201 research outputs found
Autonomous aerial robot for high-speed search and intercept applications
In recent years, high-speed navigation and environment interaction in the context of
aerial robotics has become a field of interest for several academic and industrial research studies. In
particular, Search and Intercept (SaI) applications for aerial robots pose a compelling research
area due to their potential usability in several environments. Nevertheless, SaI tasks involve a
challenging development regarding sensory weight, onboard computation resources, actuation design,
and algorithms for perception and control, among others. In this work, a fully autonomous aerial
robot for high-speed object grasping has been proposed. As an additional subtask, our system is able
to autonomously pierce balloons located in poles close to the surface. Our first contribution is the
design of the aerial robot at an actuation and sensory level consisting of a novel gripper design with
additional sensors enabling the robot to grasp objects at high speeds. The second contribution is
a complete software framework consisting of perception, state estimation, motion planning, motion
control, and mission control in order to rapidly and robustly perform the autonomous grasping
mission. Our approach has been validated in a challenging international competition and has shown
outstanding results, being able to autonomously search, follow, and grasp a moving object at 6 m/s
in an outdoor environment.Agencia Estatal de InvestigaciónKhalifa Universit
The MRS UAV System: Pushing the Frontiers of Reproducible Research, Real-world Deployment, and Education with Autonomous Unmanned Aerial Vehicles
We present a multirotor Unmanned Aerial Vehicle control (UAV) and estimation
system for supporting replicable research through realistic simulations and
real-world experiments. We propose a unique multi-frame localization paradigm
for estimating the states of a UAV in various frames of reference using
multiple sensors simultaneously. The system enables complex missions in GNSS
and GNSS-denied environments, including outdoor-indoor transitions and the
execution of redundant estimators for backing up unreliable localization
sources. Two feedback control designs are presented: one for precise and
aggressive maneuvers, and the other for stable and smooth flight with a noisy
state estimate. The proposed control and estimation pipeline are constructed
without using the Euler/Tait-Bryan angle representation of orientation in 3D.
Instead, we rely on rotation matrices and a novel heading-based convention to
represent the one free rotational degree-of-freedom in 3D of a standard
multirotor helicopter. We provide an actively maintained and well-documented
open-source implementation, including realistic simulation of UAV, sensors, and
localization systems. The proposed system is the product of years of applied
research on multi-robot systems, aerial swarms, aerial manipulation, motion
planning, and remote sensing. All our results have been supported by real-world
system deployment that shaped the system into the form presented here. In
addition, the system was utilized during the participation of our team from the
CTU in Prague in the prestigious MBZIRC 2017 and 2020 robotics competitions,
and also in the DARPA SubT challenge. Each time, our team was able to secure
top places among the best competitors from all over the world. On each
occasion, the challenges has motivated the team to improve the system and to
gain a great amount of high-quality experience within tight deadlines.Comment: 28 pages, 20 figures, submitted to Journal of Intelligent & Robotic
Systems (JINT), for the provided open-source software see
http://github.com/ctu-mr
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
Towards Precise Positioning and Movement of UAVs for Near-Wall Tasks in GNSS-Denied Environments
Abstract: UAVs often perform tasks that require flying close to walls or structures and in environments where a satellite-based location is not possible. Flying close to solid bodies implies a higher risk of collisions, thus requiring an increase in the precision of the measurement and control of the UAV’s position. The aerodynamic distortions generated by nearby walls or other objects are also relevant, making the control more complex and further placing demands on the positioning system. Performing wall-related tasks implies flying very close to the wall and, in some cases, even touching it. This work presents a Near-Wall Positioning System (NWPS) based on the combination of an Ultra-wideband (UWB) solution and LIDAR-based range finders. This NWPS has been developed and tested to allow precise positioning and orientation of a multirotor UAV relative to a wall when performing tasks near it. Specific position and orientation control hardware based on horizontal thrusters has also been designed, allowing the UAV to move smoothly and safely near walls.Ministerio de Ciencia, Innovación y Universidades; RTI2018-101114-B-I00),
Xunta de Galicia; ED431C2017/12)
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
UAV or Drones for Remote Sensing Applications in GPS/GNSS Enabled and GPS/GNSS Denied Environments
The design of novel UAV systems and the use of UAV platforms integrated with robotic sensing and imaging techniques, as well as the development of processing workflows and the capacity of ultra-high temporal and spatial resolution data, have enabled a rapid uptake of UAVs and drones across several industries and application domains.This book provides a forum for high-quality peer-reviewed papers that broaden awareness and understanding of single- and multiple-UAV developments for remote sensing applications, and associated developments in sensor technology, data processing and communications, and UAV system design and sensing capabilities in GPS-enabled and, more broadly, Global Navigation Satellite System (GNSS)-enabled and GPS/GNSS-denied environments.Contributions include:UAV-based photogrammetry, laser scanning, multispectral imaging, hyperspectral imaging, and thermal imaging;UAV sensor applications; spatial ecology; pest detection; reef; forestry; volcanology; precision agriculture wildlife species tracking; search and rescue; target tracking; atmosphere monitoring; chemical, biological, and natural disaster phenomena; fire prevention, flood prevention; volcanic monitoring; pollution monitoring; microclimates; and land use;Wildlife and target detection and recognition from UAV imagery using deep learning and machine learning techniques;UAV-based change detection
Autonomous UAV System for Cleaning Insulators in Power Line Inspection and Maintenance
The inspection and maintenance tasks of electrical installations are very demanding.
Nowadays, insulator cleaning is carried out manually by operators using scaffolds, ropes, or even
helicopters. However, these operations involve potential risks for humans and the electrical structure.
The use of Unmanned Aerial Vehicles (UAV) to reduce the risk of these tasks is rising. This paper
presents an UAV to autonomously clean insulators on power lines. First, an insulator detection and
tracking algorithm has been implemented to control the UAV in operation. Second, a cleaning tool
has been designed consisting of a pump, a tank, and an arm to direct the flow of cleaning liquid.
Third, a vision system has been developed that is capable of detecting soiled areas using a semantic
segmentation neuronal network, calculating the trajectory for cleaning in the image plane, and
generating arm trajectories to efficiently clean the insulator. Fourth, an autonomous system has been
developed to land on a charging pad to charge the batteries and potentially fill the tank with cleaning
liquid. Finally, the autonomous system has been validated in a controlled outdoor environment.Ministerio de Ciencia e Innovación (CDTI) AERIAL-CORE H2020 ICT-10-2019-2020FEDER INTERCONECT
Design, Development and Implementation of Intelligent Algorithms to Increase Autonomy of Quadrotor Unmanned Missions
This thesis presents the development and implementation of intelligent algorithms to increase autonomy of unmanned missions for quadrotor type UAVs. A six-degree-of freedom dynamic model of a quadrotor is developed in Matlab/Simulink in order to support the design of control algorithms previous to real-time implementation. A dynamic inversion based control architecture is developed to minimize nonlinearities and improve robustness when the system is driven outside bounds of nominal design. The design and the implementation of the control laws are described. An immunity-based architecture is introduced for monitoring quadrotor health and its capabilities for detecting abnormal conditions are successfully demonstrated through flight testing. A vision-based navigation scheme is developed to enhance the quadrotor autonomy under GPS denied environments. An optical flow sensor and a laser range finder are used within an Extended Kalman Filter for position estimation and its estimation performance is analyzed by comparing against measurements from a GPS module. Flight testing results are presented where the performances are analyzed, showing a substantial increase of controllability and tracking when the developed algorithms are used under dynamically changing environments. Healthy flights, flights with failures, flight with GPS-denied navigation and post-failure recovery are presented
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