Institut supérieur de l'aéronautique et de l'espace

About: Institut supérieur de l'aéronautique et de l'espace is a based out in . It is known for research contribution in the topics: Mars Exploration Program & Seismometer. The organization has 445 authors who have published 644 publications receiving 8049 citations. The organization is also known as: ISAE SUPAERO & National Higher French Institute of Aeronautics and Space.

A vision-based Flight Guidance and Navigation System for autonomous cross-country soaring UAVs

Abstract: In this work, a vision-based Flight Guidance and Navigation System is developed for providing cross-country soaring functionality to a small UAV glider. The overall system is based on existing major contributions to autonomous soaring while still missing components have been designed. A cumulus-cloud mapping algorithm was extended to not only estimate the position of cumuli but also their drift velocity and their updraft strength both factors with a significant impact on cross-country soaring performances. A guidance law was developed to navigate the vehicle during final glide from the last visited cloud to the destination given flight performance, mission and wind constraints. A path planning algorithm is proposed to then guide the vehicle in minimum time to its destination. Simulations were conducted in a nonlinear 6-DOF simulator for a small UAV glider to demonstrate the benefits of the system.

Refinement of AADL models using early-stage analysis methods : An avionics example

Abstract: Model-Driven Engineering (MDE) is a relevant approach to support the engineering of distributed embedded systems with performance and dependability constraints. MDE involves models definitions and transformations to cover most of the system life-cycle: design, implementation and Verification & Validation activities towards system qualification. Still, few works evaluate the early integration of performance evaluation based on architectural models. In this report, we investigate the earlystage use of analysis in AADL modeling. Precisely, we exemplify on an avionics case study how to dimension the data flows for an application distributed over an AFDX network. Based on the insight from this study, we suggest a simple framework and associated techniques to efficiently support analysis activities in the early-stage design phases.

Opening a new window to other worlds with spectropolarimetry

Abstract: A high level of diversity has already been observed among the planets of our own Solar System. As such, one expects extrasolar planets to present a wide range of distinctive features, therefore the characterisation of Earth- and super Earth-like planets is becoming of key importance in scientific research. The Search (Spectropolarimetric Exoplanet AtmospheRe CHaracerisation) mission proposal of this paper represents one possible approach to realising these objectives. The mission goals of Search include the detailed characterisation of a wide variety of exoplanets, ranging from terrestrial planets to gas giants. More specifically, Search will determine atmospheric properties such as cloud coverage, surface pressure and atmospheric composition, and may also be capable of identifying basic surface features. To resolve a planet with a semi major axis of down to 1.4 AU and 30 pc distant Search will have a mirror system consisting of two segments, with elliptical rim, cut out of a parabolic mirror. This will yield an effective diameter of 9 m along one axis. A phase mask coronagraph along with an integral spectrograph will be used to overcome the contrast ratio of star to planet light. Such a mission would provide invaluable data on the diversity present in extrasolar planetary systems and much more could be learned from the similarities and differences compared to our own Solar System. This would allow our theories of planetary formation, atmospheric accretion and evolution to be tested, and our understanding of regions such as the outer limit of the Habitable Zone to be further improved.

Similarity maximization of a scaled aeroelastic flight demonstrator via multidisciplinary optimization

Abstract: The developments presented in this paper take place in the context of a broader series of works carried out at ONERA and ISAE-SUPAERO on multidisciplinary design optimization applied to a scaled flight demonstrator. The aim of this work is to develop an optimization process capable of sizing a scaled flight demonstrator in order to reproduce several behaviors en- countered on its corresponding full size aircraft. Unlike the classical optimization problems found in aeronautics, whose objective functions are performance-related (e.g. mass and drag minimization), we aim to maximize the similarity between the scaled model and the full size aircraft. In the aforementioned context, the first part of this paper corresponds to the static aeroelastic similarity problem. However, the approach described herein is general enough to treat other optimization problems, including performance-related ones. The second part of this work deals with the dynamic aspects of the aeroelastic similar- ity. A benchmark case is presented where the structural properties of a given geometry are optimized in order to match the reference modal parameters (i.e., mode shapes and frequencies) of the GARTEUR SM-AG19 model.

Fleets of enduring drones to probe atmospheric phenomena with clouds

Abstract: A full spatio-temporal four-dimensional characterization of the microphysics and dynamics of cloud formation including the onset of precipitation has never been reached. Such a characterization would yield a better understanding of clouds, e.g. to assess the dominant mixing mechanism and the main source of cloudy updraft dilution. It is the sampling strategy that matters: fully characterizing the evolution over time of the various parameters (P, T, 3D wind, liquid water content, aerosols...) within a cloud volume requires dense spatial sampling for durations of the order of one hour. A fleet of autonomous lightweight UAVs that coordinate themselves in real-time as an intelligent network can fulfill this purpose. The SkyScanner project targets the development of a fleet of autonomous UAVs to adaptively sample cumuli, so as to provide relevant data to address long standing questions in atmospheric science. It mixes basic researches and experimental developments, and gathers scientists in UAV conception, in optimal flight control, in intelligent cooperative behaviors, and of course atmospheric scientists. Two directions of researches are explored: optimal UAV conception and control, and optimal control of a fleet of UAVs. The design of UAVs for atmospheric science involves the satisfaction of trade-offs between payload, endurance, ease of deployment... A rational conception scheme that integrates the constraints to optimize a series of criteria, in particular energy consumption, would yield the definition of efficient UAVs. This requires a fine modeling of each involved sub-system and phenomenon, from the motor/propeller efficiency to the aerodynamics at small scale, including the flight control algorithms. The definition of mission profiles is also essential, considering the aerodynamics of clouds, to allow energy harvesting schemes that exploit thermals or gusts. The conception also integrates specific sensors, in particular wind sensor, for which classic technologies are challenged at the low speeds of lightweight UAVs. The overall control of the fleet so as to gather series of synchronized data in the cloud volume is a poorly informed and highly constrained adaptive sampling problem, in which the UAV motions must be defined to maximize the amount of gathered information and the mission duration. The overall approach casts the problem in a hierarchy of two modeling and decision stages. A macroscopic parametrized model of the cloud is built from the gathered data and exploited at the higher level by an operator, who sets information gathering goals. A subset of the UAV fleet is allocated to each goal, considering the current fleet state. These high level goals are handled by the lower level, which autonomously optimizes the selected UAVs trajectories using an on-line updated dense model of the variables of interest. Building the models involves Gaussian processes techniques (kriging) to fuse the gathered data with a generic cumulus conceptual model, the latter being defined from thorough statistics on realistic MesoNH cloud simulations. The model is exploited by a planner to generate trajectories that minimize the uncertainty in the map, while steering the vehicles within the air flows to save energy.