I joined the Robotics and Perception Group in 2015, and I am currently pursuing a Ph.D. 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.
I received my Bachelor and Master degrees 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.
Robotics is often defined as the "intelligent connection of perception to action" (M. Bradley, "Artificial Intelligence and Robotics", MIT, 1984 [Link]). My research interests lie at the intersection between perception and control. More specifically, I am interested in using control techniques to exploit the benefits of onboard sensors.
Agile Quadrotors Flight using Active Vision
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 (active vision). Examples of such aggressive maneuvers are passing through narrow inclined gaps and flying at high speed in cluttered unknown environments.
M. Faessler, D. Falanga, and D. Scaramuzza
Thrust Mixing, Saturation, and Body-Rate Control for Accurate Aggressive Quadrotor Flight
IEEE Robotics and Automation Letters (RA-L), 2016.
Quadrotors are well suited for executing fast maneuvers with high accelerations but they are still unable to follow a fast trajectory with centimeter accuracy without iteratively learning it beforehand. In this paper, we present a novel body-rate controller and an iterative thrust-mixing scheme, which improve the trajectory-tracking performance without requiring learning and reduce the yaw control error of a quadrotor, respectively. Furthermore, to the best of our knowledge, we present the first algorithm to cope with motor saturations smartly by prioritizing control inputs which are relevant for stabilization and trajectory tracking. The presented body-rate controller uses LQR-control methods to consider both the body rate and the single motor dynamics, which reduces the overall trajectory-tracking error while still rejecting external disturbances well. Our iterative thrust-mixing scheme computes the four rotor thrusts given the inputs from a position-control pipeline. Through the iterative computation, we are able to consider a varying ratio of thrust and drag torque of a single propeller over its input range, which allows applying the desired yaw torque more precisely and hence reduces the yaw-control error. Our prioritizing motor-saturation scheme improves stability and robustness of a quadrotor’s flight and may prevent unstable behavior in case of motor saturations. We demonstrate the improved trajectory tracking, yaw-control, and robustness in case of motor saturations in real-world experiments with a quadrotor.
Aggressive Quadrotor Flight through Narrow Gaps with Onboard Sensing and Computing using Active Vision
We address one of the main challenges towards autonomous quadrotor flight in complex environments, which is flight through narrow gaps. While previous works relied on off-board localization systems or on accurate prior knowledge of the gap position and orientation in the world reference frame, we rely solely on onboard sensing and computing and estimate the full state by fusing gap detection from a single onboard camera with an IMU. This problem is challenging for two reasons: (i) the quadrotor pose uncertainty with respect to the gap increases quadratically with the distance from the gap; (ii) the quadrotor has to actively control its orientation towards the gap to enable state estimation (i.e., active vision). We solve this problem by generating a trajectory that considers geometric, dynamic, and perception constraints: during the approach maneuver, the quadrotor always faces the gap to allow state estimation, while respecting the vehicle dynamics; during the traverse through the gap, the distance of the quadrotor to the edges of the gap is maximized. Furthermore, we replan the trajectory during its execution to cope with the varying uncertainty of the state estimate. We successfully evaluate and demonstrate the proposed approach in many real experiments, achieving a success rate of 80% and gap orientations up to 45°. To the best of our knowledge, this is the first work that addresses and achieves autonomous, aggressive flight through narrow gaps using only onboard sensing and computing and without prior knowledge of the pose of the gap.
P. Foehn, D. Falanga, N. Kuppuswamy, R. Tedrake, D. Scaramuzza
Fast Trajectory Optimization for Agile Quadrotor Maneuvers with a Cable-Suspended Payload
Robotics: Science and Systems (RSS), Boston, 2017.
Best Student Paper Award Finalist! Accepted for oral presentation.
Executing agile quadrotor maneuvers with cable-suspended payloads is a challenging problem and complications induced by the dynamics typically require trajectory optimization. State-of-the-art approaches often need significant computation time and complex parameter tuning. We present a novel dynamical model and a fast trajectory optimization algorithm for quadrotors with a cable-suspended payload. Our first contribution is a new formulation of the suspended payload behavior, modeled as a link attached to the quadrotor with a combination of two revolute joints and a prismatic joint, all being passive. Differently from state of the art, we do not require the use of hybrid modes depending on the cable tension. Our second contribution is a fast trajectory optimization technique for the aforementioned system. Our model enables us to pose the trajectory optimization problem as a Mathematical Program with Complementarity Constraints (MPCC). Desired behaviors of the system (e.g., obstacle avoidance) can easily be formulated within this framework. We show that our approach outperforms the state of the art in terms of computation speed and guarantees feasibility of the trajectory with respect to both the system dynamics and control input saturation, while utilizing far fewer tuning parameters. We experimentally validate our approach on a real quadrotor showing that our method generalizes to a variety of tasks, such as flying through desired waypoints while avoiding obstacles, or throwing the payload toward a desired target. To the best of our knowledge, this is the first time that three-dimensional, agile maneuvers exploiting the system dynamics have been achieved on quadrotors with a cable-suspended payload.
D. Falanga, A. Zanchettin, A. Simovic, J. Delmerico, D. Scaramuzza
Vision-based Autonomous Quadrotor Landing on a Moving Platform
IEEE/RSJ International Symposium on Safety, Security and Rescue Robotics (SSRR), Shanghai, 2017.
We present a quadrotor system capable of autonomously landing on a moving platform using only onboard sensing and computing. We rely on state-of-the-art computer vision algorithms, multi-sensor fusion for localization of the robot, detection and motion estimation of the moving platform, and path planning for fully autonomous navigation. Our system does not require any external infrastructure, such as motioncapture systems. No prior information about the location of the moving landing target is needed. We validate our system in both synthetic and real-world experiments using low-cost and lightweight consumer hardware. To the best of our knowledge, this is the first demonstration of a fully autonomous quadrotor system capable of landing on a moving target, using only onboard sensing and computing, without relying on any external infrastructure.
Media and Press Coverage
- IEEE Spectrum: Aggressive Quadrotors Conquer Gaps With Ultimate Autonomy. [Link]
- MIT Technology Review: Watch This Robotic Quadcopter Fly Aggressively Through Narrow Gaps [Link]
- Digital Trends: Daredevil Drones Can Navigate Narrow Gaps at High Speed [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 - 2017). In collaboration with Toyota Research Institute and MIT.
Nonlinear Control for Slungload Throwing using Quadrotors.
- Robin Scherrer (Semester Thesis - 2016). 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 - 2016). 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.