Valentin Bickel

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Bickel

First Name

Valentin

ORCID

0000-0002-7914-2516

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Publications 1 - 10 of 65

Results and Lessons Learned from the First Field Trial of the ESA-ESRIC Space Resources Challenge of Team GLIMPSE
Arm, Philip; Waibel, Gabriel; Ligeza, Gabriela; et al. (2022)
Proceedings of ASTRA 2022
Impacts drive lunar rockfalls over billions of years
Bickel, Valentin; Aaron, Jordan; Manconi, Andrea; et al. (2020)
Nature Communications
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.
LunarLeaper - A Mission Concept to Explore the Lunar Subsurface with a Small-scale Legged Robot
Kolvenbach, Hendrik; Mittelholz, Anna; Stähler, Simon Christian; et al. (2024)
IAC 2024 Conference Proceedings
We present the LunarLeaper mission proposal, which is aimed to explore volcanic pits on the lunar surface. Lunar pits, or skylights, are collapsed features that may provide access to subsurface lava tubes, which could serve as shelters for future human explorers and offer insights into the volcanic history of the Moon by exposing ancient lava flows. The existence and extent of the caves are still debated today and require in-situ analysis. Our mission aims to deploy a payload-equipped, 10kg-class legged robot which can approach the Marius Hills pit, a potential entry to a cave system in a young volcanic region on the lunar nearside. Within the mission, the robot is planned to autonomously navigate the challenging terrain while using geophysical and imaging tools, such as a Ground Penetrating Radar and a Gravimeter. The mission will investigate key questions about lunar volcanism, such as the existence of subsurface caves and the magnitude and timing of lava flows, while assessing the site’s suitability for human utilization and habitation. Furthermore, the mission will demonstrate key enabling technologies, such as legged locomotion, as building blocks for next generation planetary missions.
Traversing Steep and Granular Martian Analog Slopes With a Dynamic Quadrupedal Robot
Kolvenbach, Hendrik; Arm, Philip; Hampp, Elias; et al. (2022)
Field Robotics
Celestial bodies, such as the Moon and Mars are mainly covered by loose, granular soil, which is a notoriously challenging terrain to traverse with wheeled robots. Here, we present experimental work on traversing steep, granular slopes with the dynamically-walking quadrupedal robot SpaceBok. To adapt to the challenging environment, we developed passive-adaptive, planar feet and optimized studs to reduce sinkage and increase traction. Single-foot experiments revealed that a surface area of 110 cm2 per foot reduces sinkage to an acceptable level for the 22 kg robot, even on highly collapsible soil. Implementing several 12 mm studs increases traction by 22% to 66% on granular media compared to stud-less designs. Together with a terrain-adapting walking controller, we validate — for the first time — static and dynamic locomotion on Mars analog slopes of up to 25° (the maximum of the testbed). We evaluated the performance between point- and planar feet and static and dynamic gaits for safety, velocity, and energy consumption. We show that dynamic gaits are energetically more efficient than static ones, but are riskier on steep slopes. Our tests also revealed that energy consumption with planar feet increases drastically as slope inclination approaches the soil’s angle of repose. Point feet are less affected by slippage due to their excessive sinkage but, in turn, are prone to instabilities and tripping. Based on our findings, we present safe and energy-efficient, global, path-planning strategies for negotiating steep Martian topography.
Unsupervised Learning for Thermophysical Analysis on the Lunar Surface
Moseley, Ben; Bickel, Valentin; Burelbach, Jérôme; et al. (2020)
The Planetary Science Journal
We investigate the use of unsupervised machine learning to understand and extract valuable information from thermal measurements of the lunar surface. We train a variational autoencoder (VAE) to reconstruct observed variations in lunar surface temperature from over 9 yr of Diviner Lunar Radiometer Experiment data and in doing so learn a fully data-driven thermophysical model of the lunar surface. The VAE defines a probabilistic latent model that assumes the observed surface temperature variations can be described by a small set of independent latent variables and uses a deep convolutional neural network to infer these latent variables and to reconstruct surface temperature variations from them. We find it is able to disentangle five different thermophysical processes from the data, including (1) the solar thermal onset delay caused by slope aspect, (2) effective albedo, (3) surface thermal conductivity, (4) topography and cumulative illumination, and (5) extreme thermal anomalies. Compared to traditional physics-based modeling and inversion, our method is extremely efficient, requiring orders of magnitude less computational power to invert for underlying model parameters. Furthermore our method is physics-agnostic and could therefore be applied to other space exploration data sets, immediately after the data is collected and without needing to wait for physical models to be developed. We compare our approach to traditional physics-based thermophysical inversion and generate new, VAE-derived global thermal anomaly maps. Our method demonstrates the potential of artificial intelligence-driven techniques to complement existing physical models as well as for accelerating lunar and space exploration in general.
Geologic Map and Potential Rover Traverses for Human-Assisted Sample Return Missions to the Schrödinger Basin
Farrant, Benjamin E.; Bell, S.K; Czaplinski, Ellen C.; et al. (2019)
Online Abstract: 50th Lunar and Planetary Science Conference (LPSC 2019)
Using Boulder Tracks as a Tool to Understand the Bearing Capacity of Permanently Shadowed Regions of the Moon
Sargeant, Hannah M.; Bickel, Valentin; Honniball, Casey I.; et al. (2020)
Journal of Geophysical Research: Planets
Permanently shadowed regions (PSRs) are abundant at the lunar poles. They experience no direct sunlight and reach temperatures as low as 30 K. PSRs are of interest as evidence suggests that some may contain water ice (H2O/OH‐), which could provide a record of the evolution of volatiles in the inner solar system. This water ice is also a critical resource for life‐support systems and rocket propellant. A better understanding of mechanical properties of PSR regolith, such as its bearing capacity, will help optimize the design of future exploration rovers and landers. Thirteen boulder tracks were identified on the edge of, or inside, south polar lunar PSR enhanced imagery and used to estimate the strength of the PSR regolith at latitudes of 70° to 76° in sites with maximum annual temperatures of 65 to 210 K. PSR boulder track features are similar to those observed in highland, mare, and pyroclastic regions of the Moon, implying similar properties of the regolith. Measured features were used to estimate bearing capacity for PSR regolith at depths of ~0.28 to 4.68 m. Estimated bearing capacity values suggest that these PSRs may be somewhat stronger than highland and mare regions at depths of 0.28 to 1.00 m. Bearing capacity in these PSRs is statistically the same as those in other regions of the Moon at depths of 1.00 to 2.00 m. The results of this study can be used to infer bearing capacity as one measure for the trafficability of lower‐latitude PSRs of the type measured here.
Unsupervised Distribution Learning for Lunar Surface Technosignature Detection
Angerhausen, Daniel; Bickel, Valentin; Lesnikowski, Adam (2020)
In this work we show that modern data-driven machine learning techniques can be successfully applied on lunar surface remote sensing data to learn, in an unsupervised way, sufficiently good representations of the data distribution to enable lunar technosignature and anomaly detection. In particular we have trained an unsupervised distribution learning model to find the landing module of the Apollo 15 landing site in a testing dataset, with no specific model or hyperparameter tuning .
A Big Lunar Data Application: Deep Learning-Driven Rockfall Detection and Mapping with NASA's Moon Trek
Bickel, Valentin; Law, Emily S.; Day, Brian H.; et al. (2019)
Quantitative assessment of digital image correlation methods to detect and monitor surface displacements of large slope instabilities
Bickel, Valentin; Manconi, Andrea; Amann, Florian (2018)
Remote Sensing
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.

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Valentin Bickel

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