Processors

Flight processors selected by flown planetary and orbital robotics programs, with the flight or ground result that establishes each one.

The governing tradeoff for the class is throughput against upset behavior, and it shows up in the duty cycle of autonomy rather than in a datasheet. On the Mars Exploration Rovers a single visual odometry step took an average of nearly three minutes on the 20 MHz RAD6000, so the technique was commanded only on short drives, on slopes above about 10 degrees, or when a wheel was being dragged [1].

Flight processors selected and flown

Part	Manufacturer	Used by	Source
RAD6000	BAE Systems	spirit, opportunity	[1]
RAD750	BAE Systems	curiosity, perseverance	[2][3][4]
LEON3FT	Cobham Gaisler	perseverance (SuperCam)	[5]
TMS320C6701	Texas Instruments	spheres	[6][7]
Snapdragon 801	Qualcomm	ingenuity	[8][9]
80386SX	Intel	ISS multiplexers	[10]
ThinkPad 760XD, T61p	IBM, Lenovo	ISS crew laptops	[10]

Ratings and qualification results

RAD6000, BAE Systems: a radiation-hardened 32-bit POWER single-board computer [1]. Ratings: 20 MHz as flown on MER; single CPU shared with the camera bus [1]. Qualification: Flown from January 2004. Each visual odometry step averaged nearly three minutes of computation, restricting the technique to drives typically under 15 m and to steps of no more than 75 cm or 18 degrees of turn [1].
RAD750, BAE Systems: a radiation-hardened PowerPC 750 single-board computer [2]. Ratings: MSL Rover Compute Element carries 512 MB RAM under VxWorks, two units in prime and hot backup [2][3]; measured 200 to 300 MIPS on the Mars 2020 vehicle, of which the onboard scheduler receives only a fraction [4]. Qualification: On Curiosity a failing region of RCE-A NAND flash forced a swap to RCE-B on sol 201. The RCE-B data product partition then failed to mount on sol 2173, forcing a return to RCE-A on sol 2188 until RCE-B was reformatted on sol 2342. RCE-B has carried 20,159.565 m of odometry against 1,158.793 m on RCE-A [2].
LEON3FT, Cobham Gaisler. Fault-tolerant SPARC V8 processor with SpaceWire, in the SuperCam Body Unit. The source names the core designer; the vendor of the flight part is not named [5]. Ratings: 50 MHz; four SpaceWire interfaces, one of them mapped to the Mast Unit at 30 MHz [5]. Qualification: Flown since February 2021. Paired with a Cobham Aeroflex start-up PROM that can accept a limited command set from the rover compute element and rewrite corrupt program memory on Mars, so a memory corruption is recoverable rather than terminal [5].
TMS320C6701, Texas Instruments, on a Sundance SMT375 carrier board. Floating-point DSP. Ratings: Carrier board also holds a Xilinx Spartan II XC2S200 and a MAX1294 converter [6]. Qualification: Three units launched to the ISS in 2006 and had conducted more than 70 test sessions by 2015 [7].
Snapdragon 801, Qualcomm: a commercial smartphone system on chip, not radiation hardened [9]. Ratings: Commercial part, no radiation qualification claimed [9]. Qualification: Completed 72 flights on Mars between April 2021 and January 2024 [8]. Obsolescence rather than radiation ended its use: the part is no longer available in the condition and quantity required, so the Sample Recovery Helicopter design moved to a newer Snapdragon [9].
80386SX, Intel, in the Input/Output Control Unit card: a radiation-screened commercial microprocessor in an ISS Multiplexer/Demultiplexer, the command and data handling bus that hosts canadarm2 and dextre [10]. Ratings: Each IOCU card carries the processor, a 1553 bus interface and 33,554,432 bits of DRAM; Hamming single-error-correction double-error-detection runs inside the DRAM refresh cycle, holding bad-bit residence below 10 microseconds [10]. Qualification: Flown in a three-tier scheme: tier 1 two-fault tolerant on three identical computers, tier 2 one-fault tolerant on two, tier 3 zero-fault tolerant with redundancy obtained by allocating software across computers. Sixteen probable lock-up single event functional interrupts were logged across the fleet between 2001 and 2015, each costing ground controllers 12 to 24 hours to power cycle, reboot and resynchronize, and the rate per computer per year stayed well below the pre-flight prediction for the whole 14 years [10].
ThinkPad 760XD, T61p, IBM and Lenovo: unmodified commercial laptops flown as the ISS Portable Computer System, the crew and payload interface [10]. Ratings: No radiation hardening; screened by high-energy proton testing before flight [10]. Qualification: In-flight single event functional interrupt rate measured at 0.023 +/- 0.012 per day for three 760XDs over 2001, against 0.04 per day predicted from proton testing, and 0.013 +/- 0.004 per day for seven T61p units over 2011 to 2014 against 0.13 per day predicted. Proton-beam prediction overestimated the flight rate by a factor of about two and about ten respectively. Two of the seven T61p units suffered hard failures that may or may not be radiation induced [10].

Reading the clock and rate figures

The RAD750 row covers three programs, and no single clock is published across them. The Mars 2020 build was measured at 200 to 300 MIPS [4]. The part is a family, and a clock figure is only meaningful against a specific board.

The ISS laptop rows are the only entries in the class with a matched pre-flight prediction and an in-flight measurement of the same assembled hardware.

Article and unit count	Period on orbit	Ground prediction	Flight measurement	Ratio
IBM 760XD ThinkPad, 3 units	March to December 2001	0.04 SEFI/day	0.023 +/- 0.012 SEFI/day	1.7x high
Lenovo T61p, 7 units	2011 to 2014, 877 to 1088 days each	0.13 SEFI/day	0.013 +/- 0.004 SEFI/day	10x high

Source: [10].

Both predictions came from 200 MeV proton testing of assembled articles [10]. Screening was conservative in both cases, and the two conservatism factors differ by a factor of six on the same method, so a redundancy scheme sized on a predicted rate is sized on an unknown margin. Non-radiation functional interrupts on the T61p campaign were comparable in number to the radiation ones and had to be screened out by judgment, and two of the seven units suffered hard failures of unknown cause [10]. The piece-part comparison from the same campaign, on DRAM, is on Memory and Storage, where the two predictions land within 2.7 and 2.9 times: predicting an assembled article buys coverage of the whole machine and costs the tightness the piece-part method keeps.

Parts characterized but not yet flown on a planetary robot

Radiation characterization of commercial and radiation-tolerant processors published by the NASA Electronic Parts and Packaging program and by mission avionics teams. These are test results against a named beam and facility, not flight heritage.

Part	Manufacturer	Facility and date	Source
RH-OBC-1	Vorago Technologies	Massachusetts General Hospital, June 2019	[11]
Ryzen 7 1700, Ryzen 3 1200, Ryzen 3 2200G	AMD	Massachusetts General Hospital, June 2019	[11]
Jetson TX2	NVIDIA	Massachusetts General Hospital, June 2019	[11]
Jetson TX2i	NVIDIA	Johns Hopkins Applied Physics Laboratory, 2021	[12]
MSP430	Texas Instruments	In-house dose testing, 2021	[12]
ISIS On-board Computer	Innovative Solutions in Space	No new testing	[12]
Coral USB accelerator	Google	No radiation testing	[13]

Ratings and campaign results

RH-OBC-1, Vorago Technologies. Radiation-hardened CubeSat Kit single-board computer built on the VA10820 ARM Cortex-M0 core, with regulators, ADC, watchdog and CAN controller. Tested at the Francis Burr Proton Therapy Center at Massachusetts General Hospital [11]. Ratings: Core memory carries EDAC; board carries two Cypress FRAM devices as boot and user memory. Qualification: 200 MeV proton testing at board and component level [11]. SRAM single-bit upset cross section 3e-8 cm2/device, ROM 1.3e-7 cm2/device, no multi-bit upsets. 3e11 p/cm2 and about 12.2 krad(Si) accumulated with no parametric degradation [11]. Both FRAM devices took functional interrupts, and the board has no way to gate their power, so a boot FRAM SEFI leaves the microcontroller unable to reload boot code until an external power cycle [11]. The failure was not visible in piece-part testing.
Ryzen 7 1700, Ryzen 3 1200, Ryzen 3 2200G, AMD: commercial multi-core x86 processors, the Ryzen 7 part tested being the YD1700BBM88AE [11]. Ratings: Not radiation hardened. Qualification: 200 MeV protons [11]. Single event functional interrupts and upsets observed with no failures to 1.55e11, 8.92e10 and 4.66e10 p/cm2 respectively; a power cycle recovered every event.
Jetson TX2, NVIDIA. Commercial GPU single-board computer. Ratings: Not radiation hardened. Qualification: 200 MeV protons, two devices [11]. One saw no failures to 1.89e9 p/cm2; the second failed at 1.61e9 p/cm2. A power cycle recovered every non-destructive event.
Jetson TX2i, NVIDIA, on a Connect Tech Spacely carrier. Commercial GPGPU carried as a rover central computer, proton and cobalt-60 tested. Ratings: Selected for MoonRanger, a mission of 6 to 9 days in transit and under 15 days on the surface, with avionics powered down through the Van Allen belts [12]. Qualification: Independent testing established survival to 45 krad(Si) unpowered and 10 krad(Si) powered, with proton testing giving at least 90 percent probability of survival at an average of 4 resets in a flux equivalent to a six month lunar mission [12]. Computer, cameras and interface board were also tested powered to 4 krad(Si) against a predicted mission dose under 1 krad(Si).
MSP430, Texas Instruments. Microcontroller used across the MoonRanger power, thermal and motor control boards. Ratings: Ferroelectric program memory, which is immune to the radiation effects that afflict flash program store. Qualification: CubeSat flight heritage on BasicLEO, RAX-1, RAX-2 and LMRSat [12]. In-house total ionizing dose testing was levied on the in-house boards that carry it.
ISIS On-board Computer, Innovative Solutions in Space. ARM9 CubeSat peripheral computer. Ratings: Carried as the MoonRanger peripheral computer on CubeSat space heritage rather than on a new campaign [12]. Qualification: No new testing performed; heritage accepted.
Coral USB accelerator, Google: a commercial edge TPU inference ASIC for 8-bit quantized neural networks [13]. Ratings: Paired with an ARM Cortex-A72 at 1.5 GHz and 4 GB of RAM as a candidate flight-representative host. Qualification: No radiation campaign published. Ground benchmark only: YOLOv3-tiny sample tube detection ran in 33.46 ms on the Cortex-A72 with the accelerator against 2808.62 ms without it, 84 times faster at lower power than the CPU alone [13].

A total ionizing dose or proton number is a screening result, not a qualification. None of the commercial parts above carries a latch-up immunity claim, and the Vorago board result shows that a part-level pass does not predict the board-level recovery path.

Compute benchmarked at one task

Four devices in seven configurations were run on the same workload, a Mars HiRISE image classifier over a 1793 image test set at 227x227 grayscale with an AlexNet transfer-learning model [14]:

Processor or unit	Inference time	Energy per inference
Snapdragon 855 DSP	7.6 ms	0.016 J
Snapdragon 855 NPU	not given	0.014 J
Snapdragon 855 GPU	16.3 ms	0.051 J
Intel Movidius Myriad X	16.2 ms	0.032 J
Snapdragon 855 CPU	87.8 ms	0.5 J
MacBook reference	56.9 ms	not given
Jetson Nano CPU	1069 ms	5.3 to 10.7 J

The Snapdragon 855 DSP was the fastest and near the lowest energy of the set, profiled with the Snapdragon Profiler as runtime times power; the NPU on the same part took the lowest energy per inference, with no inference time published; the GPU took three times the DSP energy at twice the time; and the CPU is the baseline those on-part accelerators were measured against [14]. The Intel Movidius Myriad X was compiled to 12 vector cores and metered over USB, so its time includes host transfer, and the MacBook is a laptop reference point for the same model and test set, with no energy published. The Jetson Nano was slowest by two orders of magnitude, and its energy is estimated from a 5 to 10 W device range rather than measured [14].

The energy spread runs over three orders of magnitude and does not track the time spread. Across the standard classifiers in the same study the DSP and NPU ran 3 to 48 times faster than the Snapdragon CPU, with the speedup rising with parameter count and single-pixel models running slower on the accelerators than on the CPU [14]. The RAD750 could not be entered at all: no common deep learning framework supports it, so the flight processor of curiosity and perseverance has no figure in this comparison [14]. These are performance numbers, not qualification results. No facility, beam or dose appears against any of them. The same two units were run on the ISS, where nine iterations of the classifier and a set of memory checks over 540 minutes returned no output difference from the ground runs and no memory error [14], but that exposure is too short to bound an upset rate and the study derives no cross section from it.

Processors and support chips in the radiation compendia

The NASA Electronic Parts and Packaging compendia carry heavy-ion and proton results for processors that no planetary robot has flown. In every commercial part below the unprotected cache sets the sensitivity [16][17].

Part	Manufacturer	Facility	Source
UT699 LEON3FT	Cobham Gaisler	Texas A and M cyclotron	[15]
MC7447A PowerPC	Freescale	Texas A and M cyclotron	[16]
P2020, P5020	Freescale	Texas A and M cyclotron	[17]
MV64460 bridge chip	Marvell	Texas A and M, Indiana	[16]
Maestro many-core	Boeing	Texas A and M cyclotron	[17]
Radeon e9173 GPU	AMD	Massachusetts General Hospital	[11]
GR740 LEON4	Gaisler	No radiation result published	[18]
TILE64	Tilera	No radiation result published	[19]
IFC6501 module	Inforce	No radiation result published	[20]

Ratings and campaign results

UT699 LEON3FT, Cobham Gaisler. Radiation-hardened fault-tolerant SPARC V8 processor, the space-grade member of the core family flown in the SuperCam Body Unit [5]. Ratings: hardened by design rather than by shielding. Qualification: heavy ion over a 9 to 60 MeV-cm2/mg range at 0 and 60 degree incidence, giving an upset threshold of 8 MeV-cm2/mg in the internal SRAM cells against 54 MeV-cm2/mg in the flip flops [15]. The SRAM threshold is under a sixth of the flip-flop threshold on the same die, so the memory and not the logic sets the scrub requirement.
MC7447A PowerPC, Freescale. Commercial PowerPC G4 processor. Ratings: not radiation hardened. Qualification: upset threshold below 1.7 MeV-cm2/mg with no latchup observed under heavy ion at the Texas A and M cyclotron [16].
P2020, P5020, Freescale. Commercial dual-core Power Architecture communications processors. Ratings: no effects are expected below 100 krad(Si), which the guideline records as an engineering expectation and not a measured qualification [15]. Qualification: unprotected cache upset threshold below 1.8 MeV-cm2/mg on both parts, with a saturated cross section of about 2e-9 cm2 per bit on the P2020 and about 1e-10 on the P5020 [17]. The effective sensitivity of the P2020 test setup was limited to 1e-6 cm2/device by the fluence at which cores crashed from double-bit cache errors [15], so the reported floor belongs to the setup rather than to the part.
MV64460 bridge chip, Marvell. System bridge and memory controller, the part between a flight processor and its memory. Ratings: commercial. Qualification: upset threshold below 1.7 MeV-cm2/mg under heavy ion with high-current functional interrupts also observed, and a proton upset threshold below 21 MeV at the Indiana University Cyclotron Facility [16]. A bridge chip upsetting at the same threshold as the processor it serves removes the benefit of hardening only the processor.
Maestro many-core, Boeing. Radiation-hardened 49-core derivative of the Tilera architecture [19]. Ratings: projected at 300 MHz with an IEEE-754 floating point unit per core, in test at the time of the source and quoted as 2.5 times the TILE64 throughput [19]. Qualification: unprotected cache upset threshold below 2 MeV-cm2/mg, saturated cross section about 8e-8 cm2 per bit [17].
Radeon e9173 GPU, AMD. Commercial embedded GPU. Ratings: not radiation hardened. Qualification: non-destructive latchup at a 200 MeV proton fluence of 6.24e9 protons/cm2, cleared by a power cycle [11].
GR740 LEON4, Gaisler. Radiation-hardened quad-core SPARC V8. Ratings: 1836 DMIPS at 250 MHz over four cores, about 1.2 W, quoted as a reference value rather than measured by the citing work [18]. Qualification: no radiation campaign is published in the corpus for this part. A flight autonomy component scaled onto it meets the Enceladus orbiter and Europa Lander use cases and fails the terrestrial field case by an order of magnitude, which is a scaled projection and not a benchmark [21].
TILE64, Tilera. Commercial 64-core mesh processor at 750 MHz, three-way VLIW, with no floating point hardware [19]. Ratings: 8 kB of L1 instruction and data cache and 64 kB of L2 per core [19]. Qualification: no radiation campaign published. Rock detection over a 116-image MER navcam set ran in 2.9 s per image on one core and 0.3 s on 32, a 9.7-fold speedup that asymptotes near ten rather than scaling with core count [19]. Converting the same code from floating point to integer cut single-core runtime by a factor of five, because software floating point emulation had been taking nearly three quarters of it.
IFC6501 module, Inforce. Commercial Snapdragon 805 module carried as the Astrobee flight processor [20]. Ratings: four cores at 2.5 GHz, shared between autonomy software and concurrent processes [20]. Qualification: no radiation campaign published. Operated on the International Space Station, where a single core measures about ten times slower than a 2.4 GHz Intel i9-9980HK [22].

A compendium entry is two or three parts tested to a threshold, not a qualification. None of the commercial processors above carries a published upset rate for any orbit, and the three parts with no campaign at all are listed because they were selected and run, not because a beam was pointed at them.

References

Maimone, M., Cheng, Y. and Matthies, L. (2007). Two Years of Visual Odometry on the Mars Exploration Rovers . Journal of Field Robotics, 3. Source

BibTeX

@article{maimone2007two,
  title = {Two Years of Visual Odometry on the Mars Exploration Rovers},
  author = {Maimone, Mark and Cheng, Yang and Matthies, Larry},
  journal = {Journal of Field Robotics},
  volume = {24},
  number = {3},
  pages = {169--186},
  institution = {NASA Jet Propulsion Laboratory},
  year = {2007},
  doi = {10.1002/rob.20184},
  abstract = {Abstract NASA's two Mars Exploration Rovers (MER) have successfully demonstrated a robotic Visual Odometry capability on another world for the first time. This provides each rover with accurate knowledge of its position, allowing it to autonomously detect and compensate for any unforeseen slip encountered during a drive. It has enabled the rovers to drive safely and more effectively in highly sloped and sandy terrains and has resulted in increased mission science return by reducing the number of days required to drive into interesting areas. The MER Visual Odometry system comprises onboard software for comparing stereo pairs taken by the pointable mast‐mounted 45 deg FOV Navigation cameras (NAVCAMs). The system computes an update to the 6 degree of freedom rover pose ( x , y , z , roll, pitch, yaw) by tracking the motion of autonomously selected terrain features between two pairs of 256×256 stereo images. It has demonstrated good performance with high rates of successful convergence (97% on Spirit, 95% on Opportunity), successfully detected slip ratios as high as 125%, and measured changes as small as 2 mm, even while driving on slopes as high as 31 deg. Visual Odometry was used over 14% of the first 10.7 km driven by both rovers. During the first 2 years of operations, Visual Odometry evolved from an “extra credit” capability into a critical vehicle safety system. In this paper we describe our Visual Odometry algorithm, discuss several driving strategies that rely on it (including Slip Checks, Keep‐out Zones, and Wheel Dragging), and summarize its results from the first 2 years of operations on Mars. © 2006 Wiley Periodicals, Inc.}
}

Rankin, A., Maimone, M., Biesiadecki, J., Patel, N., Levine, D. and Toupet, O. (2021). Mars Curiosity Rover Mobility Trends During the First Seven Years . Journal of Field Robotics, 5. Source

BibTeX

@article{rankin2021mars,
  title = {Mars Curiosity Rover Mobility Trends During the First Seven Years},
  author = {Rankin, Arturo and Maimone, Mark and Biesiadecki, Jeffrey and Patel, Nikunj and Levine, Dan and Toupet, Olivier},
  journal = {Journal of Field Robotics},
  volume = {38},
  number = {5},
  pages = {759--800},
  year = {2021},
  doi = {10.1002/rob.22011},
  abstract = {Abstract NASA's Mars Science Laboratory (MSL) Curiosity rover landed on Mars on August 6, 2012. In the 7 years between landing and August 6, 2019 (sol 2488), Curiosity has driven 21,318.5 m over a variety of terrain types and slopes, employing multiple drive modes with varying amounts of onboard autonomy. Curiosity's drive distances each sol have ranged from its shortest drive of 2.6 cm to its longest drive of 142.5 m, with an average drive distance of 28.9 m. Real‐time human intervention is not possible during Curiosity's drives due to the latency in uplinking commands and downlinking telemetry. Instead, the operations team relies on Curiosity's fault protection, autonomous navigation, and visual odometry software to keep the rover safe during drives. During its first 7 years on Mars, Curiosity has attempted 738 drives. While 622 drives ran to completion, 116 drives were prevented or stopped early by Curiosity's fault protection software. The primary risks to mobility success have been wheel damage, wheel entrapment, progressive wheel sinkage, and the potential for hardware or cable failures that result in an inability to command one or more steer or drive actuators. In this paper, we describe Curiosity's mobility subsystem, mobility trends over the first 21.3 km of the mission, operational aspects of mobility fault protection, risks to continued mobility success, and risk mitigation strategies.}
}

Makovsky, A., Ilott, P. and Taylor, J. (2009). Mars Science Laboratory Telecommunications System Design . Jet Propulsion Laboratory, California Institute of Technology, Article 14. Source

BibTeX

@techreport{makovsky2009mars,
  title = {Mars Science Laboratory Telecommunications System Design},
  author = {Makovsky, Andre and Ilott, Peter and Taylor, Jim},
  series = {DESCANSO Design and Performance Summary Series},
  number = {Article 14},
  institution = {Jet Propulsion Laboratory, California Institute of Technology},
  year = {2009},
  url = {https://descanso.jpl.nasa.gov/DPSummary/Descanso14_MSL_Telecom.pdf}
}

Agrawal, J., Yelamanchili, A. and Chien, S. (2020). Using Explainable Scheduling for the Mars 2020 Rover Mission . arXiv preprint. Source

BibTeX

@article{agrawal2020explainable,
  title = {Using Explainable Scheduling for the Mars 2020 Rover Mission},
  author = {Agrawal, Jagriti and Yelamanchili, Amruta and Chien, Steve},
  journal = {arXiv preprint},
  year = {2020},
  doi = {10.48550/arxiv.2011.08733},
  abstract = {Understanding the reasoning behind the behavior of an automated scheduling system is essential to ensure that it will be trusted and consequently used to its full capabilities in critical applications. In cases where a scheduler schedules activities in an invalid location, it is usually easy for the user to infer the missing constraint by inspecting the schedule with the invalid activity to determine the missing constraint. If a scheduler fails to schedule activities because constraints could not be satisfied, determining the cause can be more challenging. In such cases it is important to understand which constraints caused the activities to fail to be scheduled and how to alter constraints to achieve the desired schedule. In this paper, we describe such a scheduling system for NASA's Mars 2020 Perseverance Rover, as well as Crosscheck, an explainable scheduling tool that explains the scheduler behavior. The scheduling system and Crosscheck are the baseline for operational use to schedule activities for the Mars 2020 rover. As we describe, the scheduler generates a schedule given a set of activities and their constraints and Crosscheck: (1) provides a visual representation of the generated schedule; (2) analyzes and explains why activities failed to schedule given the constraints provided; and (3) provides guidance on potential constraint relaxations to enable the activities to schedule in future scheduler runs.}
}

Maurice, S., Wiens, R. C., Bernardi, P., Caïs, P., Robinson, S. H., Nelson, T., Gasnault, O., Reess, J.-M., Deleuze, M., Rull, F., Manrique, J. A., Abbaki, S., Anderson, R. B., André, Y., Angel, S., Arana, G., Battault, T., Beck, P., Benzerara, K., Bernard, S., Berthias, J.-P., Beyssac, O., Bonafous, M., Bousquet, B., Boutillier, M., Cadu, A., Castro, K., Chapron, F., Chide, B., Clark, K., Clavé, E., Clegg, S., Cloutis, E., Collin, C., Cordoba, E. C., Cousin, A., Dameury, J.-C., D'Anna, W., Daydou, Y., Debus, A., Deflores, L., Dehouck, E., Delapp, D., De Los Santos, G., Donny, C., Doressoundiram, A., Dromart, G., Dubois, B., Dufour, A., Dupieux, M., Egan, M., Ervin, J., Fabre, C., Fau, A., Fischer, W., Forni, O., Fouchet, T., Frydenvang, J., Gauffre, S., Gauthier, M., Gharakanian, V., Gilard, O., Gontijo, I., Gonzalez, R., Granena, D., Grotzinger, J., Hassen-Khodja, R., Heim, M., Hello, Y., Hervet, G., Humeau, O., Jacob, X., Jacquinod, S., Johnson, J. R., Kouach, D., Lacombe, G., Lanza, N., Lapauw, L., Laserna, J., Lasue, J., Le Deit, L., Le Mouélic, S., Le Comte, E., Lee, Q.-M., Legett, I. C., Leveille, R., Lewin, E., Leyrat, C., Lopez-Reyes, G., Lorenz, R., Lucero, B., Madariaga, J. M., Madsen, S., Madsen, M., Mangold, N., Manni, F., Mariscal, J.-F., Martinez-Frias, J., Mathieu, K., Mathon, R., McCabe, K. P., McConnochie, T., McLennan, S. M., Mekki, J., Melikechi, N., Meslin, P.-Y., Micheau, Y., Michel, Y., Michel, J. M., Mimoun, D., Misra, A., Montagnac, G., Montaron, C., Montmessin, F., Moros, J., Mousset, V., Morizet, Y., Murdoch, N., Newell, R. T., Newsom, H., Nguyen Tuong, N., Ollila, A. M., Orttner, G., Oudda, L., Pares, L., Parisot, J., Parot, Y., Pérez, R., Pheav, D., Picot, L., Pilleri, P., Pilorget, C., Pinet, P., Pont, G., Poulet, F., Quantin-Nataf, C., Quertier, B., Rambaud, D., Rapin, W., Romano, P., Roucayrol, L., Royer, C., Ruellan, M., Sandoval, B., Sautter, V., Schoppers, M. J., Schröder, S., Seran, H.-C., Sharma, S. K., Sobron, P., Sodki, M., Sournac, A., Sridhar, V., Standarovsky, D., Storms, S., Striebig, N., Tatat, M., Toplis, M., Torre-Fdez, I., Toulemont, N., Velasco, C., Veneranda, M., Venhaus, D., Virmontois, C., Viso, M., Willis, P. and Wong, K. (2021). The SuperCam Instrument Suite on the Mars 2020 Rover: Science Objectives and Mast-Unit Description . Space Science Reviews, 47. Source

BibTeX

@article{maurice2021supercam,
  title = {The SuperCam Instrument Suite on the Mars 2020 Rover: Science Objectives and Mast-Unit Description},
  author = {Maurice, Sylvestre and Wiens, Roger C. and Bernardi, Pernelle and Caïs, Phillippe and Robinson, Scott H. and Nelson, Tony and Gasnault, Olivier and Reess, Jean-Michel and Deleuze, Muriel and Rull, Fernando and Manrique, Jose Antonio and Abbaki, Sadok and Anderson, Ryan B. and André, Yves and Angel, S.M. and Arana, Gorka and Battault, T. and Beck, Pierre and Benzerara, Karim and Bernard, Sylvain and Berthias, J.-P. and Beyssac, Olivier and Bonafous, Marion and Bousquet, Bruno and Boutillier, M. and Cadu, A. and Castro, Kepa and Chapron, Frédéric and Chide, Baptiste and Clark, K. and Clavé, Elise and Clegg, S. and Cloutis, E. and Collin, Claude and Cordoba, Elizabeth C. and Cousin, A. and Dameury, J.-C. and D'Anna, W. and Daydou, Yves and Debus, Andre and Deflores, Lauren and Dehouck, Erwin and Delapp, Dorothea and De Los Santos, Greg and Donny, C. and Doressoundiram, Alain and Dromart, Gilles and Dubois, Bruno and Dufour, A. and Dupieux, M. and Egan, Miles and Ervin, Joan and Fabre, C. and Fau, Amaury and Fischer, Woodward and Forni, Olivier and Fouchet, Thierry and Frydenvang, Jens and Gauffre, S. and Gauthier, M. and Gharakanian, V. and Gilard, Olivier and Gontijo, Ivair and Gonzalez, R. and Granena, D. and Grotzinger, J. and Hassen-Khodja, Rafik and Heim, M. and Hello, Y. and Hervet, G. and Humeau, Olivier and Jacob, Xavier and Jacquinod, Sophie and Johnson, Jeffrey R. and Kouach, Driss and Lacombe, G. and Lanza, Nina and Lapauw, Laurent and Laserna, Javier and Lasue, J. and Le Deit, Laetitia and Le Mouélic, Stéphane and Le Comte, E. and Lee, Qiu-Mei and Legett, IV, C. and Leveille, Richard and Lewin, Eric and Leyrat, C. and Lopez-Reyes, Guillermo and Lorenz, R. and Lucero, Briana and Madariaga, Juan Manuel and Madsen, Soren and Madsen, M. and Mangold, N. and Manni, F. and Mariscal, J.-F. and Martinez-Frias, Jesus and Mathieu, K. and Mathon, R. and McCabe, Kevin P. and McConnochie, T. and McLennan, Scott M. and Mekki, J. and Melikechi, Noureddine and Meslin, Pierre-Yves and Micheau, Y. and Michel, Y. and Michel, John M. and Mimoun, David and Misra, A. and Montagnac, Gilles and Montaron, Christophe and Montmessin, Franck and Moros, J. and Mousset, V. and Morizet, Y. and Murdoch, Naomi and Newell, Raymond T. and Newsom, Horton and Nguyen Tuong, N. and Ollila, Ann M. and Orttner, G. and Oudda, L. and Pares, Laurent and Parisot, Jérôme and Parot, Yann and Pérez, René and Pheav, D. and Picot, L. and Pilleri, Paolo and Pilorget, C. and Pinet, P. and Pont, Gabriel and Poulet, F. and Quantin-Nataf, Cathy and Quertier, Benjamin and Rambaud, D. and Rapin, William and Romano, P. and Roucayrol, L. and Royer, C. and Ruellan, M. and Sandoval, B.F. and Sautter, Violaine and Schoppers, Marcel J. and Schröder, S. and Seran, H.-C. and Sharma, Shiv K. and Sobron, Pablo and Sodki, M. and Sournac, A. and Sridhar, Vishnu and Standarovsky, D. and Storms, Steven and Striebig, Nicolas and Tatat, M. and Toplis, M. and Torre-Fdez, Imanol and Toulemont, N. and Velasco, C. and Veneranda, Marco and Venhaus, Dawn and Virmontois, C. and Viso, M. and Willis, P. and Wong, K.W.},
  journal = {Space Science Reviews},
  volume = {217},
  number = {47},
  year = {2021},
  doi = {10.1007/s11214-021-00807-w},
  abstract = {Abstract On the NASA 2020 rover mission to Jezero crater, the remote determination of the texture, mineralogy and chemistry of rocks is essential to quickly and thoroughly characterize an area and to optimize the selection of samples for return to Earth. As part of the Perseverance payload, SuperCam is a suite of five techniques that provide critical and complementary observations via Laser-Induced Breakdown Spectroscopy (LIBS), Time-Resolved Raman and Luminescence (TRR/L), visible and near-infrared spectroscopy (VISIR), high-resolution color imaging (RMI), and acoustic recording (MIC). SuperCam operates at remote distances, primarily 2–7 m, while providing data at sub-mm to mm scales. We report on SuperCam’s science objectives in the context of the Mars 2020 mission goals and ways the different techniques can address these questions. The instrument is made up of three separate subsystems: the Mast Unit is designed and built in France; the Body Unit is provided by the United States; the calibration target holder is contributed by Spain, and the targets themselves by the entire science team. This publication focuses on the design, development, and tests of the Mast Unit; companion papers describe the other units. The goal of this work is to provide an understanding of the technical choices made, the constraints that were imposed, and ultimately the validated performance of the flight model as it leaves Earth, and it will serve as the foundation for Mars operations and future processing of the data.}
}

Nolet, S. (2007). Development of a Guidance, Navigation and Control Architecture and Validation Process Enabling Autonomous Docking to a Tumbling Satellite. Source

BibTeX

@phdthesis{nolet2007development,
  title = {Development of a Guidance, Navigation and Control Architecture and Validation Process Enabling Autonomous Docking to a Tumbling Satellite},
  author = {Nolet, Simon},
  school = {Massachusetts Institute of Technology},
  type = {Ph.D. thesis},
  year = {2007},
  url = {https://dspace.mit.edu/handle/1721.1/38598}
}

Miller, D. L. (2015). Development of Resource-Constrained Sensors and Actuators for In-Space Satellite Docking and Servicing. Source

BibTeX

@mastersthesis{miller2015development,
  title = {Development of Resource-Constrained Sensors and Actuators for In-Space Satellite Docking and Servicing},
  author = {Miller, Duncan L.},
  school = {Massachusetts Institute of Technology},
  type = {S.M. thesis},
  year = {2015},
  url = {https://dspace.mit.edu/handle/1721.1/98805}
}

Aagren, T. S., Ruan, A. W., Malpica, C., Withrow-Maser, S. and Meyn, L. (2025). In-flight System Identification of the Ingenuity Mars Helicopter . AIAA SCITECH Forum, 20240014856. Source

BibTeX

@inproceedings{aagren2025flight,
  title = {In-flight System Identification of the Ingenuity Mars Helicopter},
  author = {Aagren, Tove S. and Ruan, Allen W. and Malpica, Carlos and Withrow-Maser, Shannah and Meyn, Larry},
  booktitle = {AIAA SCITECH Forum},
  number = {20240014856},
  institution = {NASA},
  year = {2025},
  doi = {10.2514/6.2025-0007},
  abstract = {The 68th and 69th flights of NASA’s Ingenuity Mars Helicopter marked the first dedicated system identification flights of an aerial vehicle on another planet. Chirp signals were injected into the swashplate cyclic controls for both legs of the two out-and-back flights. Frequency responses were computed from the flight data, using both the Direct Method (DM) and the Joint-Input-Output (JIO) approach, for the identification of stability and control derivatives in forward flight conditions. The resulting identified state-space models were compared against existing flight dynamics simulation models, showing excellent correlation in the higher frequency range. External disturbances were seen to introduce a bias in the identified lower frequency responses, which was partially mitigated using the JIO method. These findings will inform future modeling and flight testing efforts of Mars rotorcraft.}
}

Withrow-Maser, S., Johnson, W., Tzanetos, T., Grip, H., Koning, W., Schatzman, N., Young, L., Chan, A., Ruan, A., Cummings, H., Allan, B., Malpica, C., Meyn, L., Pipenberg, B. and Keennon, M. (2023). Mars Sample Recovery Helicopter: Rotorcraft to Retrieve the First Samples from the Martian Surface . Vertical Flight Society Annual Forum and Technology Display, 20230005247. Source

BibTeX

@inproceedings{withrowmaser2023mars,
  title = {Mars Sample Recovery Helicopter: Rotorcraft to Retrieve the First Samples from the Martian Surface},
  author = {Withrow-Maser, Shannah and Johnson, Wayne and Tzanetos, Theodore and Grip, Havard and Koning, Witold and Schatzman, Natasha and Young, Larry and Chan, Athena and Ruan, Allen and Cummings, Haley and Allan, Brian and Malpica, Carlos and Meyn, Larry and Pipenberg, Benjamin and Keennon, Matthew},
  booktitle = {Vertical Flight Society Annual Forum and Technology Display},
  number = {20230005247},
  pages = {1-8},
  institution = {NASA Ames Research Center and Jet Propulsion Laboratory},
  year = {2023},
  doi = {10.4050/f-0079-2023-17969},
  abstract = {The Mars Sample Return Mission (MSR) will carry the next set of Mars helicopters, Sample Recovery Helicopters (SRHs), to the Martian surface. After successfully demonstrating extraterrestrial flight in 2021, Ingenuity has acted as a}
}

Koontz, S. L., Suggs, R. M., Alred, J. W., Worthy, E. S., Boeder, P., Steagall, C. A., Hartman, W. A., Gingras, B. D. and Schmidl, W. D. (2018). The International Space Station Space Radiation Environment: Avionics Systems Performance in Low-Earth Orbit Single Event Effects (SEE) Environments . International Conference on Environmental Systems, ICES-2018-69. Source

BibTeX

@inproceedings{koontz2018international,
  title = {The International Space Station Space Radiation Environment: Avionics Systems Performance in Low-Earth Orbit Single Event Effects (SEE) Environments},
  author = {Koontz, Steven L. and Suggs, Robert M. and Alred, John W. and Worthy, Erica S. and Boeder, Paul and Steagall, Courtney A. and Hartman, William A. and Gingras, Benjamin D. and Schmidl, William D.},
  booktitle = {International Conference on Environmental Systems},
  number = {ICES-2018-69},
  address = {Albuquerque, New Mexico},
  year = {2018},
  url = {https://ttu-ir.tdl.org/items/7fb5d403-ad77-4f6b-9cff-c70af5c04802}
}

Topper, A. D., Lauenstein, J.-M., Wilcox, E. P., Berg, M. D., Campola, M. J., Casey, M. C., Wyrwas, E. J., O'Bryan, M. V., Carstens, T. A., Fedele, C. M., Forney, J. D., Kim, H. S., Osheroff, J. M., Phan, A. M., Chaiken, M. F., Cochran, D. J., Pellish, J. A. and Majewicz, P. J. (2020). NASA Goddard Space Flight Center's Compendium of Radiation Effects Test Results . IEEE Radiation Effects Data Workshop. Source

BibTeX

@inproceedings{topper2020nasa,
  title = {NASA Goddard Space Flight Center's Compendium of Radiation Effects Test Results},
  author = {Topper, Alyson D. and Lauenstein, Jean-Marie and Wilcox, Edward P. and Berg, Melanie D. and Campola, Michael J. and Casey, Megan C. and Wyrwas, Edward J. and O'Bryan, Martha V. and Carstens, Thomas A. and Fedele, Caroline M. and Forney, James D. and Kim, Hak S. and Osheroff, Jason M. and Phan, Anthony M. and Chaiken, Max F. and Cochran, Donna J. and Pellish, Jonathan A. and Majewicz, Peter J.},
  booktitle = {IEEE Radiation Effects Data Workshop},
  pages = {1--12},
  year = {2020},
  doi = {10.1109/redw51883.2020.9325841},
  abstract = {Total ionizing dose, displacement damage dose, and single event effects testing were performed to characterize and determine the suitability of candidate electronics for NASA space utilization. Devices tested include FETs, flash memory, FPGAs, optoelectronics, digital, analog, and bipolar devices.}
}

Whittaker, C. (2021). MR-AVI-0068 Radiation Survival Summary, Revision A . Carnegie Mellon University, MoonRanger Project. Source

BibTeX

@techreport{whittaker2021radiation,
  title = {MR-AVI-0068 Radiation Survival Summary, Revision A},
  author = {Whittaker, Chuck},
  institution = {Carnegie Mellon University, MoonRanger Project},
  month = {May},
  year = {2021},
  url = {https://labs.ri.cmu.edu/moonranger/wp-content/uploads/sites/24/2021/07/MR-AVI-0068_Radiation-Survival-Summary.pdf}
}

Castilla-Arquillo, R., Perez-del-Pulgar, C. J., Paz-Delgado, G. J. and Gerdes, L. (2022). Hardware-accelerated Mars Sample Localization via deep transfer learning from photorealistic simulations . IEEE Robotics and Automation Letters, 4. Source

BibTeX

@article{castillaarquillo2022hardware,
  title = {Hardware-accelerated Mars Sample Localization via deep transfer learning from photorealistic simulations},
  author = {Castilla-Arquillo, Raul and Perez-del-Pulgar, Carlos J. and Paz-Delgado, Gonzalo J. and Gerdes, Levin},
  journal = {IEEE Robotics and Automation Letters},
  volume = {7},
  number = {4},
  pages = {12160--12167},
  year = {2022},
  doi = {10.1109/lra.2022.3219306},
  abstract = {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.}
}

Dunkel, E. R., Swope, J., Candela, A., West, L., Chien, S. A., Towfic, Z., Buckley, L., Romero-Cañas, J., Espinosa-Aranda, J. L., Hervas-Martin, E. and Fernandez, M. R. (2023). Benchmarking Deep Learning Models on Myriad and Snapdragon Processors Onboard the ISS . IEEE Aerospace Conference. Source

BibTeX

@inproceedings{dunkel2023benchmarking,
  title = {Benchmarking Deep Learning Models on Myriad and Snapdragon Processors Onboard the ISS},
  author = {Dunkel, Emily R. and Swope, Jason and Candela, Alberto and West, Lauren and Chien, Steve A. and Towfic, Zaid and Buckley, Léonie and Romero-Cañas, Juan and Espinosa-Aranda, Jose Luis and Hervas-Martin, Elena and Fernandez, Mark R.},
  booktitle = {IEEE Aerospace Conference},
  publisher = {JPL Open Repository},
  year = {2023},
  doi = {10.48577/jpl.v0obta},
  abstract = {Future space missions can benefit from processing imagery onboard to detect science events, create insights, and respond autonomously. One of the challenges to this mission concept is that traditional space flight computing has limited capabilities because it is derived from much older computing to ensure reliable performance in the extreme environments of space, particularly radiation. Modern Commercial Off The Shelf (COTS) processors, such as the Movidius Myriad X and the Qualcomm Snapdragon, provide significant improvements in small Size Weight and Power (SWaP) packaging and offer direct hardware acceleration for deep neural networks. We deploy neural network models on these processors hosted by Hewlett Packard Enterprise’s Spaceborne Computer-2 onboard the International Space Station (ISS). We benchmark a variety of algorithms trained on visual, synthetic aperture radar (SAR), or spectroscopic data from Earth or Mars, and standard deep learning models for image classification. Models are run multiple times, and memory checkers are deployed to test for radiation effects.}
}

Guertin, S. M. (2018). Guideline for Single-Event Effect (SEE) Testing of System on a Chip (SOC) Devices . NASA, 20190002148. Source

BibTeX

@techreport{guertin2018guideline,
  title = {Guideline for Single-Event Effect (SEE) Testing of System on a Chip (SOC) Devices},
  author = {Guertin, Steven M.},
  number = {20190002148},
  institution = {NASA},
  year = {2018},
  url = {https://ntrs.nasa.gov/citations/20190002148},
  abstract = {The use of complex single and multicore processors with significant cache memory, on-chip peripherals, memory controllers, and high speed input/output (IO) that integrate many of the parts of a traditional computer system is becoming more common in space applications. Such devices are often referred to as system on a chip devices (SOCs), even though the term is used somewhat inaccurately due to the lack of analog and mixed signal subcircuits. These devices are complex combinations of single- or multi-core processors with memory controllers, high-speed input/output (IO), and other peripheral structures that formerly would have been handled by off-chip resources. In the past the processors were tested for single event effects (SEE) separately, and the peripherals were often put into custom application-specific integrated circuits (ASICs) along with other resources required by the user. Performance and cost pressures have pushed commercial devices to incorporate many of the functional blocks into a single chip, an SOC. The NASA Electronic Parts and Packaging Program (NEPP) has been examining ways to perform SEE radiation hardness assurance (RHA) testing of these processor-centric SOCs to achieve reasonable understanding of their performance in space missions.}
}

McClure, S. S., Allen, G. R., Irom, F., Scheick, L. Z., Adell, P. C. and Miyahira, T. F. (2010). Compendium of test results of recent single event effect tests conducted by the Jet Propulsion Laboratory . IEEE Radiation Effects Data Workshop. Source

BibTeX

@inproceedings{mcclure2010compendium,
  title = {Compendium of test results of recent single event effect tests conducted by the Jet Propulsion Laboratory},
  author = {McClure, Steven S. and Allen, Gregory R. and Irom, Farokh and Scheick, Leif Z. and Adell, Philippe C. and Miyahira, Tetsuo F.},
  booktitle = {IEEE Radiation Effects Data Workshop},
  pages = {6-6},
  publisher = {IEEE},
  year = {2010},
  doi = {10.1109/redw.2010.5619495},
  abstract = {This paper reports heavy ion and proton-induced single event effect (SEE) results from recent tests for a variety of microelectronic devices. The compendium covers devices tested over the last two years by the Jet Propulsion Laboratory.}
}

Allen, G. R., Guertin, S. M., Scheick, L. Z., Irom, F. and Zajac, S. (2012). Compendium of recent test results of single event effects conducted by the Jet Propulsion Laboratory . IEEE Radiation Effects Data Workshop. Source

BibTeX

@inproceedings{allen2012compendium,
  title = {Compendium of recent test results of single event effects conducted by the Jet Propulsion Laboratory},
  author = {Allen, Gregory R. and Guertin, Steven M. and Scheick, Leif Z. and Irom, Farokh and Zajac, Stephanie},
  booktitle = {IEEE Radiation Effects Data Workshop},
  pages = {1-10},
  publisher = {IEEE},
  year = {2012},
  doi = {10.1109/redw.2012.6353747},
  abstract = {This paper reports heavy ion, proton, and laser induced single event effects results for a variety of microelectronic devices targeted for possible use in NASA spacecrafts. The compendium covers devices tested within the years of 2010 through 2012.}
}

Towfic, Z. J., Ogbe, D., Sauvageau, J., Sheldon, D., Jongeling, A., Chien, S., Mirza, F., Dunkel, E., Swope, J., Ogut, M. and Cretu, V. (2022). Benchmarking and Testing of Qualcomm Snapdragon System-on-Chip for JPL Space Applications and Missions . IEEE Aerospace Conference. Source

BibTeX

@inproceedings{towfic2022benchmarking,
  title = {Benchmarking and Testing of Qualcomm Snapdragon System-on-Chip for JPL Space Applications and Missions},
  author = {Towfic, Zaid J and Ogbe, Dennis and Sauvageau, Joe and Sheldon, Douglas and Jongeling, Andre and Chien, Steve and Mirza, Faiz and Dunkel, Emily and Swope, Jason and Ogut, Mehmet and Cretu, Vlad},
  booktitle = {IEEE Aerospace Conference},
  pages = {1-12},
  publisher = {IEEE},
  year = {2022},
  doi = {10.1109/aero53065.2022.9843518},
  abstract = {As some space missions become more challenging due to new environments, greater distances, or more limited size, weight, and power (SWaP) constraints, spacecraft avionics must adapt to allow the spacecraft to be more autonomous and agile‐‐‐eliminating the Spacecraft-Earth-Spacecraft feedback loop whenever possible. Prime examples of such missions include Aerobots (such as Ingenuity with extremely low SWaP constraints and demanding signal/image processing during flight) and landers in possibly hostile environments (such as a Europa lander mission, with limited communication capacity, high latency, and constrained power budget). To address these challenges, JPL worked with Qualcomm to demonstrate the use of their Snapdragon 801 system-on-chip (SoC) onboard the Ingenuity Helicopter on Mars. The Qualcomm Snapdragon SoC contains various subsystems, including an ARM cluster, a Graphics processing unit, a Digital Signal Processing subsystem, a Neural Processing Engine, Image Signal Processing subsystem, among others. Since the success of Ingenuity, JPL is continuing to work with Qualcomm to address other applications of the Snapdragon SoC technology. This includes the deployment of two 855 Snapdragon development boards onboard the International Space Station (ISS) for successful in-situ benchmarking of applications in space (beyond those tested on Ingenuity). In this paper, we will examine the performance of various applications that have been identified to benefit from greater onboard computational capability. These applications include (among others): machine vision algorithms that are expected to be critical in autonomous entry-descent-and-landing scenarios and real-time Aerobot flight navigation; Hyperspectral compression algorithms; Synthetic Aperture Radar Processing along with various instrument processing algorithms. We discuss how the infusion of Qualcomm's Snapdragon SoC is capable of enabling missions that may not have been able to achieve their goals with traditional flight computing. In addition, we also show that for some algorithms, the software implementation on the Snapdragon SoC outperforms traditional FPGA implementations.}
}

Bornstein, B., Estlin, T., Clement, B. and Springer, P. (2011). Using a multicore processor for rover autonomous science . Aerospace Conference. Source

BibTeX

@inproceedings{bornstein2011multicore,
  title = {Using a multicore processor for rover autonomous science},
  author = {Bornstein, Benjamin and Estlin, Tara and Clement, Bradley and Springer, Paul},
  booktitle = {Aerospace Conference},
  pages = {1-9},
  publisher = {IEEE},
  year = {2011},
  doi = {10.1109/aero.2011.5747454}
}

Dinkel, H., Di, J., Santos, J., Albee, K., Borges, P., Moreira, M., Alexandrov, O., Coltin, B. and Smith, T. (2023). Multi-Agent 3D Map Reconstruction and Change Detection in Microgravity with Free-Flying Robots . International Astronautical Congress, IAC-23,D1,6,1,x78669. Source

BibTeX

@inproceedings{dinkel2023multi,
  title = {Multi-Agent 3D Map Reconstruction and Change Detection in Microgravity with Free-Flying Robots},
  author = {Dinkel, Holly and Di, Julia and Santos, Jamie and Albee, Keenan and Borges, Paulo and Moreira, Marina and Alexandrov, Oleg and Coltin, Brian and Smith, Trey},
  booktitle = {International Astronautical Congress},
  number = {IAC-23,D1,6,1,x78669},
  publisher = {JPL Open Repository},
  year = {2023},
  doi = {10.48577/jpl.lbsjso},
  abstract = {Assistive free-flyer robots autonomously caring for future crewed outposts---such as NASA’s Astrobee robots on the International Space Station (ISS)---must be able to detect day-to-day interior changes to track inventory, detect and diagnose faults, and monitor the outpost status. This work presents a framework for multi-agent cooperative mapping and change detection to enable robotic maintenance of space outposts. One agent is used to reconstruct a 3D model of the environment from sequences of images and corresponding depth information. Another agent is used to scan the environment for inconsistencies against the 3D model. Change detection is validated using real image and pose data collected by Astrobee robots on Earth in a test environment and in space on the ISS. This work outlines the objectives, requirements, and algorithmic modules for the multi-agent reconstruction system, including recommendations for its use aboard future microgravity outposts.}
}

Wronkiewicz, M., Lee, J., Lightholder, J., Doran, G., Mauceri, S., Schibler, T., Moorjani, E., Nadeau, J., Lindensmith, C. and Mandrake, L. (2023). Onboard Science Instrument Autonomy for the Detection of Microscopy Biosignatures on the Ocean Worlds Life Surveyor . AIAA Journal of Spacecraft and Rockets: MAPLD. Source

BibTeX

@article{wronkiewicz2023onboard,
  title = {Onboard Science Instrument Autonomy for the Detection of Microscopy Biosignatures on the Ocean Worlds Life Surveyor},
  author = {Wronkiewicz, Mark and Lee, Jake and Lightholder, Jack and Doran, Gary and Mauceri, Steffen and Schibler, Thomas and Moorjani, Eshaan and Nadeau, Jay and Lindensmith, Chris and Mandrake, Lukas},
  journal = {AIAA Journal of Spacecraft and Rockets: MAPLD},
  publisher = {JPL Open Repository},
  year = {2023},
  doi = {10.48577/jpl.mflyra}
}

Alexandrov, O., Barlow, J., Benavides, J., Bualat, M., Carlino, R., Coltin, B., Cortez, J., Daley, E., Feller, J., Flückiger, L., Fong, T., Fusco, J., Garcia Ruiz, R., Hamilton, K., Kanis, S., Katterhagen, A., Kim, Y., Love, J. F., McIntyre, M., McLachlan, B., Mora Vargas, A., Moratto, Z., Moreira, M., Morse, T., Orosco, H., Park, I.-W., Provencher, C., Sanchez, H., Sharif, K., Smith, E., Smith, T., Soussan, R., Symington, A., Talavera, R. O., To, V., Wheeler, D. and Yoo, J. (2026). Astrobee: Free-Flying Robots for the International Space Station . IEEE Transactions on Field Robotics. Source

BibTeX

@article{alexandrov2026astrobee,
  title = {Astrobee: Free-Flying Robots for the International Space Station},
  author = {Alexandrov, Oleg and Barlow, Jonathan and Benavides, Jose and Bualat, Maria and Carlino, Roberto and Coltin, Brian and Cortez, Jose and Daley, Earl and Feller, Jeffrey and Flückiger, Lorenzo and Fong, Terrence and Fusco, Jesse and Garcia Ruiz, Ruben and Hamilton, Kathryn and Kanis, Simeon and Katterhagen, Aric and Kim, Yunkyung and Love, John F. and McIntyre, Michael and McLachlan, Blair and Mora Vargas, Andres and Moratto, Zack and Moreira, Marina and Morse, Theodore and Orosco, Henry and Park, In-Won and Provencher, Christopher and Sanchez, Hugo and Sharif, Khaled and Smith, Ernest and Smith, Trey and Soussan, Ryan and Symington, Andrew and Talavera, Rafael Omar and To, Vinh and Wheeler, DW and Yoo, Jongwoon},
  journal = {IEEE Transactions on Field Robotics},
  year = {2026},
  url = {https://ntrs.nasa.gov/citations/20260001396},
  abstract = {The Astrobees are free-flying robots that operate inside the International Space Station (ISS) and were launched to the ISS
in 2019. Since then they have successfully performed hundreds of activities in space supporting almost two dozen separate
research projects. The robots were designed to overcome multiple challenges unique to the ISS environment, including safety,
upgradeability and maintainability, limited mass and computation, and unique localization challenges from lack of gravity and a
constantly changing environment. This article provides an overview of Astrobee, from hardware and software design to deployment
results and activities.}
}