for Unmanned Aerial Vehicles (UAVs). The proposed UAV framework consists of a local planner module that finds an obstacle-free path to guide the vehicle toward a target zone. After detecting the target point, the UAV plans an optimal trajectory to perform a precision ballistic launch of an extinguishing ball, exploiting its kinematics. The generated trajectory minimises the overall traversal time and the final state error while respecting UAV dynamic limits. The performance of the proposed system is evaluated both in simulations and real tests with randomly positioned obstacles and target locations. The proposed framework has been employed in the 2022 UAV Competition of the International Conference on Unmanned Aircraft Systems (ICUAS), where it successfully completed the mission in several runs of increasing difficulty, both in simulation and in real scenarios, achieving third place overall. A video attachment to this paper is available on the website https://www.youtube.com/watch?v=_hdxX2xXkVQ.