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Abstract. As part of AMADEE-20, an integrated Mars analog field mission in the Negev Desert in Israel conducted by the Austrian Space Forum, an exploration cascade for the remote sensing of extraterrestrial terrain was implemented. For this purpose, aerial robots were conceptualized, which were used in an iterative process to generate a navigational map for an autonomous ground vehicle. This work presents the process for generating navigation maps using multiple aerial image sources from satellites as well as from low orbiting aerial vehicles. First, Deep Learning methods are used to analyze a high altitude aerial images of a large area, creating a basis map for mission planning and navigation. Second, high resolution unmanned aerial vehicle (UAV) images were recorded on low altitude for a pre-defined area of interest, processed with Deep-Learning and Structure from Motion and used to update the basis map. This approach results in a high accuracy navigation map for autonomous, off-road robot navigation. Experiments during the AMADEE-20 mission in the Israeli Negev Desert validated the proposed methods by sending an autonomous ground vehicle through the environment using the generated map.

Long-term autonomy of robotic systems implicitly requires dependable platforms that are able to naturally handle hardware and software faults, problems in behaviors, or lack of knowledge. Modelbased dependable platforms additionally require the application of rigorous methodologies during the system development, including the use of correct-by-construction techniques to implement robot behaviors. As the level of autonomy in robots increases, so do the cost of offering guarantees about the dependability of the system. Certifiable dependability of autonomous robots, we argue, can benefit from formal models of the integration of several cognitive functions, knowledge processing, reasoning, and meta-reasoning. Here, we put forward the case for a generative model of cognitive architectures for autonomous robotic agents that subscribes to the principles of model-based engineering and certifiable dependability, autonomic computing, and knowledge-enabled robotics.

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