26 research outputs found
Aerial-Ground collaborative sensing: Third-Person view for teleoperation
Rapid deployment and operation are key requirements in time critical
application, such as Search and Rescue (SaR). Efficiently teleoperated ground
robots can support first-responders in such situations. However, first-person
view teleoperation is sub-optimal in difficult terrains, while a third-person
perspective can drastically increase teleoperation performance. Here, we
propose a Micro Aerial Vehicle (MAV)-based system that can autonomously provide
third-person perspective to ground robots. While our approach is based on local
visual servoing, it further leverages the global localization of several ground
robots to seamlessly transfer between these ground robots in GPS-denied
environments. Therewith one MAV can support multiple ground robots on a demand
basis. Furthermore, our system enables different visual detection regimes, and
enhanced operability, and return-home functionality. We evaluate our system in
real-world SaR scenarios.Comment: Accepted for publication in 2018 IEEE International Symposium on
Safety, Security and Rescue Robotics (SSRR
Effective Target Aware Visual Navigation for UAVs
In this paper we propose an effective vision-based navigation method that
allows a multirotor vehicle to simultaneously reach a desired goal pose in the
environment while constantly facing a target object or landmark. Standard
techniques such as Position-Based Visual Servoing (PBVS) and Image-Based Visual
Servoing (IBVS) in some cases (e.g., while the multirotor is performing fast
maneuvers) do not allow to constantly maintain the line of sight with a target
of interest. Instead, we compute the optimal trajectory by solving a non-linear
optimization problem that minimizes the target re-projection error while
meeting the UAV's dynamic constraints. The desired trajectory is then tracked
by means of a real-time Non-linear Model Predictive Controller (NMPC): this
implicitly allows the multirotor to satisfy both the required constraints. We
successfully evaluate the proposed approach in many real and simulated
experiments, making an exhaustive comparison with a standard approach.Comment: Conference paper at "European Conference on Mobile Robotics" (ECMR)
201
Outdoor Autonomous Landing of a Quadcopter on a Moving Platform using Off-board Computer Vision
This paper presents a method that enables a quadcopter to perform autonomous landing on a moving platform using computer vision. In addition, the system implementation of the computer vision technique is presented. Unlike other researches, the camera is mounted on the moving platform instead of being installed on the quadcopter. Besides, the computer vision system is tested outdoor, and the results such as the performance and the accuracy are presented. In the stationary platform test, 5 out of 10 landings fall within 30 cm from the center. In the moving platform test, the maximum platform-moving speed for autonomous landing is 2 m/s. Hence, it is proven that this methodology is feasible. Lastly, the advantages and limitations of the computer vision technique proposed are discussed
Asymptotic Vision-Based Tracking Control of the Quadrotor Aerial Vehicle
This paper proposes an image-based visual servo (IBVS) controller for the 3D translational
motion of the quadrotor unmanned aerial vehicle (UAV). The main purpose of this paper is to
provide asymptotic stability for vision-based tracking control of the quadrotor in the presence
of uncertainty in the dynamic model of the system. The aim of the paper also includes the use
of
ow of image features as the velocity information to compensate for the unreliable linear
velocity data measured by accelerometers. For this purpose, the mathematical model of the
quadrotor is presented based on the optic
ow of image features which provides the possibility
of designing a velocity-free IBVS controller with considering the dynamics of the robot. The
image features are de ned from a suitable combination of perspective image moments without
using the model of the object. This property allows the application of the proposed controller
in unknown places. The controller is robust with respect to the uncertainties in the transla-
tional dynamics of the system associated with the target motion, image depth and external
disturbances. Simulation results and a comparison study are presented which demonstrate the
e ectiveness of the proposed approach
Real-time UAV Complex Missions Leveraging Self-Adaptive Controller with Elastic Structure
The expectation of unmanned air vehicles (UAVs) pushes the operation
environment to narrow spaces, where the systems may fly very close to an object
and perform an interaction. This phase brings the variation in UAV dynamics:
thrust and drag coefficient of the propellers might change under different
proximity. At the same time, UAVs may need to operate under external
disturbances to follow time-based trajectories. Under these challenging
conditions, a standard controller approach may not handle all missions with a
fixed structure, where there may be a need to adjust its parameters for each
different case. With these motivations, practical implementation and evaluation
of an autonomous controller applied to a quadrotor UAV are proposed in this
work. A self-adaptive controller based on a composite control scheme where a
combination of sliding mode control (SMC) and evolving neuro-fuzzy control is
used. The parameter vector of the neuro-fuzzy controller is updated adaptively
based on the sliding surface of the SMC. The autonomous controller possesses a
new elastic structure, where the number of fuzzy rules keeps growing or get
pruned based on bias and variance balance. The interaction of the UAV is
experimentally evaluated in real time considering the ground effect, ceiling
effect and flight through a strong fan-generated wind while following
time-based trajectories.Comment: 18 page