The latest IPCC 1 report shows that the aviation industry is responsible for around 2% of greenhouse gas emissions; this is lower than emissions from many other sectors, but still equivalent to the total emissions of a European country like Germany.Following the recommendations of the International Civil Aviation Organization (ICAO 2 ) and its long-term global aspirational goal (LTAG), the aeronautics industry has come together under the Air Transport Aviation Group (ATAG 3 ) to converge towards zero greenhouse gas emissions by 2050, such as CO2 emissions and other radiative effects such as those generated by condensation trails.To achieve this goal, we have a number of levers at our disposal: technical improvements to our engines and aircrafts, the use of new sustainable fuels, and the use of data now accessible thanks to new engineering 4.0 technologies.This document first presents the data we now have at our disposal.The second section briefly recalls the opportunities offered by new renewable fuels.Finally, we present some digital approaches and conclude with details of three central themes illustrated by the contributions to this special session. About data in the aeronautic domainAeronautical data is incredibly diverse, as it encompasses various measurements from onboard systems and critical supplementary information.Whether monitoring an aircraft's behavior, managing an airline, or overseeing an airport, understanding this data is essential.It includes not only data collected directly from embedded systems, but also crucial details related to weather, maintenance, safety, fuel consumption, design, development, and industrialization processesranging from the large 115000 lbf 4 thrust commercial engines for the Boeing 777 airliner to the smallest sensors. Onboard MeasurementsWhen thinking about aeronautical data, the first thing that comes to mind is the measurements acquired during flights.These measurements are typically taken at frequencies ranging from 1 to 100 Hz, and in some cases, at several tens of kHz for data 1 IPCC -Intergovernmental Panel on Climate Change (https://www.ipcc.ch/)-(GIEC in French). 2 ICAO -LTAG -https://www.icao.int/environmental-protection/Pages/LTAG.aspx.3 ATAG -Air Transport Aviation Group (https://atag.org).4 lbf (pounds) 1 lbf ~ 4.

Passive target estimation is a widely investigated problem of practical interest. We are concerned specifically with an autonomous flight system developed onboard the ONERA ReSSAC unmanned helicopter. This helicopter is equipped with a (visible or infrared) camera and so is able to measure azimuths and elevation angles of a target. The latter is supposed to follow a constant velocity motion. It is well known that observer must maneuver in order to insure the observability of the target state. We are interested in tracking partly the target state when both the observer and the target have a constant velocity model in a three-dimensional space. We describe the set of all the trajectories compatible with the angle measurements and we propose a quick method to estimate these trajectories.

The 6-pixel low-speed Visual Motion Sensor (VMS) inspired by insects' visual systems presented here performs local 1-D angular speed measurements ranging from 1.5°/s to 25°/s and weighs only 2.8 g. The entire optic flow processing system, including the spatial and temporal filtering stages, has been updated with respect to the original design. This new lightweight sensor was tested under free-flying outdoor conditions over various fields onboard a 80 kg unmanned helicopter called ReSSAC. The visual disturbances encountered included helicopter vibrations, uncontrolled illuminance, trees, roads, and houses. The optic flow measurements obtained were finely analyzed online and also offline, using the sensors of various kinds mounted onboard ReSSAC. The results show that the optic flow measured despite the complex disturbances encountered closely matched the approximate ground-truth optic flow.

We introduce TiMDPpoly, an algorithm designed to solve planning problems with durative actions, under probabilistic uncertainty, in a non-stationary, continuous-time context. Mission planning for autonomous agents such as planetary rovers or unmanned aircrafts often correspond to such time-dependent planning problems. Modeling these problems can be cast through the framework of time-dependent Markov decision processes (TiMDPs). We analyze the TiMDP optimality equations in order to exploit their properties. Then, we focus on the class of piecewise polynomial models in order to approximate TiMDPs, and introduce several algorithmic contributions which lead to the TiMDPpolyalgorithm for TiMDPs. Finally, our approach is evaluated on an unmanned aircraft mission planning problem and on an adapted version of the well-known Mars rover domain.

Time is a crucial variable in planning and often requires special attention since it introduces a specific structure along with additional complexity, especially in the case of decision under uncertainty. In this paper, after reviewing and comparing MDP frameworks designed to deal with temporal problems, we focus on Generalized Semi-Markov Decision Processes (GSMDP) with observable time. We highlight the inherent structure and complexity of these problems and present the differences with classical reinforcement learning problems. Finally, we introduce a new simulation-based reinforcement learning method for solving GSMDP, bringing together results from simulation-based policy iteration, regression techniques and simulation theory. We illustrate our approach on a subway network control example.

An autonomous system uses sensors in order to update an uncertain representation of its environment, which must remain well-grounded with regards to the risks and the constraints of its mission. The choice of a framework for the representation of beliefs which is adapted to deal with both time and uncertainty is a crucial point. A perception strategy should be founded on an evaluation of the relevance of the beliefs of the system in order to collect the most useful information at the most opportune time. This paper addresses the problem of taking time and ignorance into account in the representation of the uncertain beliefs of the system about the world. The paper proposes a well-adapted approach to the design of a perception strategy for an autonomous surveillance system in a changing environment together with initial results of simulations.