MITM9
Session assets
INTRODUCTION
After the failure of the landing of Rashid-1 on the Moon, in April 2023, the MBRSC (Mohammed Bin Rashid Space Centre) decided to set up a new mission with the same rover design. It is expected that Rashid-2 will land in the mid-latitudes of the Moon in 2026. This paper focuses on the calibration of its optical cameras.
OPTICAL CAMERAS DESCRIPTION
The Rashid-2 rover carries 3 multispectral visible (RGB) cameras, as shown Figure 1. Their design is identical to that of Rashid-1, see [1]-[2]-[3] for more details. Basically, CAM-1 and CAM-2 are wide-angle navigation cameras, with a full diagonal angle of 115°, and CAM-M is a microscopic camera, with a spatial resolution better than 30µm.
DETECTOR CHARACTERIZATION
We first characterized the radiometric response of the CMOS detectors, in terms of gain, offset, dark current and readout noise, by measuring the average levels and the spatial non-uniformity maps. The dynamics of the detectors are encoded in 10 bits and the measured gains are:
- CAM-1: 0.346 DN/e-
- CAM-2: 0.173 DN/e-
- CAM-M: 0.147 DN/e-
The average levels are given with respect to the temperature in Figure 2 and the standard deviation of the non-uniformity maps are always between 4% and 5% at ambient temperature (except for CAM-M offset: 7%). The dark current is approximated by an Arrhenius law adapted for the high levels: dark=exp(62.5-1.5/kT). Actually, it is only properly measured for CAM-M because the camera was not yet integrated, so the temperature could be measured directly on the detector. However, we can relate an internal uncalibrated temperature register from the detector to the measured dark current. This relation will be used during the mission.
INTEGRATED CAMERAS CHARACTERIZATION
This section only describes CAM-1 and CAM-2, as they are integrated at CNES. CAM-M is integrated by Kampf Telescope Optics in Munich[3]. These characterizations include the effects of the optical components.
Radiometry
Radiometric calibration consists of three distinct measurements:
- The spatial non-uniformity of the response, both at low and high frequencies,
- The spectral shape of the response (colorimetric response),
- The absolute radiometric response to a typical scene.
The non-uniformity is evaluated by illuminating the camera with a uniform illuminant generated by an integrating sphere. The integration time is chosen in order to obtain the higher dynamics in the images without saturation. A burst of images is acquired to reduce the noise effects and derive the flat field (Figure 3-left).
A median filter is then applied to extract only the low frequency component, i.e. the vignetting effect. By dividing it to the measured flat, we can also estimate the Pixel Response Non Uniformities (Figure 3-right) and detect defective and “noisy” pixels that are far from the standard response. This vignetting/PRNU separation will be done on the fly during the mission by the CASPIP[4] operational library.
Knowledge of the instrument’s colorimetry is necessary for proper interpretation of the spectral distribution of the observed scene, which is required for geological analysis[1]. It is calibrated using color patches of known reflectance, in a dedicated chamber that reproduces a spectrum similar to the sunlight reflected from the lunar surface, in order to mimic the lunar illuminant. The result is a 3x3 matrix that is used to convert camera’s readings into human-perceivable colors.
The absolute response is measured in the same chamber with a spectralon. The Digital Numbers are averaged around the center of the image, where the effect of vignetting is negligible. Knowing the mean offset and the mean dark current from the detector characterization, we can calculate the absolute radiometric coefficient that relates the measurement (in DN) to the flux (in W/m²/sr/µm), see Figure 4 for the resulting values.
Resolution
The cameras resolution is measured using the slanted-edge method[5], which is extended over the field of view with a checkerboard pattern (Figure 5-a). The slanted-edge method directly calculates the MTF curves in the vertical and horizontal directions for each transition, but it is affected by noise at high frequencies. Moreover, it is necessary to demosaic the Bayer pattern before computing the MTF, so as not to be limited to half the sampling frequency, and thus the MTF is also affected by the mosaicing/demosaicing operations[6]. However, it results in a representative MTF as obtained on the final product. It is approximated and extrapolated by an exponential law (Figure 5-b).
Geometry
These wide-angle cameras introduce a large distortion to the images. It is approximated by a specially tuned polynomial model[1]. The parameters of the model are calculated by fitting it to images of the checkerboard pattern captured at different positions and orientations (Figure 6-up). The transitions on the checkerboard are detected using a Canny filter and the MTF response to account for the smoothing of the edges. The resulting polynomial is given in Figure 6-bottom.
CONCLUSION
The Rashid-2 cameras have been calibrated on-ground and are now integrated on the rover. Launch and landing operations, as well as thermal variations on the Moon, may affect some parameters, so additional calibrations will be performed onboard during the mission, with more constraints on their realization.
REFERENCES
[1] N. Théret, E. Cucchetti, E. Robert et al., Enhanced Image Processing for the CASPEX Cameras Onboard the Rashid-1 Rover. Space Sci Rev 220, 60 (2024). https://doi.org/10.1007/s11214-024-01091-0
[2] Z. Ioannou, S. Amilineni, S.G. Els et al., Onboard and Ground Processing of the Wide-Field Cameras of the Rashid-1 Rover of the Emirates Lunar Mission. Space Sci Rev 221, 8 (2025). https://doi.org/10.1007/s11214-024-01127-5
[3] S.G. Els, N. Ageorges, M. Bogosavljevic et al., The Microscope Camera CAM-M on-Board the Rashid-1 Lunar Rover. Space Sci Rev 220, 81 (2024). https://doi.org/10.1007/s11214-024-01117-7
[4] N. Théret, Q. Douaglin et al., CASPEX Image Processing: a tool for space exploration, https://doi.org/10.5194/epsc2024-15
[5] M. Estribeau et P. Magnan, Fast MTF measurement of CMOS imagers using ISO 12233 slanted-edge methodology, SPIE Optical System Design 2003
[6] N. Théret, A. Courtois, Q. Douaglin et S. Lucas, Mesure de FTM optique sur caméras intégrées avec motifs Bayer, in preparation
How to cite: Théret, N., Guénin, M., Bonassi, T., Douaglin, Q., Virmontois, C., Lalucaa, V., and Els, S.: Preparing Rashid-2 Lunar Mission: calibration of the optical cameras, EPSC-DPS Joint Meeting 2025, Helsinki, Finland, 7–13 Sep 2025, EPSC-DPS2025-27, https://doi.org/10.5194/epsc-dps2025-27, 2025.
Introduction:
Understanding the properties of planetary regolith is essential to predict the response of a surface to interactions and correctly interpret the outcome of in-situ operations. In preparation for the interpretation of data from small body missions with surface interaction, we investigate models relating the dynamics of an impact into granular media and the mechanical properties of the surface material. The objective is to constrain frictional properties of regolith using data acquired during landing.
Methods:
Landing on small bodies can be modelled as low-velocity collisions into a granular material in low-gravity conditions. We use experimental data to assess the agreement between empirical scalings from collisional models and frictional properties. Our data are from past impact experiments [3,4] conducted in terrestrial (1g) and low-gravity conditions using a drop tower [5]. The experiments consist of releasing an instrumented spherical projectile from a controlled height into a granular material. From the acceleration profile, the velocity and position are derived by integration. Here, we focus on specific datasets obtained for glass beads and quartz sand of average grain sizes 1.5 mm and 1.8 mm, respectively. The experimental range of impact velocities is 0.2 – 1.2 m/s in 1g and 0.01-0.4 m/s in low-gravity. The average gravity level in the low-gravity experiments is 1.8 m/s² for the glass beads and 0.8 m/s² for the sand.
Results:
We consider the time series for acceleration, velocity, and position of the projectile during impact for various impact velocities. To describe the impact dynamics, the Poncelet collisional model is frequently used, including a drag force composed of two contributions: quasi-static friction and hydrodynamic drag. In Katsuragi et al. [1] the quasi-static term is linear in depth (z), and in the framework of Sunday et al. [2], this component is modified to highlight a regime transition after a certain depth z1, at which the quasi-static friction saturates.
Here we consider a hydrodynamic drag coefficient h(z), and a depth-dependent quasi-static friction force f(z), giving the following equation of motion:
ma = mg – Fd, with Fd = h(z)v² + f(z) (1)
We plot the total drag force Fd in Fig. 1 for the glass beads and the sand as a function of the velocity squared at different depths in the material. The slope of each linear fit is the hydrodynamic coefficient h(z), and the intercept is the quasi-static friction f(z). We represent the estimated quasi-static friction force against depth in Fig. 2.
In 1g, the quasi-static friction force increases linearly until the transition depth where it becomes roughly constant, consistent with [2]. Using this model, the transition depth z1 and the saturated friction force f0 can be determined (Fig. 2). As expected, the sand is more frictional than glass beads. The derived parameters (f0, z1) are also consistent with previous estimates using a different method [4].
In low-gravity, the quasi-static friction is considerably lower and approximately constant with depth, in agreement with previous studies showing that the quasi-static regime is significantly reduced in low-gravity [2-4]. The consequence is that we cannot identify a clear transition depth, nor fit the quasi-static force parameters.
Different empirical scalings from impact models have been proposed to relate the drag force coefficients to the angle of repose of the material. We show in Eq. 2 the scaling from [6] for the coefficient of friction (µf) using the coefficient of the quasi-static term k, and in Eq. 3 the scaling from [2] using the equivalent f0/z1 term.
We find that the scaling of [6] (Eq. 2) overestimates µf. However, when using the model of [2] (Eq. 3) accounting for the regime transition, the 1g experimental results provide results consistent with measured values of µf. In low-g, the use of these scalings is no longer possible given the absence of the quasi-static friction regime. Therefore, to retrieve frictional properties in low-gravity, scalings based on the hydrodynamic component need to be used.
Conclusions and perspectives:
We quantify the drag force in low velocity impact experiments into granular material in both 1g and low-gravity conditions. Our results show that the quasi-static friction is considerably lower in low-gravity and approximately constant with depth, in agreement with previous studies [2-4]. We also show that the model of Sunday et al. [2], provides the most reliable estimates of the material frictional properties in 1g. However, the same approach cannot be applied in low-gravity due to the absence of the quasi-static friction term. This analysis will be expanded to additional datasets, in order to further examine the influence of friction, grain size, and gravity levels and we will also address the challenge of retrieving mechanical properties from a single impact event. Finally, new experimental data will be collected from the future variable-gravity tower at ISAE-SUPAERO [8] and will bring us means to further investigate the mechanics of regolith in low-gravity.
Acknowledgements:
We acknowledge funding support from CNES (in the context of Hera and MMX rover/wheelcam), from the French ANR Tremplin-ERC ‘GRAVITE’, and from the European Research Council (ERC) GRAVITE project (Grant Agreement N°1087060).
References:
[1] Katsuragi, H., & Durian, D. J., Nat. Phys., 2007. https://doi.org/10.1038/nphys583
[2] Sunday, C. et al., A&A, 2022. https://doi.org/10.1051/0004-6361/202142098
[3] Murdoch, N. et al., MNRAS, 2017. https://doi.org/10.1093/mnras/stw3391
[4] Murdoch, N. et al., MNRAS, 2021. https://doi.org/10.1093/mnras/stab624
[5] Sunday, C. et al., Rev. Sci. Inst., 2016. https://doi.org/10.1063/1.4961575
[6] Katsuragi, H., & Durian, D. J., PRE, 2013. https://doi.org/10.1103/PhysRevE.87.052208
[7] Kang, W. et al., Nat. Comm., 2018. https://doi.org/10.1038/s41467-018-03344-3
[8] Wilhelm, A. et al., EPSC-DPS, 1355, 2025.
How to cite: Bigot, J. and Murdoch, N.: Deriving frictional properties of regolith during small body landing, EPSC-DPS Joint Meeting 2025, Helsinki, Finland, 7–13 Sep 2025, EPSC-DPS2025-690, https://doi.org/10.5194/epsc-dps2025-690, 2025.
The Automated Lander Planetary Analog Composition Analyzer (ALPACA) is a chemical and organic composition analyzer well suited for future landed missions to ocean worlds such as Enceladus, Europa, and Titan. ALPACA utilizes the Biosignature Preparation for Ocean Worlds (BioPOW) instrument for sample enrichment (concentrating the sample) and purification to enable detections in challenging ocean world environments [1]. The BioPOW system performs the following operations: 1) melt an icy sample; 2) purify amino acids, and other organics, from salts and inorganic compounds [2]; 3) concentrate and derivatize targeted amino acids for downstream GC analyses (Figure 1). The downstream GC analyses are accomplished with a micro-electromechanical system gas chromatograph (μ-GC) [3–5]. The ALPACA instrument is being developed under NASA’s Planetary Science and Technology Through Analog Research (PSTAR) program (NASA Grant 80NSSC25K7720).
The automated, on-chip BioPOW system receives an icy sample in a sample cup that was specifically designed to interface with the CADMES arm described in NASA’s ICEE-2 program. Sample cup testing demonstrated repeatability and reliability for melting icy samples with steady heating to 100 °C in ~350 seconds using embedded nichrome wire. Once melted, fluid is transferred to the cation exchange module (ion exchange chromatography column) that is referred to as the Wet Lab of the BioPOW system. For purification and concentration of amino acids, the following steps are performed: 1) Use 10 mM hydrochloric acid (HCl) to de-protonate the resin (cation exchange beads) and provide an overall negative charge; 2) acidified sample (4OH) elutes the amino acids from the resin for subsequent derivatization and analysis (Figure 2). The eluted amino acids are moved to the second portion of the BioPOW Wet Lab, the derivatization chamber. The eluted sample is dried (60 °C until powder) and derivatized with MTBSTFA (90 °C for 1 hour) using the custom-built derivatization chamber. The derivatization chamber has three ports, one for inserting the sample and MTBSTFA, another for carrier gas, and one output which is used for water-vapor exhaust during the drying stage and for delivering the derivatized amino acid vapor to the μ-GC. Metal microfluidic tubing connects the chamber to input sources and output for the high temperatures required during derivatization. Minco® heater elements wrapped around the chamber provide the heat source.
The μ-GC utilizes silicon chip-based technology for miniaturization and consists of three main parts: 1) preconcentrator; 2) separation column; and 3) detectors. The preconcentrator is made from deep-reactive ion etching (DRIE) of silicon wafers for μ-channel ports for carbon molecular sieve (bead) loading, sample loading (preconcentration), carrier gas and vacuum pump connection, and outlet connection to the μ-GC separation column (μ-column). The molecular sieve material is used for the trapping and preconcentration of analytes with different molecular sieves targeting different analyte sizes (shown as the number of carbons in the molecule in Figure 3). The preconcentrators can employ a single sieve or multiple-sieve configurations (4-sieve configuration in Figure 3). A vacuum pump is used to pull samples across the preconcentrator where target analytes are trapped and accumulate with increased sampling time. Once sampling is completed, the vacuum pump is turned off and μ-valves switch to allow the flow of carrier gas (He) through the preconcentrator and to the μ-column. The preconcentrator is resistively heated to desorb the trapped analytes for separation on the μ-column. The μ-column is made of square spiral channels from DRIE silicon wafers of dimensions 3 x 3 cm or 3 x 6 cm for 5- and 10-m effective column length, respectively. The square spiral channels are coated with a variety of liquid stationary phases to target different analyte selectivity within each μ-column. Finally, at the exit of the μ-column, the separated analytes pass through high sensitivity μ-detectors for detection. The μ-detectors employed are a micro-photoionization detector (μ-PID) sensitive to most organics and a micro-helium dielectric barrier discharge photoionization detector (μ-HD-PID) sensitive to fixed gases (minus helium and neon). The MEMS technology significantly decreases the size, weight, and power (SWaP) of the device thereby enabling multiple μ-GCs in a single enclosure, referred to as a μ-GC suite (Figure 4) [5].
In this presentation, we focus on ALPACA hardware development and the recent advances that have been made to impact future landed missions.
References
[1] K.A. Duval, et al., Biosignature preparation for ocean worlds ( BioPOW ) instrument prototype, (2023) 2023–2032. https://doi.org/10.3389/fspas.2023.1244682.
[2] T. Van Volkenburg, et al., Microfluidic Chromatography for Enhanced Amino Acid Detection at Ocean Worlds, Astrobiology. 22 (2022) 1116–1128. https://doi.org/10.1089/AST.2021.0182/SUPPL_FILE/SUPP_DATA.PDF.
[3] R.C. Blase, et al., Experimental Coupling of a MEMS Gas Chromatograph and a Mass Spectrometer for Organic Analysis in Space Environments, ACS Earth Sp. Chem. 4 (2020) 1718–1729. https://doi.org/10.1021/acsearthspacechem.0c00131.
[4] R.C. Blase, et al., MEMS GC Column Performance for Analyzing Organics and Biological Molecules for Future Landed Planetary Missions, Front. Astron. Sp. Sci. 9 (2022) 828103 (1–19). https://doi.org/10.3389/FSPAS.2022.828103.
[5] R. Blase, et al., Biosignature detection from amino acid enantiomers with portable GC systems, Adv. Devices Instrum. (2024) 11–23. https://doi.org/10.34133/adi.0049.
Figure 1. BioPOW a) sample processing scheme, consisting of the B) sample cup, 1cm scale bar, C) wet lab manifold with cation exchange cartridge, 4 cm scale bar and D) concentration and derivatization tank, 1 cm scale bar. E) Integrated operational workflow where sample loading (gray box) is assumed performed by an external system (from [1]).
Figure 2. BioPOW system overview that 1) melts ice sample, 2) purifies amino acids, and 3) dries amino acids to concentrate and derivatizes for downstream GC analysis. 2b) Overview of steps in the cation exchange process, modified from [2].
Figure 3. Diagram of preconcentrator, from ref [5], used in μ-GC for preconcentrating analytes prior to desorption onto the μ-column for separation of complex sample mixtures.
Figure 4. μ-GC suite layout using multiple preconcentrators, μ-GCs, and μ-detectors for analyte selectivity in each μ-GC and broad chemical coverage over the entire suite.
How to cite: Blase, R., Libardoni, M., Craft, K., Bradburne, C., and Fan, X.: Automated Lander Planetary Analog Composition Analyzer (ALPACA), EPSC-DPS Joint Meeting 2025, Helsinki, Finland, 7–13 Sep 2025, EPSC-DPS2025-890, https://doi.org/10.5194/epsc-dps2025-890, 2025.
Introduction: Quantifying and mapping the precise amount of water ice in the subsurface of the Moon has proven challenging using only remote sensing data. However, this knowledge is crucial for planning long-term exploration missions that rely on the utilization of potential water reserves. Higher-resolution data and ground truth can be obtained by in-situ measurements. Instruments on stationary landers, such as ESA’s upcoming PROSPECT instrument package [1], will analyze the vertical distribution of water up to 1 m depth. In addition, rover missions promise to be highly valuable assets for lunar exploration as their mobility allows them to analyze the lateral distribution of water ice around its landing site at much higher resolution than presently possible [2].
Studies have shown that water ice in the lunar regolith can be detected using electric permittivity sensors [3,4,5]. By measuring the temperature- and frequency-dependent electric permittivity in the extremely low-frequency band, the soil’s porosity and water ice content can be deduced. Permittivity sensors of varying complexity have been part of the Rosetta/Philae [6] and Cassini/Huygens payloads [7], and a drill-based sensor will be employed in the upcoming Intuitive Machine’s IM-4 mission as part of ESA’s PROSPECT instrument package.
Rover Permittivity Sensor: For an ESA contribution to an upcoming lunar rover mission by the MBRSC (UAE), we are currently investigating the feasibility of a rover-wheel-based permittivity sensor. A prototype of this Rover Permittivity Sensor (RPS) is currently being tested at the Technical University of Munich [8]. The sensor architecture is based on the PROSPECT P-Sensor design with added sensor capabilities and circuitry adaptations. Advantages of the instrument are its simplicity, operational robustness, and low mass, power, and data requirements.
The RPS design features four distinct elements: Two electrodes mounted on one of the rover wheels, one of which is equipped with a thermometer, a coaxial pancake slip ring to transfer the signal from the rotating wheel to the static rover body, a backend electronics board performing sensor signal conditioning, acquisition, and analog processing, and a thermopile sensor to measure the surface temperature in the close vicinity of the rover.
RPS utilizes two electrodes, which allows to periodically investigate the soil’s porosity and its water-ice content, taking advantage of the mobility offered by the rover to map resources along its traverse. As the sensing depth correlates with the electrode’s size, the two electrodes have different dimensions and allow for determining the depth-dependent stratification and stability of water ice in the shallow subsurface.
The back-end electronics board includes circuitry to cycle four different excitation frequencies. For this mission, read-out of the sensor will be performed by the rover’s digital electronics, reducing the complexity and power demand of the sensor electronics.
In between the electrode and the backend electronics, a novel coaxial pancake slip ring is used to transfer the excitation and signal from the static body to the rotating rover wheel. The slip ring was specifically developed to cope with the harsh lunar environment and dust contamination while providing effective shielding for the sensitive measurement signal of the permittivity sensor.
The permittivity measurements are accompanied by measurements of the regolith surface temperature, with both, a contact temperature sensor integrated into one electrode and a thermopile sensor attached to the rover’s body. The sample temperature is a crucial parameter for the soil permittivity; therefore these additional sensors provide important contextual information for data interpretation.
Figure 1: RPS wheel architecture (second electrode obstructe).
Figure 2: Assembled electrode prototype.
Results: In the feasibility study, a fully functional prototype of RPS has been developed and evaluated. Figure 1 conceptually shows the wheel section of the instrument and Figure 2 depicts one of the wheel-mounted sensor electrodes. The results of the feasibility study confirm the measurement concept and the predicted magnitude of geometric capacitance of the novel electrodes. In addition, experiments with the thermopile temperature sensor showed that this concept is also viable for samples at cryogenic temperatures.
Conclusion and Outlook: The recent study has proven the technical feasibility of the RPS concept for lunar exploration. With a newly developed slip ring and two electrodes, mapping of water ice on the lunar surface and investigation of its stratification in the shallow subsurface is possible. A special development effort was put into selecting materials and components suitable for an ongoing development to facilitate a fast progression towards a flight instrument.
At TUM, a specialized dusty thermal vacuum system is being assembled to assess electrode robustness, slip ring dust tolerance, and combined system performance in a representative environment and with icy regolith simulant. Refined and extensive testing of the thermopile sensor is also foreseen as a next step.
In addition to their application on lunar rovers, similar permittivity sensors could also be integrated into other exploration systems, such as penetrators or landers, to provide valuable context information about the soil properties and potential water-ice content.
Acknowledgement: The RPS Feasibility Study was funded by ESA.
References: [1] Trautner, R et al. (2025), Frontiers in Space Technologies, 5 [2] Gscheidle, C. et al. (2022), PSS, 212, 105426 [3] Nurge, M. A. (2012) PSS., 65, 76-82 [4] Trautner, R. et al. (2021) Meas. Sci. Technol, 32, 125117 [5] Gscheidle, C. et al. (2024) Frontiers in Space Technologies, 4 [6] Seidensticker, K. J. et al. (2007) Space Sci. Rev., 128, 301-337 [7] Grard, R. et al. (2006) PSS, 54, 1124-1136 [8] Trautner, R. et al. (2024) Euro. Lunar Symposium, Dumfries, UK
How to cite: Gscheidle, C., Eckert, L., Sesko, R., Trautner, R., and Reiss, P.: Feasibility of a Rover-wheel-based Permittivity Sensor for Prospecting Lunar Water, EPSC-DPS Joint Meeting 2025, Helsinki, Finland, 7–13 Sep 2025, EPSC-DPS2025-922, https://doi.org/10.5194/epsc-dps2025-922, 2025.
Introduction: Methane observed in the Martian atmosphere is not stable on geological timescales, suggesting a source on Mars that is active today [1]. Several theories have been proposed that link this atmospheric methane to emission from ancient subsurface reservoirs (such as methane hydrate clathrates) [2,3], hot water/rock reactions (e.g. Fisher Troph-Type Processes) [4], exogenous input of organic material (e.g. Interplanetary Dust Particles), [5] or biological activity [4]. However, methane is currently measured in the near-surface atmosphere only a few times per year, making testing these theories challenging [6, 7].
Methane on Earth is primarily biological, making the methane cycle on Mars of great astrobiological interest. This instrument could also provide important thermochemical data about the Martian subsurface[1].
Figure 1: The ABB breadboard prototype spectrometer.
Figure 1: The schematic for the experimental breadboard spectrometer.
The Martian Atmospheric Gas Evolution (MAGE) experiment was proposed to provide the data needed to distinguish between different theories of the origin and evolution of methane in the Martian atmosphere. A small gas spectrometer, based around the ABB Integrated Cavity-enhanced Optical Spectroscopy (ICOS), is a Canadian instrument that could be deployed to sample the concentration of atmospheric methane on an hourly basis [3].
Methodology: For this work, we use an experimental ICOS spectrometer which represents a prototype of a future spectrometer capable of flight to Mars. Images of the breadboard instrument (Figure 1) and its schematic (Figure 2are shown. The prototype spectrometer contains 2 lasers, a 1659 nm laser analyzing concentrations of 12CH4 and 13CH4, and a 1650 nm laser analyzing concentrations of 12CH4 and CO2.
The prototype spectrometer’s 1659 nm laser was tested using with a balance gas of carbon dioxide. Mass flow controllers were used to make methane concentrations of 1000 ppmv and 10 ppmv. Tedlar bags were used to make theoretical concentrations of methane of 1 ppmv and 10 ppbv by serial dilution. As there was difficulty obtaining a concentration of methane below 10 ppm due to methane contamination of the CO2 background gas, the runs of 10 ppm, 1 ppm, and 10 ppb are considered equivalent due to background gas contamination. The graphs below show the raw data, processed data with a uniform window filter of size 10 applied, and the data with the drift removed by subtracting the filtered data from the raw data. A mean line goes through the center of the graph. The integration times discussed further in the results section are calculated using the results with the drift removed.
Figure 3: Concentrations of 12CH4 from the prototype spectrometer
Figure 4: Concentrations of 13CH4 from the prototype spectrometer.
Results: As the seasonal background of methane on Mars ranges in concentration from 0.2-0.7 ppbv, with periodic high spikes of up to 45 ppbv [7], sensitivity in this range would make a flight instrument based on ours suitable for the detection of methane. The instrument takes an average of 16 hours of operation to reach 95% confidence that the concentrations of 13CH4 detected are within 1 ppb of the true value, while it takes an average of 56,647 hours to reach 95% confidence that the values of 12CH4 are within 1 ppb of true values. The significant drop in integration time when observing 13CH4 vs 12CH4 is due to a more stable signal response at lower concentrations of gas. The results from the 10 ppm, 1 ppm and 10 ppb runs are averaged to obtain the operational times discussed above, as they are considered replicas. The long measurement time is largely due to instability caused by noise within the instrument, clearly seen in the results for both 12CH4 and 13CH4 (Figure 3 and Figure 4).
Future Work: Future modifications include testing under Mars-like conditions, including testing the instrument with Mars analogue mixtures of gases. This will include pressure changes in the atmosphere to observe how the limits of detection change. If deployed on a Mars rover, or on a rotorcraft, the instrument could help to constrain the potential sinks and sources of Martian methane.
Acknowledgments: This work was funded by the Canadian Space Agency FAST program grant No, 19FAYORA13 and is being pursued in collaboration with ABB Inc.
References: [1] Atreya, S. K., Mahaffy, P. R., & Wong, A.‐S. (2007). Planetary Space Science, 55(3), 358–369. [2]Stevens, A. H., Patel, M. R. & Lewis, S. R. Icarus 281, 240–247 (2017). [3] Lasue, J., Quesnel, Y., Langlais, B. & Chassefière, E. Icarus 260, 205–214 (2015). [4] Oehler, D. Z., & Etiope, G. (2017)., 17(12), 1233–1264.1657 [5] Schuerger, A.C., Moores, J., Clausen, C., Barlow, N., Britt, D., 2012. J. Geophys. Res. Planets 117, E08007.. [6] Webster, C. R., Mahaffy, P. R., Atreya, S. K., Moores, J. E., Flesch, G. J., Malespin, C., et al. (2018). Science, 360(6393), 1093–1096. [7] Moores, J.E., Sapers, H.M., Oehler, D., Newman, C. and Whyte, L. (2021) 8 pp. published in Bulletin of the American Astronomical Society, Vol 53, issue 4. https://baas.aas.org/pub/2021n4i125/release/1
How to cite: Newton, A., Axelrod, K., Moores, J., and Sapers, H.: Laboratory Evaluation of a Methane Spectrometer for the Martian Atmosphere, EPSC-DPS Joint Meeting 2025, Helsinki, Finland, 7–13 Sep 2025, EPSC-DPS2025-1119, https://doi.org/10.5194/epsc-dps2025-1119, 2025.
The boulder shape at the surface of small bodies enables the investigation of the geological processes the surface boulders have undergone, and the estimation of certain mechanical properties (e.g. [1]). Studies conducted on surface boulders observed on extraterrestrial planetary bodies are usually performed using 2D images of the projected surface [1, 2, 3, 4]. 2D projections of irregular particle are known to differ from their 3D geometry [5, 6, 7], therefore, careful precautions are required when comparing different surfaces and, in particular, different representations (2D or 3D).
To investigate how representative 2D images of boulders are for estimating bulk 3D morphological parameters, three different granular samples (LHS-1 Lunar Highland Simulant, Øysand soil, and glass grit) were scanned by XCT (X-ray Computed Tomography) to reconstruct the 3D geometry of each particle. The shapes of more than 2500 particles have been analyzed using both the 3D geometry and different 2D projections from the 3D reconstruction. The 2D shape analysis pipeline used in this study was previously applied in Robin et al. (2024) [1] and Kohout et al. (2024) [8]. A 3D shape analysis has been extended using the same methodology as the 2D analysis.
The shape can be expressed using three independent descriptors based on the observation scale [9, 10]: the form (large scale), roundness (intermediate scale), and surface texture (small scale). The morphological parameters measured in this study include axes ratios and sphericity (large-scale measurements), and roundness. In addition to the shape analysis, the size of particles is also computed (fig. 1).
Figure 1: Definitions of size and shape descriptors measured for 2D (left) and 3D (right) representations.
Using the 2D and 3D morphological analysis of the XCT scan dataset, correlation formulas with confidence intervals have been established between both representations to estimate 3D morphological parameters from 2D measurements.
Our methodology to estimate 3D parameters from 2D measurements is being tested with boulders observed on asteroid Bennu by the NASA OSIRIS-REx mission (fig. 2). Boulders on Bennu have been observed by different instruments; in 2D by OCAMS (OSIRIS-REx Camera Suite) [11], and in 3D by OLA (OSIRIS-REx Laser Altimeter) [12]. The results of the comparison between the estimated 3D parameters obtained with the 2D images and the 3D analysis from the same boulders observed with OLA will be presented during the conference in addition to the analysis of XCT scan data.
Figure 2: An example of boulders observed on asteroid Bennu by different instruments during the NASA OSIRIS-REx mission. OLA measurements (left) provide the surface topography (3D), and the same surface has also been observed in 2D with OCAMS (right). (Credit: NASA/Goddard/University Of Arizona)
References
[1] Robin, et al. (2024). Mechanical properties of rubble pile asteroids (Dimorphos, Itokawa, Ryugu, and Bennu) through surface boulder morphological analysis. Nature communications, 15: 6203.
[2] Yingst, et al. (2007). Quantitative morphology of rocks at the Mars Pathfinder landing site. Journal of Geophysical Research: Planets, 112.
[3] Cambianica, et al. (2019). Quantitative analysis of isolated boulder fields on comet 67P/ Churyumov-Gerasimenko. Astronomy & Astrophysics, 630:15.
[4] Jawin, et al. (2023). Boulder Diversity in the Nightingale Region of Asteroid (101955) Bennu and Predictions for Physical Properties of the OSIRIS-REx Sample. Journal of Geophysical Research: Planets, 128:12.
[5] Jia et al. (2023). Sphericity and roundness for three-dimensional high explosive particles by computational geometry. Computational Particle Mechanics, 10:817-836.
[6] Beemer, et al. (2022). Comparison of 2D Optical Imaging and 3D Microtomography Shape Measurements of a Coastal Bioclastic Calcareous Sand. Journal of Imaging, 8:72.
[7] Zheng, et al. (2021). Three-dimensional Wadell roundness for particle angularity characterization of granular soils. Acta Geotechnica, 16:133-149.
[8] Kohout, et al. (2024). Impact Disruption of Bjurböle Porous Chondritic Projectile. The Planetary Science Journal, 5:128.
[9] Barrett (1980). The shape of rock particles, a critical review. Sedimentology, 27:3.
[10] Cho, et al. (2006). Particle Shape Effects on Packing Density, Stiffness, and Strength: Natural and Crushed Sands. Journal of Geotechnical and Geoenvironmental Engineering, 132:5.
[11] Rizk, et al. (2018). OCAMS: The OSIRIS-REx Camera Suite. Space Science Reviews, 214:26.
[12] Daly, et al. (2017). The OSIRIS-REx Laser Altimeter (OLA) Investigation and Instrument. Space Science Reviews, 212:899-924.
Acknowledgements
This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement N°870377 (project NEO-MAPP), and CNES in the framework of the Hera mission and the MMX rover/wheelcams. A.D. acknowledges PhD funding from Université de Toulouse III. C.R. acknowledges PhD funding from CNES and ISAE SUPAERO. O. S. Barnouin’s and R. L. Ballouz’s efforts were funded by the NASA New Frontier Data Analysis Program under grand number 80NSSC22K1035 P00004.
How to cite: Duchêne, A., Murdoch, N., Robin, C., Mikesell, T. D., Barnouin, O. S., Ballouz, R. L., and Jerves, A. X.: Boulder shape analysis: is a 2D projection reliable for capturing the 3D geometry?, EPSC-DPS Joint Meeting 2025, Helsinki, Finland, 7–13 Sep 2025, EPSC-DPS2025-114, https://doi.org/10.5194/epsc-dps2025-114, 2025.
I. INTRODUCTION
The Chandrayaan 3 (C3) mission was developed and launched by the Indian Space Research Organization (ISRO) in July 2023. After the failure of Chandrayaan 2 lander in 2019, the goal of Chandrayaan 3 was to achieve the objectives of its predecessor, i.e., demonstrate full capability in safe landing, roving, and conducting in situ experiments on the lunar surface. This mission contained a lander including a 26-kg rover named Pragyan that carried the Low-energy Eye-safe Laser-Induced Breakdown Spectroscopy (LE-LIBS) instrument. The laser operates at 1540 nm, delivering 20 pulses at 5 Hz, with an energy between 3 and 4 mJ per pulse on target.
During its 100-meter traverse (Fig. 1), the rover conducted 66 LIBS investigations. At each of those locations, there are between 1 and 10 LIBS spectra. The results have been studied in this work and compared with the preliminary results of Sridhar et al., (2025) [1].
Fig. 1: Path of Pragyan rover (from ISRO).
II. DATA PROCESSING
The C3 archive [5] contains both raw data (level-0) and wavelength-calibrated data (level-1). Starting from level-0 data, we built our own calibration. Data were first de-noised with the Undecimated Wavelet Transform method (UWT) [3], followed by baseline removal with Asymmetrically Iterative Reweighted Penalized Least Squares (AIRPLS) function [4]. We found that our results do not match level-1 data provided by the C3 team [5] with unexplained discrepancies up to 8 nm. Thus, for the rest of the study, we used our own calibration, starting from level-0 data.
Next, emission peaks were fitted with a Lorentzian function that led to peak identification and resolution from wavelength and full width at half-maximum (FWHM), respectively. The NIST database was used for peak element identification.
III. RESULTS
We have processed all 66 investigations, and when significant, classified the results (see Fig. 2 below) on the basis of Signal to Noise Ratios (SNR) and noise levels. We then focused on Target 6 to compare our element identification results with those reported in [1], and subsequently with Target 19.
A. Spectra classification
We have developed a Python tool to process 34 of the 66 operations, which are considered by the C3 team as significant for science, resulting in 170 LIBS spectra Those spectra were compared and classified based on their main characteristics, i.e., noise and SNR. SNR is defined as the ratio of the maximum peak amplitude versus the standard deviation of the signal between 680-760 nm, a zone considered as pure noise. With this information, we found that 13 of the 66 investigations have the best signal with the most peaks (tens to hundreds of peaks as expected in LIBS spectra [2]) and are therefore scientifically usable.
Fig 2: Operations classification regarding the noise and the SNR of the strongest peak. Data from operations 14, 21, 22, 23, 24, and 47 are damaged and not shown. The 13 optimal investigations are circled in red.
B. Comparison with Sridhar et al., (2025) [1]
Target 6 reported in Sridhar et al., (2025) [1], was acquired on August 27, 2023 at 18:12.23 UT. Figure 3 shows our processing of the LIBS spectrum, which we compared with the one processed by the C3 team.
Both spectra appear similar, but differences arise after removing the baseline and fitting the peaks. The same number of peaks could be fitted in both of the studies, however, our element identification based on NIST database do not fully agree with the results in [1].
We have identified 28 peaks. 11 match in wavelength and element identification results of [1]. We found that 17 do not match in wavelength (i.e., shifted by more than 1 nm). Specifically, 6 do not match in element identification because another element is more probable while the other 11 do not have an unequivocal element candidate. This means those peaks do not match regarding the element in the NIST database, or the presence of the element is in doubt because the major emission peaks of this element are missing (e.g., manganese, chromium, titanium). Finally, some elements like silicon, iron and aluminum are clearly present in this sample, but among the few peaks attributed to those elements some do not include the most intense peaks like the one attributed to Si I at 792 nm. Our wavelength recalibration did not change these observations.
Fig. 3: Spectrum of Target 6 derived in this work. Some peaks could not be associated with specific elements, as no unequivocal correspondence using NIST could be established.
C. Comparison with Target 19
The measurements of target 19 took place 2 days after Target 6, and their spectra are both clean and strong (Fig. 2). Thus, the comparison can remove doubt and confirm peak wavelengths. It appears that the spectra of these targets are very similar, which confirms that the same regolith was sampled in both cases. We will continue to investigate other targets to confirm the presence of additional elements beyond Fe, Al, Mg, Ca, O, and Si.
IV. CONCLUSION
Chandrayaan-3 has demonstrated that LIBS can work on the Moon, despite challenges with the current setup (no active refocus) to systematically acquire a meaningful signal. One of the next steps is to investigate the origin of the observed nonlinear wavelength shifts. A possible explanation is an issue with the beam focus, which we are currently investigating in our laboratory.
REFERENCES
[1] Sridhar, R. V. L. N., et al. « Chandrayaan-3 LIBS Sensor: Preflight Characterization, Inflight Operations, and Preliminary Observations ». IEEE Sensors Journal 25, no 2 (2025): 2554‑66. https://doi.org/10.1109/JSEN.2024.3506037.
[2] Wiens, Roger C., Sylvestre Maurice, et al. « The SuperCam Instrument Suite on the NASA Mars 2020 Rover: Body Unit and Combined System Tests ». Space Science Reviews 217, no 1 (2021): 4. https://doi.org/10.1007/s11214-020-00777-5.
[3] Starck, Jean-Luc, et al. « The Undecimated Wavelet Decomposition and its Reconstruction ». IEEE Transactions on Image Processing 16, no 2 (2007): 297‑309. https://doi.org/10.1109/TIP.2006.887733.
[4] Baek, Sung-June, et al. « Baseline Correction Using Asymmetrically Reweighted Penalized Least Squares Smoothing ». The Analyst 140, no 1 (2015): 250‑57. https://doi.org/10.1039/C4AN01061B.
[5] pradan.issdc.gov.in/ch3/
How to cite: Mourlin, F., Rapin, W., Maurice, S., Lasue, J., Forni, O., Cousin, A., and Meslin, P.-Y.: Chandrayaan 3 data: Unveiling the challenges of performing LIBS on the Lunar Surface, EPSC-DPS Joint Meeting 2025, Helsinki, Finland, 7–13 Sep 2025, EPSC-DPS2025-704, https://doi.org/10.5194/epsc-dps2025-704, 2025.
Introduction: With the possible return of mankind to the Moon comes the need for detailed analyses of ambient conditions in possible landing regions. Several missions (e.g. IM-2, Blue Ghost M1) have already landed in the Moon’s south pole region to study the environmental conditions, and more are planned. Of particular interest are temperature measurements as they can be used to draw conclusions about the material properties of the regolith such as the surface roughness or the thermal inertia. However, any lander affects the temperature of the ground around it. This effect is particularly strong in the polar regions. Here, the ground is hit by sunlight at a large angle, while for the sides of the lander the incidence angle can be very small. Consequently, a relatively large amount of light can be scattered from the lander to the ground increasing the temperature there. Furthermore, the lander may heat up more strongly, which (depending on the emissivity of the lander's surface) could lead to comparatively large thermal emissions. This study investigates the temperature change caused by a lander located at a latitude of -84.7715° and a longitude of 29.21994°, close to the landing site of Intuitive Machines' IM-2 mission.
Methods: The lander is modeled as a cube with a side length of 1m. To disentangle the influence of the lander from the influence of the local topography, the lander's neighborhood is modeled by a plane. Both the cube and the plane are modelled using triangular meshes with side lengths of 50 cm and 12.5 cm, respectively. We assume that the lander's surface is either covered by black paint (solar albedo 0.04, IR emissivity 0.88) or by Multi-layer Insulation (MLI, solar albedo 0.95, IR emissivity 0.05). For the sake of simplicity we assume, for now, that the lander has a constant temperature of 250 K. With regard to the material properties of the ground we adopt the values of the Oxford Thermal Model [1], except for the solar albedo, which we assume to be constant at 0.12. Regarding scattered light we currently consider only single-scattering. First the surface temperature of the undisturbed plane is determined for epoch 2025-03-07T12:00:00 UTC. On this date the lander is placed on the surface. For all subsequent times the ground temperature is computed taking the lander's presence into account.
Results: Figures 1-4 show temperature curves for a location about 33 cm in front of the lander, where the corresponding lander side is directed to the North. The black curve shows the temperature without the lander, the orange curve shows the temperature when single-scattering and the red curve when single-scattering and thermal emission from the lander are taken into account. Figure 1 compares the temperature curves during one month for a lander whose surface consists of black paint. Due to the low albedo, single scattering only increases the temperature of the ground by a maximum of 7K. The thermal emissions from the lander, on the other hand, increase the temperature of the ground by up to 25 K. At the same time, the ground does not cool down as much during the night as it would without the lander's presence. Figure 2 provides a closer look at the first two hours after landing and demonstrates that the ground responds quickly to the lander's presence.
Figure 3 shows the temperature curves over the course of one month for a lander whose surface consists of MLI. Due to the lander's large albedo, single scattering increases the temperature of the ground by up to 90 K. However, due to the lander's small emissivity, thermal emissions from the lander are negligible during the daytime. At night, they prevent the ground from cooling down as much as it would without the lander. Again, Figure 4 shows the first two hours after landing in more detail.
Figure 1: The temperature at the North side of the lander at a distance of 33 cm for a lander covered with black paint. Black: Temperature without the lander's presence. Yellow: Single-scattering included. Red curve: Single-scattering and thermal emissions included. Before the arrival of the lander at around 8:25 am local time, the three curves are identical. Around 2 pm an eclipse occurs.
Figure 2: The temperature at the North side of the lander at a distance of 33 cm for the first two hours after the landing. For an explanation of the color code see Figure 1.
Figure 3: The temperature at the North side of the lander at a distance of 33 cm for a lander covered with MLI. For an explanation of the color code see Figure 1. Before the arrival of the lander at around 8:25 am local time, the three curves are identical. During daytime the orange and the red curve are identical, too. Around 2 pm an eclipse occurs.
Figure 4: The temperature at the North side of the lander at a distance of 33 cm for the first two hours after the landing. For an explanation of the color code see Figure 1.
Summary and Outlook: If the temperature of the regolith is measured, the presence of the lander must be taken into account, especially if conclusions are to be drawn about the material properties from the temperature.
The next steps are to increase the resolution of the meshes and run our code in multi-scattering mode. Moreover, the lander's temperature will be calculated more accurately. We also plan to study a landing site at the Shackleton - de Gerlache ridge and the case of a lander situated inside a permanently shadowed region.
Acknowledgements: LRAD is supported by the German Federal Ministry for Economic Affairs and Climate Action on the basis of a decision by the German Bundestag. Grant: 50OW2103
References: [1] King et al. (2020), PSS 182. doi:10.1016/j.pss.2019.104790
How to cite: Ziese, R., Knollenberg, J., Hamm, M., Grott, M., and Rauer, H.: The influence of a lunar lander on the temperature of the surrounding surface, EPSC-DPS Joint Meeting 2025, Helsinki, Finland, 7–13 Sep 2025, EPSC-DPS2025-1398, https://doi.org/10.5194/epsc-dps2025-1398, 2025.
The Tumbleweed Mission proposes a cost‑effective swarm of over 90 wind‑driven spheroidal rovers to acquire extensive spatio‑temporal datasets across the Martian surface [1]. Built from modular, mass‑producible components and off‑the‑shelf parts, these 5-meter-major-diameter box‑kite rovers leverage wind for locomotion, reducing mission cost and complexity. Our previous simulations indicate that a randomly deployed swarm could average more than 400 km of travel per individual rover within 100 sols, despite individual unit losses due to terrain obstacles or system faults [2]. Thus, this mission will significantly enhance our understanding of Mars, facilitating future human exploration and settlement.
Figure 1 - Proposed Mission Architecture: mid-air deployment (1), landing (2), rolling towards the equator (3) and stationary phase (4).
Given the unconventional mission architecture (figure 1) and unprecedented capabilities of the platform, the Tumbleweed rover embodies a technology‐driven paradigm for in situ exploration, decoupling sensor development from predefined science objectives. In order to quantify and maximize the anticipated science return of the mission, we have implemented an optimization framework balancing scientific utility per instrument against SWaP (Size, Weight, and Power) constraints. Through this payload pre-selection framework, we have identified a high‑TRL instrument suite [3], comprising of atmospheric sensors (pressure, temperature, relative humidity, electric field, dust), a triaxial fluxgate magnetometer, a hand‑lens imager, a multispectral stereoscopic camera, radiation and neutron spectrometers, and a soil permittivity sensor.
Exploring the synergies amongst our pre-selected list of instruments, we arrived at the opportunity to use multispectral cameras, radiation and neutron spectrometers, and a triaxial fluxgate magnetometer to characterise Martian Habitability across the traversed paths of individual rovers.
Herein, Martian Habitability not only refers to scouting before the first human footsteps on Mars [4] but also to studying the Martian radiation environment [5] as well as its impact on potential biosignatures [6]. As such, we define areas of interest for the assessment of present-day Martian Habitability as the intersection of (a) low radiation exposure, (b) local availability of water/H‑bearing materials, and (c) magnetospheric shielding. These areas could constitute niches conducive for preservation of potential biosignatures. Current knowledge gaps relate to:
- Spatio-temporal variation characteristics of ionizing radiation and its interaction with the atmosphere, regolith, and local magnetic fields;
- Abundance of carbon, water equivalent hydrogen (WEH), and other biologically important (CHNOPS) elements near the surface;
- Existence of discrete mini-magnetospheres capable of locally modulating the radiation environment by measurably deflecting incoming charged particles;
- Characterisation of environmental and topographical hazards.
All these knowledge gaps can be probed by a synergistic suite of instruments on-board a multi‑rover swarm of Tumbleweeds. For example, the neutron spectrometer shall continuously measure both fast and epithermal neutrons produced when Galactic Cosmic Rays (GCRs) and Solar Energetic Particles (SEPs) interact with the regolith, enabling high‐resolution WEH mapping along each rover’s path [7]. Neutron measurements will be complemented by continuous measurements of soil permittivity using patch electrodes-based sensors [8]. Meanwhile, the fluxgate magnetometer shall record vector magnetic fields at every stop and during locomotion, detecting localized magnetic anomalies that can act as mini‑magnetospheres [9].
Moreover, the multispectral stereoscopic camera will play a crucial role in hazard assessment and geological reconnaissance. By capturing high‑resolution stereo pairs across visible and near‑infrared bands, the camera shall generate digital elevation models to identify slopes, rock hazards, and dust‑accumulation zones—vital data for planning and selecting human landing sites. Spectral indices derived from the same imagery shall reveal mineralogical variations (iron‑oxide, clay, and sulfate signatures) that trace aqueous alteration processes, pinpointing locations where past water activity may have created habitable niches.
Fusing neutron spectrometer data with magnetic‑field measurements, we will generate spatio‑temporal maps of radiation dose rates, hydrogen content, and magnetic shielding strength. These integrated datasets pinpoint radiation “cold spots” co‑located with elevated WEH—locations where mini‑magnetospheric shielding and subsurface hydrogen may coincide. Such sites are prime candidates for astrobiological investigations and future human landing zones, offering potential water reservoirs and enhanced natural protection from ionizing particles.
Deployed as a distributed swarm, each Tumbleweed rover will continuously sample radiation flux, hydrogen abundance, magnetic anomalies, topography, and surface composition over hundreds of kilometers, collectively generating habitability maps that bridge the gap between orbital surveys and point measurements. This multi‑dimensional dataset will not only resolve small‑scale heterogeneities in Martian environmental conditions but also guide the selection of optimal sites for both astrobiological investigations and future human exploration.
Figure 2 – Pink Lady in the Negev Desert, credit to OEWF/AMADEE20.
Presently, development of scientific use-cases requires proof of feasibility on the Tumbleweed rover and simultaneously of individual instruments aboard it. Although several prototypes have been developed to some success (figure 2) [10], the ability of the Tumbleweed Rovers to produce environmental data both statically and dynamically must be proven – specifically on extreme environments and in Martian analogous experimental set ups.
Figure 3 - First iteration of the Science Testbed under development in the Delft (Netherlands) workshop.
We have recently developed a reusable platform that enables the proposed suite of instruments to be tested: the “Tumbleweed Science Testbed” (figure 3) [11]. This is a sub-scale rover prototype with a major diameter of 2.7 meters, equipped with a 1U cuboid payload bay which provides modular interfaces to a variety of COTS and bespoke payloads. In addition to the payload bay, modestly sized sensors can also be incorporated on the structure, providing opportunities for contact-based measurements and vertical profiling relevant for atmospheric sciences.
The next phase in our technological development roadmap involves testing an iteration of the Tumbleweed Science Testbed in a ‘dirty’ wind tunnel able to reproduce the conditions found at the surface of Mars. Under this controlled environment, we aim to characterize the rolling dynamics, wind and dust interaction, and locomotion of the rover extensively. These developments are envisioned to raise the Technology Readiness Level (TRL) of our platform towards levels 5 to 6. Additionally, planning for field trials in deserts and in polar environments is currently underway.
[1] - https://doi.org/10.5194/egusphere-egu24-20149
[2] – Renoldner, 2023.
[3] - https://doi.org/10.5194/epsc2024-1103
[4] - https://doi.org/10.5194/epsc2024-790
[5] - https://doi.org/10.5194/egusphere-egu25-19534
[6] – Shanbhag, 2023.
[7] - https://doi.org/10.1016/j.icarus.2021.114805.
[8] - Gscheidle, 2024.
[9] - Maxwell, 2023.
[10] - Rothenbuchner, 2022.
[11] - https://doi.org/10.5194/egusphere-egu25-18773
How to cite: Kingsnorth, J., de Pinto Balsemão, M., Shanbhag, A., Pikulić, L., Moisuc, C., Bounova, G., Peterson, M., and Rothenbuchner, J.: A Swarm of Wind-Driven Tumbleweed Rovers for in-situ Mapping of Radiation, Water‑Equivalent Hydrogen and Magnetic Fields on Mars, EPSC-DPS Joint Meeting 2025, Helsinki, Finland, 7–13 Sep 2025, EPSC-DPS2025-1779, https://doi.org/10.5194/epsc-dps2025-1779, 2025.