478 research outputs found
Safe Controller Optimization for Quadrotors with Gaussian Processes
One of the most fundamental problems when designing controllers for dynamic
systems is the tuning of the controller parameters. Typically, a model of the
system is used to obtain an initial controller, but ultimately the controller
parameters must be tuned manually on the real system to achieve the best
performance. To avoid this manual tuning step, methods from machine learning,
such as Bayesian optimization, have been used. However, as these methods
evaluate different controller parameters on the real system, safety-critical
system failures may happen. In this paper, we overcome this problem by
applying, for the first time, a recently developed safe optimization algorithm,
SafeOpt, to the problem of automatic controller parameter tuning. Given an
initial, low-performance controller, SafeOpt automatically optimizes the
parameters of a control law while guaranteeing safety. It models the underlying
performance measure as a Gaussian process and only explores new controller
parameters whose performance lies above a safe performance threshold with high
probability. Experimental results on a quadrotor vehicle indicate that the
proposed method enables fast, automatic, and safe optimization of controller
parameters without human intervention.Comment: IEEE International Conference on Robotics and Automation, 2016. 6
pages, 4 figures. A video of the experiments can be found at
http://tiny.cc/icra16_video . A Python implementation of the algorithm is
available at https://github.com/befelix/SafeOp
Safe Learning of Quadrotor Dynamics Using Barrier Certificates
To effectively control complex dynamical systems, accurate nonlinear models
are typically needed. However, these models are not always known. In this
paper, we present a data-driven approach based on Gaussian processes that
learns models of quadrotors operating in partially unknown environments. What
makes this challenging is that if the learning process is not carefully
controlled, the system will go unstable, i.e., the quadcopter will crash. To
this end, barrier certificates are employed for safe learning. The barrier
certificates establish a non-conservative forward invariant safe region, in
which high probability safety guarantees are provided based on the statistics
of the Gaussian Process. A learning controller is designed to efficiently
explore those uncertain states and expand the barrier certified safe region
based on an adaptive sampling scheme. In addition, a recursive Gaussian Process
prediction method is developed to learn the complex quadrotor dynamics in
real-time. Simulation results are provided to demonstrate the effectiveness of
the proposed approach.Comment: Submitted to ICRA 2018, 8 page
Barrier Functions in Cascaded Controller: Safe Quadrotor Control
Safe control for inherently unstable systems such as quadrotors is crucial.
Imposing multiple dynamic constraints simultaneously on the states for safety
regulation can be a challenging problem. In this paper, we propose a quadratic
programming (QP) based approach on a cascaded control architecture for
quadrotors to enforce safety. Safety regions are constructed using control
barrier functions (CBF) while explicitly considering the nonlinear
underactuated dynamics of the quadrotor. The safety regions constructed using
CBFs establish a non-conservative forward invariant safe region for quadrotor
navigation. Barriers imposed across the cascaded architecture allows
independent safety regulation in quadrotor's altitude and lateral domains.
Despite barriers appearing in a cascaded fashion, we show preservation of
safety for quadrotor motion in SE(3). We demonstrate the feasibility of our
method on a quadrotor in simulation with static and dynamic constraints
enforced on position and velocity spaces simultaneously.Comment: Submitted to ACC 2020, 8 pages, 7 figure
AutoTune: Controller Tuning for High-Speed Flight
Due to noisy actuation and external disturbances, tuning controllers for
high-speed flight is very challenging. In this paper, we ask the following
questions: How sensitive are controllers to tuning when tracking high-speed
maneuvers? What algorithms can we use to automatically tune them? To answer the
first question, we study the relationship between parameters and performance
and find out that the faster the maneuver, the more sensitive a controller
becomes to its parameters. To answer the second question, we review existing
methods for controller tuning and discover that prior works often perform
poorly on the task of high-speed flight. Therefore, we propose AutoTune, a
sampling-based tuning algorithm specifically tailored to high-speed flight. In
contrast to previous work, our algorithm does not assume any prior knowledge of
the drone or its optimization function and can deal with the multi-modal
characteristics of the parameters' optimization space. We thoroughly evaluate
AutoTune both in simulation and in the physical world. In our experiments, we
outperform existing tuning algorithms by up to 90\% in trajectory completion.
The resulting controllers are tested in the AirSim Game of Drones competition,
where we outperform the winner by up to 25\% in lap-time. Finally, we show that
AutoTune improves tracking error when flying a physical platform with respect
to parameters tuned by a human expert.Comment: Video: https://youtu.be/m2q_y7C01So; Code:
https://github.com/uzh-rpg/mh_autotun
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