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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. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Rovers operating on Mars are in need of more and more autonomous features to ful ll their challenging mission requirements. However, the inherent constraints of space systems make the implementation of complex algorithms an expensive and difficult task. In this paper we propose a control architecture for autonomous navigation. Efficient implementations of autonomous features are built on top of the current ExoMars navigation method, enhancing the safety and traversing capabilities of the rover. These features allow the rover to detect and avoid hazards and perform long traverses by following a roughly safe path planned by operators on ground. The control architecture implementing the proposed navigation mode has been tested during a field test campaign on a planetary analogue terrain. The experiments evaluated the proposed approach, autonomously completing two long traverses while avoiding hazards. The approach only relies on the optical Localization Cameras stereobench, a sensor that is found in all rovers launched so far, and potentially allows for computationally inexpensive long-range autonomous navigation in terrains of medium difficulty.