The ESA Lunar Lander mission is a step in the preparation for future exploration missions, being tasked with demonstrating autonomous soft, safe precision landing on the Moon. The last segment of the mission's descent and landing phase is the approach phase, during which the GNC subsystem steers the spacecraft to the targeted landing site (LS). The Hazard Detection and Avoidance (HDA) subsystem is active during this phase and uses the measurements provided by a camera and a LIDAR to assess the safety of the terrain. The HDA subsystem also determines which are the ground locations that can be reached by the spacecraft. Based on the safety and reachability assessments, the HDA subsystem then decides autonomously if a retargeting should be commanded to a new LS. In that case, the GNC subsystem is notified and tasked with driving the vehicle to perform a soft landing at the designated LS. This paper describes the concept and performances of the HDA subsystem proposed for the Lunar Lander mission. Sensitivity tests are performed in order to evaluate the robustness of the system. The performance of the HDA subsystem is demonstrated through Monte Carlo simulation campaigns on a functional engineering simulator.

ABSTRACT:The PERIGEO R&D project aims at developing, testing and validating algorithms and/or methods for space missions in various field of research. This paper focuses in one of the scenarios considered in PERIGEO: navigation for atmospheric flights. Space missions heavily rely on navigation to reach success, and autonomy of on-board navigation systems and sensors is desired to reach new frontiers of space exploration. From the technology side, optical frame cameras, LiDAR and inertial technologies are selected to cover the requirements of such missions. From the processing side, image processing techniques are developed for vision-based relative and absolute navigation, based on point extraction and matching from camera images, and crater detection and matching in camera and LiDAR images. The current paper addresses the challenges of space navigation, presents the current developments and preliminary results, and describes payload elements to be integrated in an Unmanned Aerial Vehicle (UAV) for in-flight testing of systems and algorithms. Again, UAVs are key enablers of scientific capabilities, in this case, to bridge the gap between laboratory simulation and expensive, real space missions.