The interest in exploring planetary bodies for scientific investigation and in situ resource utilization is ever-rising. Yet, many sites of interest are inaccessible to state-of-the-art planetary exploration robots because of the robots’ inability to traverse steep slopes, unstructured terrain, and loose soil. In addition, current single-robot approaches only allow a limited exploration speed and a single set of skills. Here, we present a team of legged robots with complementary skills for exploration missions in challenging planetary analog environments. We equipped the robots with an efficient locomotion controller, a mapping pipeline for online and postmission visualization, instance segmentation to highlight scientific targets, and scientific instruments for remote and in situ investigation. Furthermore, we integrated a robotic arm on one of the robots to enable high-precision measurements. Legged robots can swiftly navigate representative terrains, such as granular slopes beyond 25°, loose soil, and unstructured terrain, highlighting their advantages compared with wheeled rover systems. We successfully verified the approach in analog deployments at the Beyond Gravity ExoMars rover test bed, in a quarry in Switzerland, and at the Space Resources Challenge in Luxembourg. Our results show that a team of legged robots with advanced locomotion, perception, and measurement skills, as well as task-level autonomy, can conduct successful, effective missions in a short time. Our approach enables the scientific exploration of planetary target sites that are currently out of human and robotic reach.

We evaluate the capability of three different digital image correlation (DIC) algorithms to measure long-term surface displacement caused by a large slope instability in the Swiss Alps. DIC was applied to high-resolution optical imagery taken by airborne sensors, and the accuracy of the displacements assessed against global navigation satellite system measurements. A dynamic radiometric correction of the input images prior to DIC application was shown to enhance both the correlation success and accuracy. Moreover, a newly developed spatial filter considering the displacement direction and magnitude proved to be an effective tool to enhance DIC performance and accuracy. Our results show that all algorithms are capable of quantifying slope instability displacements, with average errors ranging from 8 to 12% of the observed maximum displacement, depending on the DIC processing parameters, and the pre- and postprocessing of the in- and output. Among the tested approaches, the results based on a fast Fourier transform correlation approach provide a considerably better spatial coverage of the displacement field of the slope instability. The findings of this study are relevant for slope instability detection and monitoring via DIC, especially in the context of an ever-increasing availability of high-resolution air- and spaceborne imagery.

Past exploration missions have revealed that the lunar topography is eroded through mass wasting processes such as rockfalls and other types of landslides, similar to Earth. We have analyzed an archive of more than 2 million high-resolution images using an AI and big data-driven approach and created the first global map of 136.610 lunar rockfall events. Using this map, we show that mass wasting is primarily driven by impacts and impact-induced fracture networks. We further identify a large number of currently unknown rockfall clusters, potentially revealing regions of recent seismic activity. Our observations show that the oldest, pre-Nectarian topography still hosts rockfalls, indicating that its erosion has been active throughout the late Copernican age and likely continues today. Our findings have important implications for the estimation of the Moon’s erosional state and other airless bodies as well as for the understanding of the topographic evolution of planetary surfaces in general.

The analysis of rockfall distribution and magnitude is a useful tool to study the past and current endogenic and exogenic activity of Mars. At the same time, tracks left by rockfalls provide insights into the mechanical properties of the martian surface. While a wealth of high-resolution space-borne image data are available, manual mapping of displaced boulders with tracks is inefficient and slow, resulting in 1) a small total number of mapped features, 2) inadequate statistics, and 3) a sub-optimal utilization of the available big data. This study implements a deep learning-driven approach to automatically detect and map martian boulders with tracks in High Resolution Imaging Science Experiment (HiRISE) imagery. Six off-the-shelf neural networks have been trained either on martian or lunar rockfall data, or a combination of both, and are able to achieve a maximum recall of up to 0.78 and a maximum precision of up to 1.0, with a mean average precision of 0.71. The fusion of training data from different planets and sensors results in an increased detection precision, highlighting the value of domain generalization and multi-domain learning. Average processing time per HiRISE image is ~45 s using an NVIDIA Titan Xp, which is more than one order of magnitude faster than a human operator. The developed deep learning-driven infrastructure can be deployed to map martian rockfalls on a global scale and within a realistic timeframe.

The lunar permanently shadowed regions (PSRs) are expected to host large quantities of water-ice, which are key for sustainable exploration of the Moon and beyond. In the near future, NASA and other entities plan to send rovers and humans to characterize water-ice within PSRs. However, there exists only limited information about the small-scale geomorphology and distribution of ice within PSRs because the orbital imagery captured to date lacks sufficient resolution and/or signal. In this paper, we develop and validate a new method of post-processing LRO NAC images of PSRs. We show that our method is able to reveal previously unseen geomorphological features such as boulders and craters down to 3 meters in size, whilst not finding evidence for surface frost or near-surface ice. Our post-processed images significantly facilitate the exploration of PSRs by reducing the uncertainty of target selection and traverse/mission planning.

This paper implements a novel approach to automatically detect and classify rockfalls in Lunar Reconnaissance Orbiter narrow angle camera (NAC) images using a single-stage dense object detector (RetinaNet). The convolutional neural network has been trained with a data set of 2932 original rockfall images. In order to avoid overfitting, the initial training data set has been augmented during training using random image rotation, scaling, and flipping. Testing images have been labelled by human operators and have been used for RetinaNet performance evaluation. Testing shows that RetinaNet is capable to reach recall values between 0.98 and 0.39, precision values between 1 and 0.25, and average precisions ranging from 0.89 to 0.69, depending on the used confidence threshold and intersection-over-union values. Mean processing time of a single NAC image in RetinaNet is around 10 s using a GeForce GTX 1080 Ti and GeForce Titan Xp, which is in orders of magnitudes faster than a human operator. The processing speed allows to efficiently exploit the currently available NAC data stack with more than 1 million images in a reasonable timeframe. The combination of speed and detection performance can be used to produce lunar rockfall distribution maps on large spatial scales for utilization by the scientific and engineering community.