arXiv subjects

Christophe De Wagter

Publications and source records attributed to Christophe De Wagter.

26 records · Page 2Linked to original sources

arXiv2020

The NederDrone: A hybrid lift, hybrid energy hydrogen UAV

A lot of UAV applications require vertical take-off and landing (VTOL) combined with very long-range or endurance. Transitioning UAVs have been proposed to combine the VTOL capabilities of helicopters with the efficient long-range flight properties of fixed-wing aircraft. But energy is still a bottleneck for many electric long endurance applications. While solar power technology and battery technology have improved a lot, in rougher conditions they still respectively lack the power or total amount of energy required for many real-world situations. In this paper, we introduce the NederDrone, a hybrid lift, hybrid energy hydrogen-powered UAV which can perform vertical take-off and landings using 12 propellers while flying efficiently in forward flight thanks to its fixed wings. The energy is supplied from a mix of hydrogen-driven fuel-cells to store large amounts of energy and battery power for high power situations. The hydrogen is stored in a pressurized cylinder around which the UAV is optimized. This paper analyses the selection of the concept, the implemented safety elements, the electronics and flight control and shows flight data including a 3h38 flight at sea, starting and landing on a small moving ship.

cs.RO↗

arXiv2019

Visual Model-predictive Localization for Computationally Efficient Autonomous Racing of a 72-gram Drone

Drone racing is becoming a popular e-sport all over the world, and beating the best human drone race pilots has quickly become a new major challenge for artificial intelligence and robotics. In this paper, we propose a strategy for autonomous drone racing which is computationally more efficient than navigation methods like visual inertial odometry and simultaneous localization and mapping. This fast light-weight vision-based navigation algorithm estimates the position of the drone by fusing race gate detections with model dynamics predictions. Theoretical analysis and simulation results show the clear advantage compared to Kalman filtering when dealing with the relatively low frequency visual updates and occasional large outliers that occur in fast drone racing. Flight tests are performed on a tiny racing quadrotor named "Trashcan", which was equipped with a Jevois smart-camera for a total of 72g. The test track consists of 3 laps around a 4-gate racing track. The gates spaced 4 meters apart and can be displaced from their supposed position. An average speed of 2m/s is achieved while the maximum speed is 2.6m/s. To the best of our knowledge, this flying platform is the smallest autonomous racing drone in the world and is 6 times lighter than the existing lightest autonomous racing drone setup (420g), while still being one of the fastest autonomous racing drones in the world.

cs.RO↗

arXiv2019

Aggressive Online Control of a Quadrotor via Deep Network Representations of Optimality Principles

Optimal control holds great potential to improve a variety of robotic applications. The application of optimal control on-board limited platforms has been severely hindered by the large computational requirements of current state of the art implementations. In this work, we make use of a deep neural network to directly map the robot states to control actions. The network is trained offline to imitate the optimal control computed by a time consuming direct nonlinear method. A mixture of time optimality and power optimality is considered with a continuation parameter used to select the predominance of each objective. We apply our networks (termed G\&CNets) to aggressive quadrotor control, first in simulation and then in the real world. We give insight into the factors that influence the `reality gap' between the quadrotor model used by the offline optimal control method and the real quadrotor. Furthermore, we explain how we set up the model and the control structure on-board of the real quadrotor to successfully close this gap and perform time-optimal maneuvers in the real world. Finally, G\&CNet's performance is compared to state-of-the-art differential-flatness-based optimal control methods. We show, in the experiments, that G\&CNets lead to significantly faster trajectory execution due to, in part, the less restrictive nature of the allowed state-to-input mappings.

cs.RO↗

arXiv2018

Learning what is above and what is below: horizon approach to monocular obstacle detection

A novel approach is proposed for monocular obstacle detection, which relies on self-supervised learning to discriminate everything above the horizon line from everything below. Obstacles on the path of a robot that keeps moving at the same height, will appear both above and under the horizon line. This implies that classifying obstacle pixels will be inherently uncertain. Hence, in the proposed approach the classifier's uncertainty is used for obstacle detection. The (preliminary) results show that this approach can indeed work in different environments. On the well-known KITTI data set, the self-supervised learning scheme clearly segments the road and sky, while application to a flying data set leads to the segmentation of the flight arena's floor.

cs.RO↗

arXiv2017

Efficient Optical flow and Stereo Vision for Velocity Estimation and Obstacle Avoidance on an Autonomous Pocket Drone

Miniature Micro Aerial Vehicles (MAV) are very suitable for flying in indoor environments, but autonomous navigation is challenging due to their strict hardware limitations. This paper presents a highly efficient computer vision algorithm called Edge-FS for the determination of velocity and depth. It runs at 20 Hz on a 4 g stereo camera with an embedded STM32F4 microprocessor (168 MHz, 192 kB) and uses feature histograms to calculate optical flow and stereo disparity. The stereo-based distance estimates are used to scale the optical flow in order to retrieve the drone's velocity. The velocity and depth measurements are used for fully autonomous flight of a 40 g pocket drone only relying on on-board sensors. The method allows the MAV to control its velocity and avoid obstacles.

cs.RO↗

arXiv2017

Design, Control and Visual Navigation of the DelftaCopter

To participate in the Outback Medical Express UAV Challenge 2016, a vehicle was designed and tested that can hover precisely, take-off and land vertically, fly fast forward efficiently and use computer vision to locate a person and a suitable landing location. A rotor blade was designed that can deliver sufficient thrust in hover, while still being efficient in fast forward flight. Energy measurements and windtunnel tests were performed. A rotor-head and corresponding control algorithms were developed to allow transitioning flight with the non-conventional rotor dynamics. Dedicated electronics were designed that meet vehicle needs and regulations to allow safe flight beyond visual line of sight. Vision based search and guidance algorithms were developed and tested. Flight tests and a competition participation illustrate the applicability of the DelftaCopter concept.

cs.RO↗

arXiv2017

Obstacle Avoidance Strategy using Onboard Stereo Vision on a Flapping Wing MAV

The development of autonomous lightweight MAVs, capable of navigating in unknown indoor environments, is one of the major challenges in robotics. The complexity of this challenge comes from constraints on weight and power consumption of onboard sensing and processing devices. In this paper we propose the "Droplet" strategy, an avoidance strategy based on stereo vision inputs that outperforms reactive avoidance strategies by allowing constant speed maneuvers while being computationally extremely efficient, and which does not need to store previous images or maps. The strategy deals with nonholonomic motion constraints of most fixed and flapping wing platforms, and with the limited field-of-view of stereo camera systems. It guarantees obstacle-free flight in the absence of sensor and motor noise. We first analyze the strategy in simulation, and then show its robustness in real-world conditions by implementing it on a 20-gram flapping wing MAV.

cs.RO↗

arXiv2016

First free-flight flow visualisation of a flapping-wing robot

Flow visualisations are essential to better understand the unsteady aerodynamics of flapping wing flight. The issues inherent to animal experiments, such as poor controllability and unnatural flapping when tethered, can be avoided by using robotic flyers. Such an approach holds a promise for a more systematic and repeatable methodology for flow visualisation, through a better controlled flight. Such experiments require high precision position control, however, and until now this was not possible due to the challenging flight dynamics and payload restrictions of flapping wing Micro Air Vehicles (FWMAV). Here, we present a new FWMAV-specific control approach that, by employing an external motion tracking system, achieved autonomous wind tunnel flight with a maximum root-mean-square position error of 28 mm at low speeds (0.8 - 1.2 m/s) and 75 mm at high speeds (2 - 2.4 m/s). This allowed the first free-flight flow visualisation experiments to be conducted with an FWMAV. Time-resolved stereoscopic Particle Image Velocimetry (PIV) was used to reconstruct the 3D flow patterns of the FWMAV wake. A good qualitative match was found in comparison to a tethered configuration at similar conditions, suggesting that the obtained free-flight measurements are reliable and meaningful.

physics.bio-ph↗