Exploration of lava caves on the surface of planetary bodies near Earth is of high importance for scientific research and space exploration. The natural shielding that these caves offer against radiation and small meteorites makes them well suited for preserving exobiological signatures and protecting human-made facilities. The use of a robot team arises as the safest and most cost-efficient way to explore extraterrestrial lava caves because they are difficult to access. Although the approach has been demonstrated in similar scenarios on Earth, its adaptation to space conditions needs further research. Here, we define a lava cave exploration mission concept, including four mission phases that are performed by a heterogeneous team of three robots equipped with the required hardware and software. This mission concept was validated in a relevant scenario, a lava cave on Lanzarote island (Spain), where the team of robots was able to build a three-dimensional model of the surrounding area and skylight, introducing a scout rover through rappelling and exploring the inner part of the cave. The results obtained demonstrate the proposed mission concept’s feasibility, including three next-generation planetary exploration rovers that were coordinated to obtain meaningful information about the lava cave’s external and internal morphology.

A highly restricted workspace of the robotic arm may hinder to perform safely any autonomous mobile manipulation task with planetary exploration rovers. To ensure mission safety as well as high efficiency, a coupled path and motion planner for mobile manipulation is presented in this work. First, a Fast Marching Method based path planner generates a safe trajectory to reach the goal vicinity, avoiding obstacles and non-traversable areas in the scenario. The path planner is able to control the final rover orientation to ensure that the goal is finally reachable by the arm. Second, a 3D Fast Marching Method based motion planner generates the arm joints motion profile, by creating a 3D tunnel-like cost volume surrounding the already computed rover base trajectory. This tunnel makes use of an offline-computed safe workspace of the manipulator, thus ensuring that no self-collision will occur during the planned motion. The presented algorithm has been tested with multiple simulation experiments, benchmarked with an off-the-shelf motion planning library, and validated in a field test campaign with the rover SherpaTT of DFKI Robotics Innovation Center. The tests consisted in SherpaTT approaching an interesting area on the scenario and performing a mobile manipulation sample scanning operation. These experiments have demonstrated that the proposed motion planner increases efficiency as well as ensures mission safety. This is thanks to, on the one hand, a coordinated base-arm movement that results in maximized efficiency in time terms, and, on the other hand, considering the manipulator workspace offline in the mobile manipulation motion planner to guarantee self-collision avoidance.

The use of autonomous rovers for planetary exploration is crucial to traverse long distances and perform new discoveries on other planets. One of the most important issues is related to the interaction between the rover wheel and terrain, which would help to save energy and even avoid getting entrapped. The use of reconfigurable rovers with different locomotion modes has demonstrated improvement of traction and energy consumption. Therefore, the objective of this paper is to determine the best locomotion mode during the rover traverse, based on simple parameters, which would be obtained from propioceptive sensors. For this purpose, interaction of different terrains have been modelled and analysed with the ExoTeR, a scale prototype rover of the European ExoMars 2020 mission. This rover is able to perform, among others, the wheel walking locomotion mode, which has been demonstrated to improve traction in different situations. Currently, it is difficult to decide the instant time the rover has to switch from this locomotion mode to another. This paper also proposes a novel method to estimate the slip ratio, useful for deciding the best locomotion mode. Finally, results are obtained from an immersive simulation environment. It shows how each locomotion mode is suitable for different terrains and slopes and the proposed method is able to estimate the slip ratio.

The goal of the Mars Sample Return campaign is to collect soil samples from the surface of Mars and return them to Earth for further study. The samples will be acquired and stored in metal tubes by the Perseverance rover and deposited on the Martian surface. As part of this campaign, it is expected that the Sample Fetch Rover will be in charge of localizing and gathering up to 35 sample tubes over 150 Martian sols. Autonomous capabilities are critical for the success of the overall campaign and for the Sample Fetch Rover in particular. This work proposes a novel system architecture for the autonomous detection and pose estimation of the sample tubes. For the detection stage, a Deep Neural Network and transfer learning from a synthetic dataset are proposed. The dataset is created from photorealistic 3D simulations of Martian scenarios. Additionally, the sample tubes poses are estimated using Computer Vision techniques such as contour detection and line fitting on the detected area. Finally, laboratory tests of the Sample Localization procedure are performed using the ExoMars Testing Rover on a Mars-like testbed. These tests validate the proposed approach in different hardware architectures, providing promising results related to the sample detection and pose estimation.

This work presents a computationally lightweight motion planner for over-actuated platforms. For this purpose, a general state-space model for mobile platforms with several kinematic chains is defined, which considers dynamics, nonlinearities and constraints. The proposed motion planner is based on a sequential multi-stage approach that takes advantage of the warm start on each step. Firstly, a globally optimal and smooth 2D/3D trajectory is generated using the Fast Marching Method. This trajectory is fed as a warm start to a sequential linear quadratic regulator that is able to generate an optimal motion plan without constraints for all the platform actuators. Finally, a feasible motion plan is generated considering the constraints defined in the model. In this respect, the sequential linear quadratic regulator is employed again, taking the previously generated unconstrained motion plan as a warm start. The motion planner has been deployed into the Exomars Testing Rover of the European Space Agency. This rover is an Ackermann-capable planetary exploration testbed that is equipped with a robotic arm. Several experiments were carried out demonstrating that the proposed approach speeds up the computation time and increases the success ratio for a martian sample retrieval mission, which can be considered as a representative use case of goal-constrained trajectory generation for an over-actuated mobile platform.