Analysis of layering-related linear features on comet 67P/Churyumov-Gerasimenko

We analysed layering-related linear features on the surface of comet 67P/Churyumov-Gerasimenko (67P) to determine the internal configuration of the layerings within the nucleus. We used high-resolution images from the OSIRIS Narrow Angle Camera onboard the Rosetta spacecraft, projected onto the SHAP7 shape model of the nucleus, to map 171 layering-related linear features which we believe to represent terrace margins and strata heads. From these curved lineaments, extending laterally to up to 1925 m, we extrapolated the subsurface layering planes and their normals. We furthermore fitted the lineaments with concentric ellipsoidal shells, which we compared to the established shell model based on planar terrace features. Our analysis confirms that the layerings on the comet's two lobes are independent from each other. Our data is not compatible with 67P's lobes representing fragments of a much larger layered body. The geometry we determined for the layerings on both lobes supports a concentrically layered, `onion-shell' inner structure of the nucleus. For the big lobe, our results are in close agreement with the established model of a largely undisturbed, regular, concentric inner structure following a generally ellipsoidal configuration. For the small lobe, the parameters of our ellipsoidal shells differ significantly from the established model, suggesting that the internal structure of the small lobe cannot be unambiguously modelled by regular, concentric ellipsoids and could have suffered deformational or evolutional influences. A more complex model is required to represent the actual geometry of the layerings in the small lobe

Automatic inspection of aeronautical mechanical assemblies using 2D and 3D computer vision

International audienceQuality control is of key importance in the aerospace industry. This paper deals with the automatic inspection of mechanical aeronautical assemblies. For that purpose, we have developed a computer-vision-based system made of a robot equipped with two 2D cameras and a 3D scanner. The 3D CAD model of the mechanical assembly is available. It is used as a reference and it describes the assembly as it should be. The objective is to verify that the mechanical assembly conforms with the CAD model. Several types of inspection are required. For instance, we must check that the needed elements of the assembly are present, and that they have been mounted in the correct position. For this kind of inspection we use the 2D cameras and we have developed inspection solutions based on 2D image analysis. We have found that some types of inspection cannot be performed by using only 2D image analysis. A typical example of such types is detecting the interference between elements. It requires to check if two flexible elements (e.g. cables,harnesses) or a flexible and a rigid element (e.g. pipe, support) are at a safe distance from each other. For this type of situations, we use the 3D data provided by the 3D scanner and we have developed an inspection solution based on 3D point cloud analysis. We have also developed a method to compute the best viewpoints for the sensor held by the robot, in order to obtain an optimal view of each component to be inspected. The view-point selection is performed off-line (before the on-line inspection) and it exploits the CAD model of the mechanical assembly. The proposed automatic computer-vision-based inspection system has been validated in a context of industrial applications. Our software solution for 2D image analysis has been deployed on the robot platform as well as in a hand-held tablet. Since it requires a 3D sensor, our approach based on 3D point cloud has been tested in the robotic context only

Inspection d'assemblages mécaniques par une approche Deep Learning 3D : résultats préliminaires

International audienceNos travaux de recherche sont menés dans le cadre du laboratoire de recherche commun "Inspection 4.0" entre IMT Mines Albi/ICA et la société DIOTA spécialisée dans ledéveloppement d’outils numériques pour l’industrie 4.0. Dans cet article, nous nous intéressons au contrôle de conformité d’ensembles mécaniques aéronautiques complexes (typiquement un moteur d’avion en fin ou en mi- lieu de chaine d’assemblage). Un scanner 3D porté par un bras de robot permet l’acquisition de nuages de points 3D pour la phase d’inspection. Nous avons à notre disposition un modèle CAO de l’assemblage mécanique à inspecter, et c’est ce modèle qui guidera notre démarche. Nous mettons en œuvre des techniques de classification 3D par Deep Learning. Ces modèles d’apprentissage profond sont formés sur des données synthétiques et simulées, générées à partir des modèles CAO. Plusieurs approches sont proposées et des résultats sur des acquisitions réelles sont pré-sentés

Rosetta lander Philae: Flight dynamics analyses for landing site selection and post- landing operations

International audienceOn the 12th of November 2014, The Rosetta Lander Philae became the first spacecraft to softly land on a comet nucleus. Due to the double failure of the cold gas hold-down thruster and the anchoring harpoons that should have fixed Philae to the surface, it spent approximately two hours bouncing over the comet surface to finally come at rest one km away from its target site. Nevertheless it was operated during the 57 hours of its First Science Sequence. The FSS, performed with the two batteries, should have been followed by the Long Term Science Sequence but Philae was in a place not well illuminated and fell into hibernation. Yet, thanks to reducing distance to the Sun and to seasonal effect, it woke up at end of April and on 13th of June it contacted Rosetta again. To achieve this successful landing, an intense preparation work had been carried out mainly between August and November 2014 to select the targeted landing site and define the final landing trajectory. After the landing, the data collected during on-comet operations have been used to assess the final position and orientation of Philae, and to prepare the wake-up. This paper addresses the Flight Dynamics studies done in the scope of this landing preparation from Lander side, in close cooperation with the team at ESA, responsible for Rosetta, as well as for the reconstruction of the bouncing trajectory and orientation of the Lander after touchdown