Davide Falanga

MSc University of Naples "Federico II"

Robotics and Perception Group

Department of Informatics

University of Zurich

Email: falanga (at) ifi (dot) uzh (dot) ch

Office: Andreasstrasse 15, AND 2.16

Linkedin

Biography


I am currently a Ph.D. student at the Robotics and Perception Group under the supervision Prof. Davide Scaramuzza. My research interests lie in the area of control and planning for vision-based Micro Aerial Vehicles (MAV). More specifically, I am interested in coupling control and onboard perception to leaverage the incredible agility that quadrotors have recently shown within motion-capture systems, without relying on any external infrastractures. I received my Bachelor degree and my Master degree in control engineering from University of Naples in 2012 and 2015, respectively. I accomplished my Master thesis on Robotic Nonprehensile Dynamic Manipulation at the PRISMA Lab, led by Prof. Bruno Siciliano.


Research Interests


Agile Flight with Vision-based Quadrotors

Quadrotors are very agile, yet simple aerial vehicles, and recent work showed they can execute extremely complex maneuvers. Most of this work relies on motion-capture systems for state estimation, preventing those machines from exploiting their potentials in the real world. Conversely, I am interested in executing agile flight with quadrotors using solely onboard sensing (namely, a single camera and an IMU) and computing. This leads to a number of interesting challenges and open questions, since perception and control cannot be treated as two separated problems, but need to be coupled. Examples of such aggressive maneuvers are passing through narrow inclined gaps and flying at high speed in cluttered unknown environments.

Publications


ICRA17_falanga

D. Falanga, E. Mueggler, M. Faessler, D. Scaramuzza

Aggressive Quadrotor Flight through Narrow Gaps with Onboard Sensing and Computing

IEEE International Conference on Robotics and Automation (ICRA), under review, 2016.

Video


Videos


Aggressive Quadrotor Flight through Narrow Gaps with Onboard Sensing and Computing

We present a method to let a quadrotor autonomously pass through narrow gaps using only onboard sensing and computing. We estimate the full state by fusing gap detections from a single onboard camera with an IMU. We generate a trajectory that considers geometric, dynamic, and perception constraints. During the approach maneuver, the quadrotor always faces the gap to allow robust state estimation. During the traverse through the gap, the quadrotor maximizes the distance from the edges of the gap to minimize the risk of collision. We can pass through gaps with only 10 centimeters of tolerance. Our method does not require any prior knowledge about the position and the orientation of the gap.

Media Coverage


  • IEEE Spectrum: Aggressive Quadrotors Conquer Gaps With Ultimate Autonomy. [Link]
  • Robohub: Drone flight through narrow gaps using onboard sensing and computing. [Link]
  • DIYDrones: PX4-based "aggressive quadcopter" navigates gaps with pure autonomy. [Link]

Supervised Student Projects


If you are a student looking for a project, please check this page.


  • Philipp Foehn (Master Thesis - Ongoing). In collaboration with Toyota Research Institute and MIT.
    Nonlinear Control for Slungload Throwing using Quadrotors.
  • Robin Scherrer (Semester Thesis - Ongoing). In collaboration with Toyota Research Institute and MIT.
    Development of a self-calibration method for quadrotors using only the onboard sensors.
  • Maria Chiara Giorgetti (Semester Thesis - Ongoing). In collaboration with Toyota Research Institute and MIT.
    Drake-ROS integration for Quadrotor control and gain tuning.
  • Alessio Zanchettin (Master Thesis - 2016).
    Autonomous Quadrotor Landing on a Moving Platform with only Onboard Sensing and Computing.
  • Valentin Wuest (Semester Thesis - 2016).
    Collaborative Transportation with Vision Based Quadrotors.
  • Philipp Foehn (Semester Thesis - 2016).
    Impedance Control for Physical Interaction with Quadrotors.
  • Kevin Egger (Semester Thesis - 2016).
    On-board Height Estimation for Quadrotors.