103 research outputs found
Pushbroom Stereo for High-Speed Navigation in Cluttered Environments
We present a novel stereo vision algorithm that is capable of obstacle
detection on a mobile-CPU processor at 120 frames per second. Our system
performs a subset of standard block-matching stereo processing, searching only
for obstacles at a single depth. By using an onboard IMU and state-estimator,
we can recover the position of obstacles at all other depths, building and
updating a full depth-map at framerate.
Here, we describe both the algorithm and our implementation on a high-speed,
small UAV, flying at over 20 MPH (9 m/s) close to obstacles. The system
requires no external sensing or computation and is, to the best of our
knowledge, the first high-framerate stereo detection system running onboard a
small UAV
External localization system for mobile robotics
We present a fast and precise vision-based software intended for multiple robot localization. The core component of
the proposed localization system is an efficient method for black and white circular pattern detection. The method is robust to variable lighting conditions, achieves sub-pixel precision, and its computational complexity is independent of the processed image size. With off-the-shelf computational equipment and low-cost camera, its core algorithm is able to process hundreds of images per second while tracking hundreds of objects with millimeter precision. We propose a mathematical model of the method that allows to calculate its precision, area of coverage, and processing speed from the camera’s intrinsic parameters and hardware’s processing capacity. The correctness of the presented model and
performance of the algorithm in real-world conditions are verified in several experiments. Apart from the method description, we also publish its source code; so, it can be used as an enabling technology for various mobile robotics problems
Impedance control of a planar quadrotor with an extended Kalman filter external wrench estimator
In this work we deal with the non-linear control of aerial vehicles under external disturbances. We develop a non-linear velocity controller able to accommodate estimations of the external disturbing forces and moments. To estimate the external actions and at the same time provide improvements on the state estimation we make use of the EKF approach. Finally, we present simulations comparing close loop performance of a system with the proposed methodology implemented against close loop performance of the same controller but without the
estimation of the external forces.Postprint (published version
Geometric Adaptive Control for a Quadrotor UAV with Wind Disturbance Rejection
This paper presents a geometric adaptive control scheme for a quadrotor
unmanned aerial vehicle, where the effects of unknown, unstructured
disturbances are mitigated by a multilayer neural network that is adjusted
online. The stability of the proposed controller is analyzed with Lyapunov
stability theory on the special Euclidean group, and it is shown that the
tracking errors are uniformly ultimately bounded with an ultimate bound that
can be abridged arbitrarily. A mathematical model of wind disturbance on the
quadrotor dynamics is presented, and it is shown that the proposed adaptive
controller is capable of rejecting the effects of wind disturbances
successfully. These are illustrated by numerical examples
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
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