Teams of mobile robots will play a crucial role in future missions to explore the surfaces of extraterrestrial bodies. Setting up infrastructure and taking scientific samples are expensive tasks when operating in distant, challenging, and unknown environments. In contrast to current single-robot space missions, future heterogeneous robotic teams will increase efficiency via enhanced autonomy and parallelization, improve robustness via functional redundancy, as well as benefit from complementary capabilities of the individual robots. In this article, we present our heterogeneous robotic team, consisting of flying and driving robots that we plan to deploy on scientific sampling demonstration missions at a Moon-analogue site on Mt. Etna, Sicily, Italy in 2021 as part of the ARCHES project. We describe the robots' individual capabilities and their roles in two mission scenarios. We then present components and experiments on important tasks therein: automated task planning, high-level mission control, spectral rock analysis, radio-based localization, collaborative multi-robot 6D SLAM in Moon-analogue and Marslike scenarios, and demonstrations of autonomous sample return.

The increasing number of launched satellites per year, calls for solutions to keep free operational space for telecommunication systems in geo-synchronized orbit, as well as to avoid the endangering of space systems in LEO (Low-Earth Orbit) and of the public living in the habited parts on Earth. Examples for such dangerous stranded space systems in the past are Skylab and MIR. In the future, the uncontrolled and accidental de-orbiting of other huge satellites is expected, where parts of these will hit the surface of the Earth.

Planetary rovers increasingly rely on vision‐based components for autonomous navigation and mapping. Developing and testing these components requires representative optical conditions, which can be achieved by either field testing at planetary analog sites on Earth or using prerecorded data sets from such locations. However, the availability of representative data is scarce and field testing in planetary analog sites requires a substantial financial investment and logistical overhead, and it entails the risk of damaging complex robotic systems. To address these issues, we use our compact human‐portable DLR Sensor Unit for Planetary Exploration Rovers (SUPER) in the Moroccan desert to show resource‐efficient field testing and make the resulting Morocco‐Acquired data set of Mars‐Analog eXploration (MADMAX) publicly accessible. The data set consists of 36 different navigation experiments, captured at eight Mars analog sites of widely varying environmental conditions. Its longest trajectory covers 1.5 km and the combined trajectory length is 9.2 km. The data set contains time‐stamped recordings from monochrome stereo cameras, a color camera, omnidirectional cameras in stereo configuration, and from an inertial measurement unit. Additionally, we provide the ground truth in position and orientation together with the associated uncertainties, obtained by a real‐time kinematic‐based algorithm that fuses the global navigation satellite system data of two body antennas. Finally, we run two state‐of‐the‐art navigation algorithms, ORB‐SLAM2 and VINS‐mono, on our data to evaluate their accuracy and to provide a baseline, which can be used as a performance reference of accuracy and robustness for other navigation algorithms. The data set can be accessed at https://rmc.dlr.de/morocco2018.

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Future space missions are directed to robotic precursor missions to nearby celestial objects. Various science experiments are meant to be performed by autonomous robotic vehicles including detection of widely speculated polar-ice in lunar craters and detect signs of past life on Mars. The locomotion subsystem plays a key role in moving a robot on a surface with high performance capabilities, irrespective of the nature of the terrain. Locomotion on extraterrestrial surfaces can be achieved by a wheeled rover, tracked rover, legged walker or a hybrid vehicle. The first three modes can be classified based on the number of wheels, tracks, or legs the robot possesses. Hybrids can be either a wheeled-leg or a legged-track combination.