PhD Projects

Kuldeep R. Barad

Modular vISion for dynamic grasping of Unknown Resident Space Objects

Advanced robotic capabilities and operational autonomy are crucial enabling technologies for the envisioned progress in in-space servicing, assembly, and manufacturing and resource utilization. Intelligent robotic manipulation is deemed central to enabling and scaling up these applications, where capabilities like handling unknown objects in unstructured scenarios are crucial. In this context, this project proposes to develop a state-of-the-art robot vision pipeline and the underlying algorithms for space-borne vision-based grasping. The project's developments are geared toward testing, validation and use on Redwire's commercial light-weight robotic manipulation system. The development concerns synthesis of compute-efficient pipeline to accomplish the following tasks on an on-board processing unit: (1) accurate estimation of target object’s position and attitude and (2) subsequent estimation of grasp configuration that leads to a secure grasp on an unknown object in space.

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Maxime Hubert Delisle

Design of a Capturing, Absorbing, SEcuring system for active space Debris removal

Space debris is caused by millions of non-functional, human-made objects left in space that become a hazard for current and future space missions. Yet, no Active Debris Removal (ADR) systems currently exist. Many methods have been proposed, but most of them are still in early technological development stages. CASED concerns the design, prototyping, and testing of a SotA flexible capturing mechanism for ADR missions that can be placed in a CubeSat-based (CS) structure, targeting a range of uncooperative space debris, reliable and cost-effective. In addition, CASED will verify and validate (V&V) the developed capture system in the close-to-real facility of SnT that simulates orbital scenarios. Therein, the project will tackle the following objectives: OBJ-1: Design and development of robust, reliable, flexible CS capturing mechanisms for ADR missions. OBJ-2: Prototype and test these novel capturing mechanisms in relevant close-to-real orbital scenarios at SnT facilities. OBJ-3: V&V of the performance of the selected system for ADR missions.

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Andrej Orsula

Space Assembly through Learning of Transferable Skills

This project investigates how model-based reinforcement learning can be applied to acquire manipulation skills for performing assembly tasks in different application domains of space robotics. The developed approach aims to facilitate self-supervised robot learning that is deployable to various real robot systems based on experience collected inside realistic simulation environments. Skill transfer among application domains will be accomplished by employing high-dimensional actions and observations that are invariant to the environment, task, robot kinematics and visual sensors. The applicability of the proposed approach will be evaluated on real robots under laboratory conditions of planetary and orbital scenarios, demonstrating the feasibility of sim-to-real transfer.

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Xiao LI

Design and implementation of software in the loop architecture for active space debris removal high-fidelity scenarios

The thesis is part of the HELEN project which aims to create a high-fidelity testing environment for the active space debris removal capturing system (FlexeS). The thesis is focused on developing real-time photorealistic in-orbit simulations (digital twin) for the capturing system, as well as designing and implementing a software-in-the-loop architecture for testing the latter.

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Mohatashem Reyaz

Interaction Strategies for Robotic Manipulators in Space Applications

The goal of this project is development and performance evaluation of interaction control strategies for orbital robotic applications and ground based HIL simulations. To this effect, the focus of this work is two-fold. Firstly, development of application specific control algorithms for robotic manipulators in simulated microgravity environments. This involves a wide range of in-orbital robotic manipulation activities. Secondly, to use robot interaction control to simulate spacecraft interaction behaviour in the ground testing facilities.

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This PhD thesis projects aims to analyse the existing state-of-the-art visual Simultaneous Localisation And Mapping (vSLAM) software, verify its suitability for robotics exploration on the lunar surface, identify challenges and optimize the software for potential shortcomings as the algorithms have been optimized for terrestrial applications. The main challenges on the lunar surface consist of the extreme lighting conditions of extremely dark shadows and extremely bright sunlight that is not filtered through an atmosphere, and the feature-poor environment. To mitigate the shortcomings of the purely vSLAM algorithm, additional sensors such as (solid state) Light Detection And Ranging (LiDAR) sensors, wheel encoders and IMU can improve the robustness and accuracy of the mapping process, making it more adapted for the lunar surface.

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Jose Ignacio Delgado Centeno

Enhancing robotic mission planning on the Moon

Using Machine Learning based techniques to improve the quality and resolution of remote sensing data of the Moon. Robotic task planning on lunar surface requires enormous amount of data, typically gathered with satellites. This project presents a direct solution to improve the already collected data, particularly images, enhancing the capabilities of the planning teams working in future lunar robotics missions. This work explores additionally an alternative to the launch of new satellites with better instrumentation into lunar orbits to obtain more precise data, with the corresponding economic and time saving.

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Loïck Chovet

DLT-based decision-making strategies of heterogeneous multi-robot system for planetary exploration

FiReSpARX’ objective is to use distributed ledger technologies as well as intelligent agent technologies to develop space robots that follow a given governance (RegTech) and make autonomous economic decisions (FinTech) to jointly create collaborative MRS. This form of dynamic collaboration between space robots could lower market barriers in the space sector and thus transform the whole industry. This topic hence perfectly fits Luxembourg’s national research priorities “Autonomous and intelligent systems and robotics for earth and space“ as well as “Fintech/RegTech and transformative applications of distributed ledger technologies“.

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Gabriel M. Garcia

CollAborative Exploration and Structural Analysis using heterogenous multi-Robot system in eXtreme enviRonments

Nowadays, we are using more and more robots to replace the tedious, difficult, or even impossible tasks to be done by humans. This is especially true in the undergrounds, where the walls can be damaged by water, vibration, seismic waves or any aggression through time. The CAESAR-XR project allows, through all its dangers, to send entire fleets of robots to map, explore and discover points of interest in long-abandoned mines and caves. For such systems to work autonomously, they must 'see' and 'understand' the environment through different components. In the CAESAR-XR project, robots of all types can be used. Some sensors allow the robot to make a 3D map, while others use colour cameras or calculate the distance between the robot and the various obstacles in front of the robot. These fleets of intelligent robots will not always be connected with a human pilot, which is why they must communicate and make decisions by themselves when exploring the underground. CAESAR-XR aims to develop a simulated environment based on a randomised generation of underground environments to facilitate the validation and verification of the developed algorithms before accessing underground environments. Experts can then use the data collected by the robots to help identify potential weaknesses in the internal structure of the explored areas. The use of this information can be used in the mining industry as well as in space with potential explorations of lunar lava tubes that could serve as protection for a base against radiation and extreme temperatures.

PhD Details