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Ing. Tomas Baca and RNDr. Petr Stepan, Ph.D., CTU in Prague, Department of Cybernetics
Autonomous Landing on a Moving Vehicle with an UAV + Vision techniques for on-board detection..
On 2019-03-28 14:30 at E112
Title 1 (Tomáš Báča): Autonomous Landing on a Moving Vehicle with an
Unmanned Aerial Vehicle

Abstract 1: 
his paper addresses the perception, control, and trajectory planning for an
aerial platform to identify and land on a moving car at 15 km/hr. The
hexacopter unmanned aerial vehicle (UAV), equipped with onboard sensors and a
computer, detects the car using a monocular camera and predicts the car future
movement using a nonlinear motion model. While following the car, the UAV lands
on its roof, and it attaches itself using magnetic legs. The proposed system is
fully autonomous from takeoff to landing. Numerous field tests were conducted
throughout the year‐long development and preparations for the Mohamed Bin
Zayed International Robotics Challenge (MBZIRC) 2017 competition, for which the
system was designed. We propose a novel control system in which a model
predictive controller is used in real time to generate a reference trajectory
for the UAV, which are then tracked by the nonlinear feedback controller. This
combination allows to track predictions of the car motion with minimal position
error. The evaluation presents three successful autonomous landings during the
MBZIRC 2017, where our system achieved the fastest landing among all competing

Title 2 (Petr Štěpán): Vision techniques for on-board detection, following
and mapping of moving targets

Abstract 2:
This article presents computer vision modules of a multi-unmanned aerial
vehicle (UAV) system, which scored gold, silver, and bronze medals at the
Mohamed bin Zayed International Robotics Challenge (MBZIRC) 2017. This
autonomous system, which was running completely on-board and in real-time, had
to address two complex tasks in challenging outdoor conditions. In the first
task, an autonomous UAV had to find, track, and land on a human-driven car
moving at $15$~$km/h$ on a figure-eight-shaped track. During the second task, a
group of three UAVs had to find small colored objects in a wide area, pick them
up, and deliver them into a specified drop-off zone. The computer vision
presented here achieved computationally efficient detection, accurate
localization, robust velocity estimation, and reliable future position
prediction of both the colored objects and the car. These properties had to be
achieved in adverse outdoor environments with changing light conditions.
Lighting varied from intense direct sunlight with sharp shadows cast over the
objects by the UAV itself, to reduced visibility caused by overcast to dust and
sand in the air. The results presented in this paper demonstrate good
performance of the modules both during testing, which took place in the harsh
desert environment of the central area of United Arab Emirates, as well as
during the contest, which took place at a racing complex in the urban, near-sea
location of Abu Dhabi. The stability and reliability of these modules
contributed to the overall result of the contest, where our multi-UAV system
outperformed teams from world-leading robotic laboratories in two challenging
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