Many successful landings have been performed on celestial bodies such as Mars, the Moon, Venus and others. All of these had in common that they were designed such that they had to land in regions, which were supposedly free of any hazards or that a certain level of risk was accepted. However, while rocks and other geological features are nightmares of any landing engineer they are the dream targets of scientists. Therefore, currently landing-site selection is a trade-off between the scientists’ wishes and the engineers’ fears. To bring the engineering capabilities closer to what the scientists desire, landing capabilities need to be advanced. Therefore, this work tries to answer the research question: Are autonomous safe landings in hazardous and potentially unknown environments possible? which lead to the following two sub-questions: 1. How can a landing vehicle autonomously assess the safety of a potentially unknown and unmapped landing site? 2. Howcan a landing vehicle ensure a safe touch downavoiding autonomously detected hazards? @en

Hazard-detection and avoidance systems will become an important asset for next-generation landing and exploration missions. To date, multiple studies into these systems were conducted to develop the methods needed and to demonstrate their performance in mainly software-based tests. Few studies were able to demonstrate the performance of the algorithm in hardware-in-the-loop tests, as these are usually difficult to set up and expensive to execute. In this paper, the hardware-in-the-loop testing of a stereo-vision based hazarddetection algorithm is presented. It was performed with the Testbed for Robotic Optical Navigation (TRON) at the German Aerospace Center (DLR) in Bremen, Germany. Since this testbed only allows for testing in a scaled environment, one of the challenging tasks was to design a scaled-down test set-up to represent a real-life lunar descent. The hardwarein- the-loop testing confirmed the results obtained during the earlier software-in-the-loop testing, that stereo vision can successfully be used for hazard detection during planetary descent.@en

Precise landings on other bodies require more than just dead reckoning using an inertial measurement unit on-board the lander. If navigation of the lander with respect to a planetary surface is desired, so-called crater detection and crater-matching algorithms might be a valuable asset to find the inertial position of the vehicle using terrain relative navigation techniques. This would enable landing close to an inertially defined landing site, which could, for example, be a surface asset of a previous mission. With the desire to reduce the landing ellipse size, more precise knowledge of the inertial state of the lander is required. Based on an extensive literature review, six different algorithms were implemented to assess the performance of these. This assessment will aid the selection of crater-detection techniques for future precision landing missions. To compare the different algorithms trade-off criteria have been established. The following criteria are assessed: 1) True detection rates 2) False detection rates 3) Accuracy: as the reference maps usually have rather high resolution, inaccuracies of just a few pixels can cause large errors. 4) Run-time: the algorithm should be on-board capable. Moreover, the robustness of the algorithms was investigated. It was found that all algorithms are capable of performing the task of extracting sufficient craters for localising the landing vehicle with respect to a surface map. A method based on extracting and clustering lit pixels delivered the most promising results for the overall detections, whereas, the machine-learning based algorithms showed slightly better robustness.@en

Landing autonomously in hazardous environments is a very likely scenario for future exploration missions. Landing in hazardous but scientifically interesting sites on Mars or the Moon, returning to the surface of Venus or a landing on Europa are just a few examples for missions where hazards might be encountered during landing. These missions will need the ability to sense surface hazards, to select a safe landing site, and to avoid the detected hazards during touchdown. This will require autonomous landing site evaluation, but also more accurate navigation capabilities than the current state-of-the-art. Building upon our previous developments in the field of hazard detection and landing site evaluation, we developed a navigation filter capable of limiting the relative error with respect to detected features such as hazards and relative to selected points such as a safe landing site in the surface plane, as well as reducing any absolute navigation error accumulated in the altitude measurements. Using an error-state Kalman filter and measurements based on images and surface DEMs obtained from a hazard-detection method, we were able to greatly improve both landing accuracy and landing precision with respect to the current state of the art. With our filter we are able to reduce the hazard relative landing ellipse size by a factor of 3, while also reducing the ranging error to the surface by almost 99% thus enabling accurate altitude estimation during the descent. The developed method proofed to be robust with less than 1% of outliers created. Performing a hardware-in-the-loop test at the TRON facility at DLR Bremen concluded the work. The results of the test verified the results from the software-in-the-loop testing. This shows that hazard relative navigation techniques are a good candidate to enable a new class of exploration missions, capable of autonomous landing in unsafe and potentially even unknown landing regions.

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As a result of new aviation legislation, from 2019 on all air-carrier pilots are obliged to go through flight simulator-based stall recovery training. For this reason the Control and Simulation division at Delft University of Technology has set up a task force to develop a new methodology for high-fidelity aircraft stall behavior modeling and simulation. As part of this research project, the development of a new high-fidelity Cessna II simulation model, valid throughout the normal, pre-stall flight envelope, is presented in this paper. From an extensive collection of flight test data, aerodynamic model identification was performed using the Two-Step Method. New in this approach is the use of the Unscented Kalman Filter for an improved accuracy and robustness of the state estimation step. Also, for the first time an explicit data-driven model structure selection is presented for the Citation II by making use of an orthogonal regression scheme. This procedure has indicated that most of the six non-dimensional forces and moments can be parametrized sufficiently by a linear model structure. It was shown that only the translational and lateral aerodynamic force models would benefit from the addition of higher order terms, more specifically the squared angle of attack and angle of sideslip. The newly identified aerodynamic model was implemented into an upgraded version of the existing simulation framework and will serve as a basis for the integration of a stall and post-stall model. @en