Efficient Autonomous Navigation for Planetary Rovers with Limited Resources

Rovers operating on Mars are in need of more and more autonomous features to ful ll their challenging mission requirements. However, the inherent constraints of space systems make the implementation of complex algorithms an expensive and difficult task. In this paper we propose a control architecture for autonomous navigation. Efficient implementations of autonomous features are built on top of the current ExoMars navigation method, enhancing the safety and traversing capabilities of the rover. These features allow the rover to detect and avoid hazards and perform long traverses by following a roughly safe path planned by operators on ground. The control architecture implementing the proposed navigation mode has been tested during a field test campaign on a planetary analogue terrain. The experiments evaluated the proposed approach, autonomously completing two long traverses while avoiding hazards. The approach only relies on the optical Localization Cameras stereobench, a sensor that is found in all rovers launched so far, and potentially allows for computationally inexpensive long-range autonomous navigation in terrains of medium difficulty

Ultrathin mono-resonant nano photovoltaic device for broadband solar conversion

International audienceNano-resonators can be used in photovoltaics to drastically improve the ability of the device to absorb light and generate photo-carriers, therefore enabling a reduction of the absorber volume. Conventionally, the harvest of the spectrally broad solar spectrum is achieved via the tedious engineering of multiple optical resonances. In this paper, we propose a breakthrough approach, which consists in reducing the solar spectral range with a spectral conversion layer to match only one resonance that can then be easily designed. We use a Maxwell solver and a ray-tracing code to optimize the nano-resonator and its spectral converter. We show that 66.2% optical efficiency can be theoretically achieved in less than 40 nm mean thick absorber while leading to device design enabling collection of photo-generated carriers

NOAH-H, a deep-learning, terrain classification system for Mars: Results for the ExoMars Rover candidate landing sites

In this investigation a deep learning terrain classification system, the “Novelty or Anomaly Hunter – HiRISE” (NOAH-H), was used to classify High Resolution Imaging Science Experiment (HiRISE) images of Oxia Planum and Mawrth Vallis. A set of ontological classes was developed that covered the variety of surface textures and aeolian bedforms present at both sites. Labelled type-examples of these classes were used to train a Deep Neural Network (DNN) to perform semantic segmentation in order to identify these classes in further HiRISE images. This contribution discusses the methods and results of the study from a geomorphologists perspective, providing a case study applying machine learning to a landscape classification task. Our aim is to highlight considerations about how to compile training datasets, select ontological classes, and understand what such systems can and cannot do. We highlight issues that arise when adapting a traditional planetary mapping workflow to the production of training data. We discuss both the pixel scale accuracy of the model, and how qualitative factors can influence the reliability and usability of the output. We conclude that “landscape level” reliability is critical for the use of the output raster by humans. The output can often be more useful than pixel scale accuracy statistics would suggest, however the product must be treated with caution, and not considered a final arbiter of geological origin. A good understanding of how and why the model classifies different landscape features is vital to interpreting it reliably. When used appropriately the classified raster provides a good indication of the prevalence and distribution of different terrain types, and informs our understanding of the study areas. We thus conclude that it is fit for purpose, and suitable for use in further work

Croissance et caractérisation de films de YF3 dopés terre rares par MOCVD en vue d’application pour la conversion du spectre solaire

National audienc

A solar spectrum up-conversion layer, studies of the MOCVD growth and luminescent properties of Er/Yb doped YF3 thin films

International audienc

Remote Rover Operations: Testing the Exomars Egress Case

This paper presents the results of the remote rover operations tests run on the 27-29th of October 2015 focused on the ExoMars egress manoeuvre scenario. A total of 5 differently challenging scenarios were tested in order to evaluate the capabilities of the operators with regards to the proper understanding of the criticality of each case that would allow them to make a sound decision on which egress direction to take. These experiments showed the usability of simulation tools 3DROCS&3DROV for acquiring the situational awareness needed for this purpose and the importance of planning and establishing the rules and conditions that enable the decision making process

Croissance et caractérisation de films minces luminescents dopés terres rares par MOCVD en vue d’application pour la conversion du spectre solaire"

National audienc

Oxia Planum, Mars, classified using the NOAH-H deep-learning terrain classification system

ABSTRACTWe present a map of Oxia Planum, Mars, the landing site for the ExoMars Rover. This shows surface texture and aeolian bedform distribution, classified using a deep learning (DL) system. A hierarchical classification scheme was developed, categorising the surface textures observed at the site. This was then used to train a DL network, the ‘Novelty or Anomaly Hunter – HiRISE’ (NOAH-H). The DL applied the classification scheme across a wider area than could have been mapped manually. The result showed strong agreement with human-mapped areas reserved for validation. The resulting product is presented in two ways, representing the two principle levels of the classification scheme. ‘Descriptive classes’ are purely textural in nature, making them compatible with a machine learning approach. These are then combined into ‘interpretive groups’, broader thematic classes, which provide an interpretation of the landscape. This step allows for a more intuitive analysis of the results by human users