Engineering topics
Aaron, Seth
Publications and source records attributed to Aaron, Seth.
Camera Simulation for the Perseverance Rover’s Lander Vision System
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.
Mars 2020 Lander Vision System Flight Performance 1
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.
Mars 2020 Perseverance Entry, Descent, and Landing Simulation Statistical Analysis and Operations
No abstract provided
Assessment of M2020 Terrain Relative Landing Accuracy: Flight Performance vs Predicts
No abstract provided
Mars 2020 Perseverance Entry Descent and Landing Simulation Statistical Analysis and Operations
On February 18, 2021, the Perseverance rover and Ingenuity helicopter demonstra- tion landed at Jezero Crater. The Entry, Descent, and Landing (EDL) architecture, largely the same used to land Curiosity at Gale Crater on August 6, 2012, required high-fidelity flight dynamics simulation with two independent tools to verify per- formance. The process for creating the EDL simulation using the Dynamics Sim- ulator for Entry, Descent and Surface landing (DSENDS) tool will be discussed, along with its use for for independent verification of the EDL statistical analy- sis results and reference trajectory simulation. Analysis and usage details both in development and cruise, along with post-landing assessment of the prediction performance of the simulation, will also be discussed.
Assessment of M2020 Terrain Relative Landing Accuracy: Flight Performance vs Predicts
Terrain Relative Navigation (TRN) was a critical enabling Entry, Descent, and Landing (EDL) technology that enabled Mars 2020 mission Perseverance rover to land at Jezero crater. TRN pro-vides real-time, autonomous, map-relative position determination and generates a landing target based on a priori knowledge of hazards. The required performance for TRN was to land within 60m of the selected target. The required 60m was sub-allocated to various error sources in three major categories: targeting error, knowledge error, and control error. The targeting error is the error in selecting an appropriate landing target and the knowledge of the target on the surface. It includes the Lander Vision System (LVS) position localization with respect the ground, the synchronization between the Lander Vision System measurement and the main Navigation filter, and errors associated with the LVS Reference Map and Safe Target Selec-tion (STS). The knowledge error is the contribution of knowledge growth from the synchronization with LVS to touchdown. The control error encompasses how accurately the system could stay on the desired reference trajectory. The TRN error budget uses a combination of analysis, simulation, and hardware test-ing results to bound the various error contributions obtained during the verification and validation process. This paper first presents a description the TRN system, focusing on the architecture of LVS and STS. The paper then gives detailed overview of the TRN error budget, with a description of the major error contribu-tions in each of the three categories. Next, the paper gives the results for three versions of the error budget, pre-launch, in-flight pre-landing, and post-landing. The paper compares the pre-flight analysis, the pre-landing analysis using in-flight data during cruise, to the post-landing analysis of the TRN performance. Pre-landing analysis best estimate of the landing performance was 33m, compared to the 60m require-ment. Post-landing analysis estimated a landing accuracy of 8.53m or better, much better than the 33m pre-landing estimate. The actual post-landing imagery calculated the distance of the rover to the targeted location to be 5m. The post-landing analysis closely bounds the image-based assessment of landing accu-racy, indicating the success of the error budget architecture in bounding the landing accuracy, as well as the fidelity of the simulations used to model and predict performance.
Mars 2020 Perseverance trajectory reconstruction and performance from launch through landing
The Mars 2020 (M2020) Mission carrying Perseverance, the most advanced rover ever sent to Mars, successfully launched on an Atlas V 541 (AV-088) launch vehicle from the Eastern Test Range (ETR) at Cape Canaveral Air Force Station (CCAFS) in Florida at 11:50:00 UTC (T-Zero time) on July 30, 2020. After some station reconfiguration, carrier/telemetry were locked at both Deep Space Network (DSN) Canberra and Goldstone stations. Perseverance entered the Martian atmosphere at 20:36:50 Spacecraft Event Time (SCET) UTC, and landed inside Jezero Crater at 20:43:49 SCET UTC on February 18, 2021. Confirmation of nominal landing was received at the DSN Goldstone and Madrid tracking stations via the Mars Reconnaissance Orbiter at 20:55:11 Earth Received Time (ERT) UTC. This paper summarizes in detail the actual vs. predicted performance in terms of launch vehicle events, launch vehicle injection performance, actual DSN spacecraft lockup, trajectory correction maneuver performance, Entry, Descent, and Landing events, and overall trajectory and geometric characteristics.
Mars 2020 Perseverance Trajectory Reconstruction and Performance from Launch through Landing
The Mars 2020 (M2020) Mission carrying Perseverance, the most advanced rover ever sent to Mars, successfully launched on an Atlas V 541 (AV-088) launch vehicle from the Eastern Test Range (ETR) at Cape Canaveral Air Force Station (CCAFS) in Florida at 11:50:00 UTC (T-Zero time) on July 30, 2020. After some station reconfiguration, carrier/telemetry were locked at both Deep Space Network (DSN) Canberra and Goldstone stations. Perseverance entered the Martian atmosphere at 20:36:50 Spacecraft Event Time (SCET) UTC, and landed inside Jezero Crater at 20:43:49 SCET UTC on February 18, 2021. Confirmation of nominal landing was received at the DSN Goldstone and Madrid tracking stations via the Mars Reconnaissance Orbiter at 20:55:11 Earth Received Time (ERT) UTC. This paper summarizes in detail the actual vs. predicted performance in terms of launch vehicle events, launch vehicle injection performance, actual DSN spacecraft lockup, trajectory correction maneuver performance, Entry, Descent, and Landing events, and overall trajectory and geometric characteristics.
Mars 2020 Mission Design and Navigation Overview
Following the exceptionally successful Mars Science Laboratory mission which placed the Curiosity rover in the interior of Gale Crater in August 2012, NASA will launch the next rover in the 2020 Earth to Mars opportunity arriving to the Red Planet in February 2021 to explore areas suspected of former habitability and look for evidence of past life. This paper details the mission and navigation requirements set by the Project and how the final mission design and navigation plan satisfies those requirements.
Mars 2020 mission design and navigation overview
Following the exceptionally successful Mars Science Laboratory mission which placed the Curiosity rover in the interior of Gale Crater in August 2012, NASA will launch the next rover in the 2020 Earth to Mars opportunity arriving to the Red Planet in February 2021 to explore areas suspected of former habitability and look for evidence of past life. This paper details the mission and navigation requirements set by the Project and how the final mission design and navigation plan satisfies those requirements.
BiBlade Sampling Tool Validation for Comet Surface Environments
The BiBlade sampling chain was developed for use in a potential Comet Surface Sample Return mission. Following prior versions of the sampling tool, a new tool was developed and validated to TRL 6. Sample acquisition testing was performed across a range of comet simulants and operational conditions. Tool operation was validated in a thermal-vacuum chamber. The end-to-end sampling chain was validated including sampling, sample measurement, and sample transfer. The sampling system is now ready for flight implementation.
The Lander Vision System for Mars 2020 Entry Descent and Landing
In January 2016, the Mars 2020 project added Terrain Relative Navigation to the project baseline. This new capability helps the mission avoid large hazards in the landing ellipse, which enables the consideration of landing sites that more geologically diverse than before. This diversity should improve the quality of the samples collected by Mars 2020 for possible future return to earth. The Lander Vision System (LVS) is the sensor that provides the position fix that is used to determine where to land between hazards identified in orbital data prior to landing. This paper describes the LVS flight design for Mars 2020, a high-fidelity simulation used as a design tool and the expected LVS performance for Mars 2020.
Design and Analysis of Map Relative Localization for Access to Hazardous Landing Sites on Mars
Human and robotic planetary lander missions require accurate surface relative position knowledge to land near science targets or next to pre-deployed assets. In the absence of GPS, accurate position estimates can be obtained by automatically matching sensor data collected during descent to an on-board map. The Lander Vision System (LVS) that is being developed for Mars landing applications generates landmark matches in descent imagery and combines these with inertial data to estimate vehicle position, velocity and attitude. This paper describes recent LVS design work focused on making the map relative localization algorithms robust to challenging environmental conditions like bland terrain, appearance differences between the map and image and initial input state errors. Improved results are shown using data from a recent LVS field test campaign. This paper also fills a gap in analysis to date by assessing the performance of the LVS with data sets containing significant vertical motion including a complete data set from the Mars Science Laboratory mission, a Mars landing simulation, and field test data taken over multiple altitudes above the same scene. Accurate and robust performance is achieved for all data sets indicating that vertical motion does not play a significant role in position estimation performance.