2,049 research outputs found
Accurate navigation applied to landing maneuvers on mobile platforms for unmanned aerial vehicles
Drones are quickly developing worldwide and in Europe in particular. They represent the future of a high percentage of operations that are currently carried out by manned aviation or satellites. Compared to fixed-wing UAVs, rotary wing UAVs have as advantages the hovering, agile maneuvering and vertical take-off and landing capabilities, so that they are currently the most used aerial robotic platforms.
In operations from ships and boats, the final approach and the landing maneuver are the phases of the operation that involves a higher risk and where it is required a higher level of precision in the position and velocity estimation, along with a high level of robustness in the operation. In the framework of the EC-SAFEMOBIL and the REAL projects, this thesis is devoted to the development of a guidance and navigation system that allows completing an autonomous mission from the take-off to the landing phase of a rotary-wing UAV (RUAV). More specifically, this thesis is focused on the development of new strategies and algorithms that provide sufficiently accurate motion estimation during the autonomous landing on mobile platforms without using the GNSS constellations.
In one hand, for the phases of the flights where it is not required a centimetric accuracy solution, here it is proposed a new navigation approach that extends the current estimation techniques by using the EGNOS integrity information in the sensor fusion filter. This approach allows improving the accuracy of the estimation solution and the safety of the overall system, and also helps the remote pilot to have a more complete awareness of the operation status while flying the UAV
In the other hand, for those flight phases where the accuracy is a critical factor in the safety of the operation, this thesis presents a precise navigation system that allows rotary-wing UAVs to approach and land safely on moving platforms, without using GNSS at any stage of the landing maneuver, and with a centimeter-level accuracy and high level of robustness. This system implements a novel concept where the relative position and velocity between the aerial vehicle and the landing platform can be calculated from a radio-beacon system installed in both the UAV and the landing platform or through the angles of a cable that physically connects the UAV and the landing platform. The use of a cable also incorporates several extra benefits, like increasing the precision in the control of the UAV altitude. It also facilitates to center the UAV right on top of the expected landing position and increases the stability of the UAV just after contacting the landing platform.
The proposed guidance and navigation systems have been implemented in an unmanned rotorcraft and a large number of tests have been carried out under different conditions for measuring the accuracy and the robustness of the proposed solution. Results showed that the developed system allows landing with centimeter accuracy by using only local sensors and that the UAV is able to follow a mobile landing platform in multiple trajectories at different velocities
Learning Pose Estimation for UAV Autonomous Navigation and Landing Using Visual-Inertial Sensor Data
In this work, we propose a robust network-in-the-loop control system for autonomous navigation and landing of an Unmanned-Aerial-Vehicle (UAV). To estimate the UAV’s absolute pose, we develop a deep neural network (DNN) architecture for visual-inertial odometry, which provides a robust alternative to traditional methods. We first evaluate the accuracy of the estimation by comparing the prediction of our model to traditional visual-inertial approaches on the publicly available EuRoC MAV dataset. The results indicate a clear improvement in the accuracy of the pose estimation up to 25% over the baseline. Finally, we integrate the data-driven estimator in the closed-loop flight control system of Airsim, a simulator available as a plugin for Unreal Engine, and we provide simulation results for autonomous navigation and landing
Unmanned Aerial Systems for Wildland and Forest Fires
Wildfires represent an important natural risk causing economic losses, human
death and important environmental damage. In recent years, we witness an
increase in fire intensity and frequency. Research has been conducted towards
the development of dedicated solutions for wildland and forest fire assistance
and fighting. Systems were proposed for the remote detection and tracking of
fires. These systems have shown improvements in the area of efficient data
collection and fire characterization within small scale environments. However,
wildfires cover large areas making some of the proposed ground-based systems
unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial
Systems (UAS) were proposed. UAS have proven to be useful due to their
maneuverability, allowing for the implementation of remote sensing, allocation
strategies and task planning. They can provide a low-cost alternative for the
prevention, detection and real-time support of firefighting. In this paper we
review previous work related to the use of UAS in wildfires. Onboard sensor
instruments, fire perception algorithms and coordination strategies are
considered. In addition, we present some of the recent frameworks proposing the
use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more
efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at:
https://doi.org/10.3390/drones501001
UAV/UGV Autonomous Cooperation: UAV Assists UGV to Climb a Cliff by Attaching a Tether
This paper proposes a novel cooperative system for an Unmanned Aerial Vehicle
(UAV) and an Unmanned Ground Vehicle (UGV) which utilizes the UAV not only as a
flying sensor but also as a tether attachment device. Two robots are connected
with a tether, allowing the UAV to anchor the tether to a structure located at
the top of a steep terrain, impossible to reach for UGVs. Thus, enhancing the
poor traversability of the UGV by not only providing a wider range of scanning
and mapping from the air, but also by allowing the UGV to climb steep terrains
with the winding of the tether. In addition, we present an autonomous framework
for the collaborative navigation and tether attachment in an unknown
environment. The UAV employs visual inertial navigation with 3D voxel mapping
and obstacle avoidance planning. The UGV makes use of the voxel map and
generates an elevation map to execute path planning based on a traversability
analysis. Furthermore, we compared the pros and cons of possible methods for
the tether anchoring from multiple points of view. To increase the probability
of successful anchoring, we evaluated the anchoring strategy with an
experiment. Finally, the feasibility and capability of our proposed system were
demonstrated by an autonomous mission experiment in the field with an obstacle
and a cliff.Comment: 7 pages, 8 figures, accepted to 2019 International Conference on
Robotics & Automation. Video: https://youtu.be/UzTT8Ckjz1
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
Inertial Navigation for Quadrotor Using Kalman Filter with Drift Compensation
The main disadvantage of an Inertial Navigation System is a low accuracy due to noise, bias, and drift error in the inertial sensor. This research aims to develop the accelerometer and gyroscope sensor for quadrotor navigation system, bias compensation, and Zero Velocity Compensation (ZVC). Kalman Filter is designed to reduce the noise on the sensor while bias compensation and ZVC are designed to eliminate the bias and drift error in the sensor data. Test results showed the Kalman Filter design is acceptable to reduce the noise in the sensor data. Moreover, the bias compensation and ZVC can reduce the drift error due to integration process as well as improve the position estimation accuracy of the quadrotor. At the time of testing, the system provided the accuracy above 90 % when it tested indoor
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