RT Conference Proceedings
T1 Samples detection and retrieval for a sample fetch rover
A1 Mantoani, Laura
A1 Castilla-Arquillo, Raúl
A1 Paz Delgado, Gonzalo Jesús
A1 Pérez-del-Pulgar-Mancebo, Carlos Jesús
A1 Azkarate, Martin
K1 Robótica
K1 Astronomía del espacio
K1 Exploración espacial
K1 Navegación espacial
AB Future planetary exploration missions are demanding more and more autonomy since these missions are getting more complex. A clear example is the Mars Sample Return mission, where the Sample Fetch Rover needs to collect sample tubes on a remote location, and bring them back to the base station to be launched to Earth. This mission requires to extend the autonomous capabilities onboard. First, the Navigation component needs to be able to detect and locate the sample tubes, and second, the Guidance and Control ones require to place the rover close the sample tubes and move the manipulator to pick them up. These are the main contributions of this paper. The first issue has been solved by the use of Deep Neural Networks, which allow to identify the previously trained sample tubes on images, and the second one has been solved by extending the path planning algorithm within the Guidance component.To demonstrate and validate the proposed methods, two experiments were carried out. A first field test in the Search and Rescue experimental terrain at the University of Malaga, and a second lab test in the Planetary Robotics Lab at the European Space Agency. Both experiments were carried out using the ExoMars Testing Rover owned by the last institution.
YR 2022
FD 2022
LK https://hdl.handle.net/10630/24497
UL https://hdl.handle.net/10630/24497
LA spa
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026