1,820 research outputs found
Adaptive fast open-loop maneuvers for quadrocopters
We present a conceptually and computationally lightweight method for the design and iterative learning of fast maneuvers for quadrocopters. We use first-principles, reduced-order models and we do not require nor make an attempt to follow a specific state trajectory—only the initial and the final states of the vehicle are taken into account. We evaluate the adaptation scheme through experiments on quadrocopters in the ETH Flying Machine Arena that perform multi-flips and other high-performance maneuver
Deep Drone Acrobatics
Performing acrobatic maneuvers with quadrotors is extremely challenging.
Acrobatic flight requires high thrust and extreme angular accelerations that
push the platform to its physical limits. Professional drone pilots often
measure their level of mastery by flying such maneuvers in competitions. In
this paper, we propose to learn a sensorimotor policy that enables an
autonomous quadrotor to fly extreme acrobatic maneuvers with only onboard
sensing and computation. We train the policy entirely in simulation by
leveraging demonstrations from an optimal controller that has access to
privileged information. We use appropriate abstractions of the visual input to
enable transfer to a real quadrotor. We show that the resulting policy can be
directly deployed in the physical world without any fine-tuning on real data.
Our methodology has several favorable properties: it does not require a human
expert to provide demonstrations, it cannot harm the physical system during
training, and it can be used to learn maneuvers that are challenging even for
the best human pilots. Our approach enables a physical quadrotor to fly
maneuvers such as the Power Loop, the Barrel Roll, and the Matty Flip, during
which it incurs accelerations of up to 3g.Comment: 8 pages + 2 pages references. Video: https://youtu.be/2N_wKXQ6MXA.
Code: https://github.com/uzh-rpg/deep_drone_acrobatic
Career: artificial learning control systems for performance critical applications
Issued as final reportNational Science Foundation (U.S.
Data-Driven MPC for Quadrotors
Aerodynamic forces render accurate high-speed trajectory tracking with
quadrotors extremely challenging. These complex aerodynamic effects become a
significant disturbance at high speeds, introducing large positional tracking
errors, and are extremely difficult to model. To fly at high speeds, feedback
control must be able to account for these aerodynamic effects in real-time.
This necessitates a modelling procedure that is both accurate and efficient to
evaluate. Therefore, we present an approach to model aerodynamic effects using
Gaussian Processes, which we incorporate into a Model Predictive Controller to
achieve efficient and precise real-time feedback control, leading to up to 70%
reduction in trajectory tracking error at high speeds. We verify our method by
extensive comparison to a state-of-the-art linear drag model in synthetic and
real-world experiments at speeds of up to 14m/s and accelerations beyond 4g.Comment: 8 page
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