1,136 research outputs found
A tracking error control approach for model predictive position control of a quadrotor with time varying reference
In mobile robotic applications, a common problem
is the following of a given trajectory with a constant velocity.
Using standard model predictive control (MPC) for tracking
of time varying trajectories leads to a constant tracking error.
This problem is modelled in this paper as quadrotor position
tracking problem. The presented solution is a computationally
light-weight target position control (T PC), that controls the
tracking error of MPCs for constantly moving targets. The
proposed technique is assessed mathematically in the Laplace
domain, in simulation, as well as experimentally on a real
quadrotor system
A survey on fractional order control techniques for unmanned aerial and ground vehicles
In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade
PAMPC: Perception-Aware Model Predictive Control for Quadrotors
We present the first perception-aware model predictive control framework for
quadrotors that unifies control and planning with respect to action and
perception objectives. Our framework leverages numerical optimization to
compute trajectories that satisfy the system dynamics and require control
inputs within the limits of the platform. Simultaneously, it optimizes
perception objectives for robust and reliable sens- ing by maximizing the
visibility of a point of interest and minimizing its velocity in the image
plane. Considering both perception and action objectives for motion planning
and control is challenging due to the possible conflicts arising from their
respective requirements. For example, for a quadrotor to track a reference
trajectory, it needs to rotate to align its thrust with the direction of the
desired acceleration. However, the perception objective might require to
minimize such rotation to maximize the visibility of a point of interest. A
model-based optimization framework, able to consider both perception and action
objectives and couple them through the system dynamics, is therefore necessary.
Our perception-aware model predictive control framework works in a
receding-horizon fashion by iteratively solving a non-linear optimization
problem. It is capable of running in real-time, fully onboard our lightweight,
small-scale quadrotor using a low-power ARM computer, to- gether with a
visual-inertial odometry pipeline. We validate our approach in experiments
demonstrating (I) the contradiction between perception and action objectives,
and (II) improved behavior in extremely challenging lighting conditions
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, 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. 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
Suspended Load Path Tracking Control Using a Tilt-rotor UAV Based on Zonotopic State Estimation
This work addresses the problem of path tracking control of a suspended load
using a tilt-rotor UAV. The main challenge in controlling this kind of system
arises from the dynamic behavior imposed by the load, which is usually coupled
to the UAV by means of a rope, adding unactuated degrees of freedom to the
whole system. Furthermore, to perform the load transportation it is often
needed the knowledge of the load position to accomplish the task. Since
available sensors are commonly embedded in the mobile platform, information on
the load position may not be directly available. To solve this problem in this
work, initially, the kinematics of the multi-body mechanical system are
formulated from the load's perspective, from which a detailed dynamic model is
derived using the Euler-Lagrange approach, yielding a highly coupled, nonlinear
state-space representation of the system, affine in the inputs, with the load's
position and orientation directly represented by state variables. A zonotopic
state estimator is proposed to solve the problem of estimating the load
position and orientation, which is formulated based on sensors located at the
aircraft, with different sampling times, and unknown-but-bounded measurement
noise. To solve the path tracking problem, a discrete-time mixed
controller with pole-placement constraints
is designed with guaranteed time-response properties and robust to unmodeled
dynamics, parametric uncertainties, and external disturbances. Results from
numerical experiments, performed in a platform based on the Gazebo simulator
and on a Computer Aided Design (CAD) model of the system, are presented to
corroborate the performance of the zonotopic state estimator along with the
designed controller
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