Future planetary exploration missions demand high-performance, fault-tolerant computing to enable autonomous Guidance, Navigation, and Control (GNC) and Lander Vision System (LVS) operations during Entry, Descent, and Landing (EDL). This paper evaluates the deployment of GNC and LVS algorithms on next-generation multi-core processors--HPSC, Snapdragon VOXL2, and AMD Xilinx Versal--demonstrating up to 15x speedup for LVS image processing and over 250x speedup for Guidance for Fuel-Optimal Large Divert (GFOLD) trajectory optimization compared to legacy spaceflight hardware. To ensure computational reliability, we present ARBITER (Asynchronous Redundant Behavior Inspection for Trusted Execution and Recovery), a Multi-Core Voting (MV) mechanism that performs real-time fault detection and correction across redundant cores. ARBITER is validated in both static optimization tasks (GFOLD) and dynamic closed-loop control (Attitude Control System). A fault injection study further identifies the gradient computation stage in GFOLD as the most sensitive to bit-level errors, motivating selective protection strategies and vector-based output arbitration. This work establishes a scalable and energy-efficient architecture for future missions, including Mars Sample Return, Enceladus Orbilander, and Ceres Sample Return, where onboard autonomy, low latency, and fault resilience are critical.

Astrobotic’s OPAL (Optical Precision Autonomous Landing) system, developed under public-private partnership, is a stand-alone, bolt-on commercial terrain relative navigation (TRN) sensor enabling pose estimation over a wide range of altitudes, off-nadir angles, and velocities. TRN enables spacecraft to land at more challenging sites through pinpoint landing and a priori hazard avoidance. The OPAL system will fly aboard Astrobotic's Peregrine Mission One that will launch in 2023 under the NASA Commercial Lunar Payload Services (CLPS) program. OPAL’s testing and verification and validation approaches used are overviewed here, including software testing with simulated data, hardware-in-the-loop testing, terrestrial flight testing, and lander integration.

Natural pits on the Moon expose deep cross-sections of the lunar maria, enabling direct investigation of the Moon's volcanic history and providing potential access to subsurface lava tubes. The Moon Diver mission concept seeks to explore the Mare Tranquillitatis pit, which exposes the largest wall of bedrock of the mare pits (similar to 65 m). The concept is enabled by two innovative capabilities: pinpoint landing near the pit and robotic access to its nearvertical wall with an instrument package to examine the elemental chemistry, mineralogy, and morphology of these bedrock layers. Pinpoint landing uses closed-loop guidance with terrain-relative navigation (TRN), which was advanced by Perseverance landing on Mars, to deliver the lander within a 100-m ellipse. The Axel robotic explorer, which remains tethered to the lander, would egress onto the lunar surface and traverse the relatively flat terrain to the pit's funnel entrance. The lander, which is the data link to Earth, also serves as an anchor and provides power and communication to the rover through its tether. The rover is a novel two-wheeled platform with a trailing boom and a spool that pays out the tether as the rover traverses toward the pit. The 300-m long tether is well margined for the rover to scale the pit wall. The rover carries a surface preparation tool and three additional instrument types: (a) three high-resolution cameras for acquiring context images of the near and far walls with the near-wall pair in a stereoscopic configuration, (b) an alpha-particle-X-ray spectrometer (APXS) for elemental composition, and (c) a multi-spectral microscopic imager (MMI) that uses controlled lighting for mineralogy. The surface-preparation tool removes dust and patina from the rock wall by grinding a small area. This tool, the MMI, and the APXS would be deployed from an instrument bay inside the wheel wells. The rover would independently point each instrument at the same target on the wall with millimeter repeatability. Landing shortly after sunrise, the surface mission timeline is just shy of a lunar daytime (14 Earth days). Beyond the primary mission, the rover would be capable of descending from the overhang and peering into the void that may open to a large cave or lava tube. Lunar pits provide an exciting new target for exploration using innovative robotic capabilities that have been tested with integrated science instruments at multiple terrestrial analogue sites including a pit with basaltic layers in Arizona.

In patients aged ≥65 years with resectable non-small cell lung cancer (NSCLC), selecting effective and safe neoadjuvant treatment (chemotherapy vs immunotherapy [IO] vs chemotherapy+IO) is a major challenge. Also, the diverse T-cell receptor (TCR) repertoire crucial for a robust anti-cancer immune response changes with age. We present a subgroup analysis of clinical outcomes by age and TCR repertoire from LCMC3 (NCT02927301), a Phase II study of neoadjuvant and adjuvant atezolizumab (anti-PD-L1) in NSCLC that met its primary endpoint: a 20% major pathological response (MPR) rate after neoadjuvant atezolizumab.

The Mars 2020 Entry, Descent, and Landing (EDL) system successfully delivered the Perseverance rover to Jezero Crater, the most hazardous landing site ever attempted on Mars. To mitigate the risk of landing hazards, which included cliffs, inescapable dune fields, and rocks, a novel terrain relative navigation system was developed and integrated with the heritage Mars Science Laboratory EDL system. First the hazards were identified using orbital imagery and stored on-board the spacecraft as a hazard map. Then, during parachute descent, the Lander Vision System (LVS) estimated map relative position by fusing landmarks matched between descent imagery and a map of the landing site with inertial measurement unit data. Finally, the position estimate and hazard map were then used by the powered descent system to identify and then fly to the safest and reachable landing target. Postflight analysis indicated that all required systems worked much better than predicted. In particular, the fully autonomous LVS generated a position estimate in 10 s that was in error by only a few meters relative to a 40 m requirement. This paper describes the LVS design, how it was tested before launch, and the LVS performance during EDL.

As part of a NASA Tipping Point Partnership with Blue Origin to mature precision lunar landing technologies, two test flights of the Blue Origin New Shepard vehicle carrying a NASA-developed sensor suite were conducted on 10/13/2020 and 08/26/2021 at the West Texas Launch Site (LS-1). Part of the acquired datasets, comprising data from an inertial measurement unit and a downward facing camera, was postprocessed through a JPL-developed prototype Visual Odometry and Map Relative Localization software (TRNVOSIM), and compared against ground truth acquired by the host vehicle navigation system. In this paper, we provide a description of the algorithms, the test setup, and the processed results.

View Video Presentation: https://doi.org/10.2514/6.2022-0746.vid On February 18, 2021, the Perseverance Rover safely landed on Mars at Jezero Crater. Part of the successful landing was due to the Lander Vision System (LVS), which takes descent images from the LVS Camera (LCAM) and IMU measurements and estimates the lander position relative to a map of the Jezero landing site. The LVS Simulation LCAM (LVSS LCAM) model is an image rendering program developed to test the LVS in a variety of scenarios to ensure performance amid uncertainty. The LVSS LCAM model includes a pointing misalignment model, an exposure timing model, shadowing, a terrain reflectance model, atmospheric attenuation from dust, and sensor effects. This model was used for performance analysis, verification, and validation of the LVS algorithms in a Mars-like simulation prior to landing. This paper describes the LVSS LCAM rendering algorithm and compares flight images from LVS operation during the Perseverance landing with their rendered counterparts.

View Video Presentation: https://doi.org/10.2514/6.2022-1214.vid The Mars 2020 Entry Descent and Landing (EDL) system delivered the Perseverance rover to the surface of Mars on February 18th, 2021. A large fraction of the Jezero Crater landing site was covered with landing hazards including cliffs, inescapable dune fields and rocks. These hazards were identified or inferred using orbital imagery before launch so that they could be avoided using Terrain Relative Navigation (TRN) which was composed of two parts: the Lander Vision System (LVS) and Safe Target Selection (STS). During EDL, the LVS successfully estimated map relative position by fusing landmarks matched between descent imagery and a map of the landing site with Inertial Measurement Unit (IMU) data. This position estimate was used by STS to identify the safest target for landing that was also reachable given fuel and other constraints. The EDL system then used the powered descent phase to retarget to this location and land safely. The overall error between the targeted location and actual landing location was 5m which was an order of magnitude less than the 60m touchdown error requirement. This paper will describe the final tests of the LVS before launch, the checkout of the LVS during operations and the LVS performance during EDL.

Guidance, Navigation and Control (GN&C) technologies for precise and safe landing are enablers for solar system exploration of destinations where terrain hazards or pre-positioned surface assets pose a significant risk to successful mission touchdown and surface operations.Technologies for precise landing enable intelligent descent maneuvers to minimize landed position error from targeted science locations and to avoid large terrain hazards (craters, hills, cliffs) viewable in a priori orbital reconnaissance maps.Technologies for safe landing enable intelligent landing-divert maneuvers to avoid smaller lander-relevant terrain hazards (rocks, boulders, sharp features) detectable in high-resolution, onboard descent imagery of much higher resolution than reconnaissance maps.These capabilities integrated onto a lander increase mission surface accessibility, reduce landing risks, increase mission science opportunities, and promote new mission concepts.Collectively, these capabilities are known within NASA as PL&HA (Precision Landing and Hazard Avoidance) technology.PL&HA has maintained consistent high prioritization within space technology roadmaps from NASA and the National Research Council (NRC) for more than a decade.NASA investments in PL&HA have been ongoing since the mid 2000's and have involved funding from multiple mission directorates (STMD, SMD, HEO) and contributions from several centers and supporting institutions.The technologies are on track to be Technology Readiness Level (TRL) 8-9 between 2021-2024, with various component pieces being infused on the SMD Mars 2020 lander mission and into multiple SMD and STMD payloads on upcoming Commercial Lunar Payload Services (CLPS) missions.

Abstract The Mars 2020 rover, Perseverance, landed in Jezero crater (18.4663°N, 77.4298°E) on February 18, 2021 to collect samples from Mars that could be returned to Earth by a future Mars Sample Return campaign. While providing a rich sampling opportunity, Jezero also contains numerous landing hazards including scarps, canyons, mesas, dune fields, rock fields, and smaller craters. The Mars 2020 onboard inertial navigation system, which is the same as that used by the Mars Science Laboratory (MSL), only provides a very coarse inertially propagated position, which can have error as large as 3.2 km. The Lander Vision System (LVS) was added to Mars 2020 to reduce this position knowledge error to less than 40 m with respect to an on‐board reference map of the landing area. LVS uses a reference map on board to compare with the descent images for lander localization during the terminal stage of Entry, Descent and Landing (EDL). Because the reference map is used directly during EDL, it is critical that it have as little spatial and photometric error as possible. Photometrically, it should resemble as much as possible the real descent images to allow reliable terrain matching. Spatially, it should match as faithfully as possible the real, underlying terrain and contribute minimal error to the final localization solution. On February 18, 2021, the LVS reference map passed its ultimate test. The LVS system executed Terrain Relative Navigation (TRN) flawlessly based on the LVS reference map. In addition to its use for TRN, the local hazard map is also registered to the LVS reference map so that the safe target selection (STS) system can select a safe and reachable landing site. The final error between the site targeted by the STS system and the real landing site is estimated at only 5 m. The exceptional performance of LVS indicates that the reference map met mission requirements with comfortable margin. LVS on Mars 2020 represents the first ever use of a reference map during spacecraft EDL. This breakthrough will have profound implications for future lander missions and the scope of scientific inquiry they are able to address. In this paper, we will describe the necessary precursor steps to building the Jezero Crater LVS map, including the methodology to dejitter Context Imager (CTX) images, improve the CTX sensor model, as well as the process used to validate the LVS map accuracy.

The NASA Double Asteroid Redirection Test (DART) is a technology demonstration mission designed, built and operated by the Johns Hopkins Applied Physics Lab (JHU/APL). The mission's primary objectives are to 1) achieve a hypervelocity kinetic impact with the secondary member of the binary asteroid (65603) Didymos and 2) downlink at least two images of the target with a pixel sample distance of 66 cm or better. Impact guidance is achieved using the onboard Small-body Maneuvering Autonomous Real-time Navigation (SMART Nav) system developed by JHU/APL. The SMART Nav system ingests images from an onboard imager, performs image processing and ultimately guides the spacecraft to impact. In parallel to SMART Nav operations, the spacecraft streams images back to the ground in real-time. At least two images are required from the last twenty seconds of imaging to achieve the desired pixel sample distance. Both onboard guidance and real-time image streaming drive strict data latency requirements. These requirements are levied across multiple subsystems and interfaces, making verification challenging. The highly-integrated spacecraft design and compressed integration schedule also preclude DART from fully testing these data-streams on the flight system before launch. DART has prioritized an early, end-to-end system-test effort with engineering model components and high-fidelity testbeds to address these concerns. This risk reduction effort seeks to demonstrate critical interfaces with adequate data latencies before the start of the Spacecraft Integration and Test phase (I&T). This paper describes 1) the overall mission and spacecraft image processing and downlink driving requirements, 2) the resultant architecture, 3) the risk reduction philosophy and 4) the demonstration plan and early results.

Planetary landers need to reduce velocity at low altitude for soft landing. Traditionally, estimating velocity and altitude has been performed with radar sensors whose performance meets the specific mission needs. There are not very many options for these sensors and they are difficult to include in a flight system either due to obsolescence, prohibitive cost or difficulty in accommodation. Recently, alternative sensing modalities are being pursued including Doppler LiDAR and vision. This paper describes results from a recent helicopter field test of a binocular stereo vision system for deorbit descent and landing applications. The system consisted of two 18.6 field of view cameras mounted 1.7m apart. Post processing of the images showed ranging accuracy better than 1% up to 500m and 17 cm/s velocimetry accuracy at 37m. For a flight system these images could be input into an FPGA-based processor which processes dense stereo and visual odometry in less than 1 second to achieve the stereo ranging frame rates required for soft landing. When coupled with vision based Terrain Relative Navigation this stereo system enables landing accuracies on the order of 10m.