Student Projects
Note: If you are an international student interested in doing an internship, research stay, or your senior/master thesis in our lab, check the information available in Open Positions.
If you are an undergraduate or graduate student from UMA interested in space robotics, below you will find the latest available projects in our lab.
If you would like to do a project with us but couldn’t find a suitable project, all projects are already assigned, or you have a project idea you’d like to pursue in our lab, please email us explaining your proposed idea.
Please note that some projects are exclusively available for 
, while others are open for 
or both. Some of our projects are often conducted in collaboration with one of our multiple partners in the frame of national and international research projects. Our partners often include the European Space Agency, NASA Ames Research Center, DFKI, University of Luxembourg, Delft University of Technology, Tohoku University, EPFL, and many more.
How to apply
Please, send an email to srl@uma.es or directly to the person in charge of each project with the following: 1) your CV, 2) your bachelor’s or master’s transcripts (you can find it via UMA’s website), 3) title of the project you are applying for, and (optional) 4) a brief description of any relevant experience related to the project’s scope.
2026/2027 Projects
#1 Fast Vision-based Autonomous Navigation Across Extreme, Off-road Environments / Navegación Autónoma Rápida a Través de Entornos Extremos Basada en Visión Artificial - Unavailable
 |
A number of upcoming planetary exploration missions demand speeds orders of magnitude greater than current operational rover speeds. In this project, you will evaluate, design, implement, and test a navigation pipeline aimed at estimating the position and orientation of a robot when traversing an unstructured environment at a speed of 1 m/s or faster across different times of the day (daytime vs nighttime). Performance will be compared against state-of-the-art solutions for off-road autonomous navigation and evaluated for potential implementation in space-grade processing units. An evaluation of the accuracy and precision of the pose estimates will be conducted. The goal is to benchmark this navigation pipeline as the foundation for future developments. Both conventional and learning-based approaches could be considered. Keywords: #navigation, #off-road, #speed, #planetary-exploration, #computer-vision |
#2 Relative Localization in Dark Environments Using Thermal Infrared (TIR) Cameras / Localización relativa en entornos oscuros empleando cámaras TIR - Unavailable
 |
This project focuses on improving relative localization for mobile robots operating in low-visibility environments using thermal infrared (TIR) cameras. Relative localization estimates a robot’s change in position and orientation over time and is essential in applications such as space exploration, inspection, and rescue, where GPS is unavailable or unreliable.Conventional visual localization methods rely on detecting and tracking image keypoints using RGB cameras. However, these techniques often fail in environments with poor visibility, such as lunar lava tubes or smoke-filled disaster sites. Thermal cameras provide an alternative sensing modality, but existing keypoint-based localization methods are not well suited to thermal imagery because of its lower resolution, higher noise levels, and the presence of misleading thermal gradients caused by reflections and heat distortions. While some thermal-based localization approaches have shown promising results in structured indoor environments, they do not generalize well to unstructured outdoor settings such as caves or cliffs. The project therefore aims to evaluate different keypoint detection and description techniques on thermal images and adapt one of them to achieve accurate, robust, and computationally efficient relative localization in unstructured, low-visibility environments. Keywords: #navigation, #computer-vision, #multi-modal, #underground-exploration, #machine-learning, #advanced-sensing |
more coming soon...
