Gautier Hattenberger

A ‘No-Flow-Sensor’ Wind Estimation Algorithm for Unmanned Aerial Systems:

A ‘no-flow-sensor’ wind estimation algorithm for Unmanned Aerial Systems (UAS) is presented. It is based on ground speed and flight path azimuth information from the autopilots GPS system. The algorithm has been tested with the help of the simulation option in the Paparazzi autopilot software using artificial wind profiles. The retrieval accuracy of the predefined profiles by the wind algorithm and its sensitivity to vertical aircraft velocity, diameter of the helical flight pattern and different data sampling methods have been investigated. The algorithm with a correspondingly optimized set of parameters is then applied to various scientific flight missions under real wind conditions performed by the UAS SUMO (Small Unmanned Meteorological Observer). The SUMO wind profiles are compared to measurements of conventional atmospheric profiling systems as radiosondes and piloted balloons. In general, the presented ‘no-flow-sensor’ wind estimation method performs well in most atmospheric situations and is now operationally used in the post-processing routine for wind profile determination from SUMO measurements.

Formation flight: evaluation of autonomous configuration control algorithms

In military missions in hostile environments involving teams of UAVs flying in formation, it is important to get the maximum benefits of the auto-protection systems of each aircraft to enhance the global security and efficiency of the team. One way to achieve this is to select a proper configuration for the formation. In this paper, we present an approach to autonomously adapt the configuration of a formation and we focus on its evaluation within a realistic framework where each UAV is simulated independently and communicate through a network.

Development of a Long Endurance Mini-UAV: ETERNITY

This study presents the effort given for the first prototype of a Long Endurance Mini UAV concept called Eternity. A multi-disciplinary conceptual aircraft design program called CDSGN is developed and used for the design of the Eternity. Unlike the traditional design methods that uses statistical data from the previous well-flown aircrafts, CDSGN analyses numerous aircraft candidates and simulates each candidate for the given mission definition and outputs the corresponding performance. The unique property of the presented design methodology comes from a computationally fast and physically accurate modelling of the aerodynamic characteristics of each candidate by using a modified version of a vortex lattice program called AVL from Mark Drela. Two types of configurations have been analysed for the Eternity design, conventional and flying-wing. A wide envelope of variable design parameters used for both configurations such as wing surface area, cruise speed, battery capacity, different airfoils, etc… Integration of solar cells, and the management of solar energy is also considered for every candidate. Only the wing span size has been limited to one meter. Additionally, the on-board avionics and payload weights and sizes are fixed for every candidate as they are independent of the design. Analyses by CDSGN concluded the dominance of the conventional configuration for the given long endurance mission performance both on solar and non-solar conditions. Optimum wing surface area and the on-board battery energy found interactively by a post-filtering program developed in-house. A custom airfoil family, transitioning along the span, have been designed specifically for the corresponding local Reynolds number for specific spanwise locations. A wind-tunnel campaign is performed with a full-scale model and first flight tests have been performed in order to show the feasibility of long endurance flights.

Nonlinear state estimation using an invariant unscented Kalman filter

In this paper, we proposed a novel approach for nonlinear state estimation, named π-IUKF (Invariant Unscented Kalman Filter), which is based on both invariant filter estimation and UKF theoretical principles. Several research works on nonlinear invariant observers have been led and provide a geometrical-based constructive method for designing filters dedicated to nonlinear state estimation problems while preserving the physical properties and system symmetries. The general invariant observer guarantees a straight-forward form of the nonlinear estimation error dynamics whose properties are remarkable. The developed π-IUKF estimator suggests a systematic approach to determine all the symmetry-preserving correction terms, associated with a nonlinear state-space representation used for prediction, without requiring any linearization of the differential equations. The exploitation of the UKF principles within the invariant framework has required the definition of a compatibility condition on the observation equations. As a first result, the estimated covariance matrices of the π-IUKF converge to constant values due to the symmetry-preserving property provided by the nonlinear invariant estimation theory. The designed π-IUKF method has been successfully applied to some relevant practical problems such as the estimation of Attitude and Heading for aerial vehicles using low-cost AH reference systems (i.e., inertial/magnetic sensors characterized by low performances).

Guidance algorithm for smooth trajectory tracking of a fixed wing UAV flying in wind flows

This paper presents an algorithm for solving the problem of tracking smooth curves by a fixed wing unmanned aerial vehicle travelling with a constant airspeed and under a constant wind disturbance. The algorithm is based on the idea of following a guiding vector field which is constructed from the implicit function that describes the desired (possibly time-varying) trajectory. The output of the algorithm can be directly expressed in terms of the bank angle of the UAV in order to achieve coordinated turns. Furthermore, the algorithm can be tuned offline such that physical constraints of the UAV, e.g. the maximum bank angle, will not be violated in a neighborhood of the desired trajectory. We provide the corresponding theoretical convergence analysis and performance results from actual flights.

Design of A High-Performance Tailless MAV Through Planform Optimization

The paper presents the development of a high-performance tailless micro air vehicle. Design is optimized via a conceptual design optimizer according to the requirements of an atmospheric-research flight mission. Main focus is on the elevator control surface configuration that generates a lifting force which is placed in front of the center of gravity without requiring a high aspect ratio thanks to the particular wing planform shape. As a result, the longitudinal maneuvers are performed more efficiently such as, pitch-up maneuver requires a positive flap deflection which is also beneficial for the local airfoil’s maximum lift coefficient. This distinguishing feature of the design allows to design the wing surface more efficiently for maximum range and endurance performance speeds without too much compromising from the take-off and landing performance. Additionally, the angle of attack envelope of the aircraft is significantly reduced with the use of positive local camber change that was necessary for the selected mission requirements. Flight tests of the two prototypes and a final version is presented including the comparison between the expected theoretical results and the real-flight results. The resultant FENIX UAV enlarges the flight envelope of a compact tailless vehicle with hand-launch, low-speed flight, and more than 90minutes of endurance capabilities.

Pi-Invariant Unscented Kalman Filter for sensor fusion

A novel approach based on Unscented Kalman Filter (UKF) is proposed for nonlinear state estimation. The Invariant UKF, named π-IUKF, is a recently introduced algorithm dedicated to nonlinear systems possessing symmetries as illustrated by the quaternion-based mini Remotely Piloted Aircraft System (RPAS) kinematics modeling considered in this paper. Within an invariant framework, this algorithm suggests a systematic approach to determine all the symmetry-preserving terms which correct accordingly the nonlinear state-space representation used for prediction, without requiring any linearization. Thus, based on both invariant filters, for which Lie groups have been identified and UKF theoretical principles, the developed π-IUKF has been previously and successfully applied to the mini-RPAS attitude estimation problem, highlighting remarkable invariant properties. We propose in this paper to extend the theoretical background and the applicability of our proposed π-IUKF observer to the case of a mini-RPAS equipped with an aided Inertial Navigation System (INS) which leads to augment the nonlinear state space representation with both velocity and position differential equations. All the measurements are provided on board by a set of low-cost and low-performance sensors (accelerometers, gyrometers, magnetometers, barometer and even Global Positioning System (GPS)). Our designed π-IUKF estimation algorithm is described in this paper and its performances are evaluated by exploiting successfully real flight test data. Indeed, the whole approach has been implemented onboard using a data logger based on the well-known Paparazzi system. The results show promising perspectives and demonstrate that nonlinear state estimation converges on a much bigger set of trajectories than for more traditional approaches

Learning to combine multi-sensor information for context dependent state estimation

The fusion of multi-sensor information for state estimation is a well studied problem in robotics. However, the classical methods may fail to take into account the measurements validity, therefore ruining the benefits of sensor redundancy. This work addresses this problem by learning context-dependent knowledge about sensor reliability. This knowledge is later used as a decision rule in the fusion task in order to dynamically select the most appropriate subset of sensors. For this purpose we use the Mixture of Experts framework. In our application, each expert is a Kalman filter fed by a subset of sensors, and a gating network serves as a mediator between individual filters, basing its decision on sensor inputs and contextual information to reason about the operation context. The performance of this model is evaluated for altitude estimation of a UAV.

AUTONOMOUS CONFIGURATION CONTROL FOR UAV FORMATION FLIGHT IN HOSTILE ENVIRONMENTS

A fleet of UAVs flying according a planned mission in hostile environments must optimize its configuration, so that the UAVs auto-protection systems ensure the fleet safety. Such an optimization can be done in the mission planning phase, and must also be reactively updated to tackle unpredicted threats. In this paper, we present an approach to the problem of selecting autonomously the fleet configuration. Algorithms that explicitly consider models of the threats and of the countermeasures systems are presented, and integrated within a global decisional architecture that ensures reactiveness and constraints satisfactions.

Aerodynamic Characterization of an Off-the-Shelf Aircraft via Flight Test and Numerical Simulation

Characterization of an off-the-shelf small tailless aircraft with a wing span of 1:3m is presented. Mentioned aircraft is being used in several scientific measurement projects by authors, whereas the ight performance and quality such as endurance, and stability plays an important role on the measurement quality. Hence the main objective is to use the extracted and fine tuned aerodynamic and ight performance characteristics of the aircraft for a better flight control and mission planning during simulations and real flights. Aerodynamic characteristics are obtained through flight tests, numerical analyses, and some isolated ground experiments for the propulsion system. The comparison of different measurement and estimation techniques are discussed.