To mitigate the growth of space debris, it is vital that Active Debris Removal (ADR) is performed in the near future. Since ADR will require robust relative navigation, future spacecraft should be marked with fiducial markers that are robustly extracted from an image taken by a chaser spacecraft. To prevent the restriction of robotic movement and occlusions of features, planarity of these fiducials is desired. While numerous planar fiducials exist, the performance of these systems suffers from pose ambiguities and loss of precision under frontal observations. In order to mitigate these issues, encoding markers have been proposed in literature. These markers encode an extra dimension of information in the signal between marker and sensor, thus increasing the robustness of the fiducial system. However, little research has been done on these encoding markers and existing solutions are hard to manufacture, qualify and scale. This thesis proposes a novel fiducial marker design based on the compound eye of a Mantis insect. The fiducial proposed in this work, the so-called Mantis Marker, is planar and encodes a virtual point in its signal. This virtual point is located at a distance from the marker plane. This makes the marker system mathematically equivalent to a fiducial that uses a protrusion or indentation. This increases pose robustness by an order of magnitude with respect to planar fiducials of the same dimensions, as demonstrated in this thesis. Furthermore, the marker is easy to manufacture, is scalable and has low complexity. Finally, the marker is suitable for pose estimation for robotics and Augmented Reality (AR) on Earth where additional pose robustness is required.

To manage the increasing amount of satellite traffic in orbit and renewed operational ambitions, it is crucial to advance the capabilities of space robotic systems to enable complex manipulability tasks. These abilities will provide the necessary components to perform satellite de-orbiting, servicing of important assets, and assistance in on-orbit operations. With knowledge transfer from the terrestrial domain, the Aut-O-MAGIC research project proposes global graspability maps based on analytic grasp quality metrics to enable robust gripper operations without external intervention. Global graspability maps draw parallels from terrain traversability maps in robotic navigation, which use cost maps to plan optimised traverses ahead of time. Until now, grasping tasks in space have only had sufficient target surface information to plan the next action, disregarding optimisation or complex tasks that require multiple actions. The Aut-O-MAGIC grasp planning engine provides promising graspability cost maps that indicate populous sets of satisfactory grasps on the target object surface.