Mars Exploration Rover (MER) battery control board
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power Li-Ion battery power electronics batteries b↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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The care and feeding of the Mars Exploration Rover (MER) Ground Data System (GDS)
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MGD↗
Comparing Apollo and Mars Exploration Rover (MER) Operations Paradigms for Human Exploration During NASA Desert-Rats Science Operations
NASA's Desert Research and Technology Studies (D-RATS) field test is one of several analog tests that NASA conducts each year to combine operations development, technology advances and science under planetary surface conditions. The D-RATS focus is testing preliminary operational concepts for extravehicular activity (EVA) systems in the field using simulated surface operations and EVA hardware and procedures. For 2010 hardware included the Space Exploration Vehicles, Habitat Demonstration Units, Tri-ATHLETE, and a suite of new geology sample collection tools, including a self-contained GeoLab glove box for conducting in-field analysis of various collected rock samples. The D-RATS activities develop technical skills and experience for the mission planners, engineers, scientists, technicians, and astronauts responsible for realizing the goals of exploring planetary surfaces.
Yingst, R. A.↗
Mars Exploration Rover (MER) project environmental assurance program
This paper presents a comprehensive summary of the environmental assurance program for the MER project. A series of test and analysis metrics are generated. Selections of the numerous lessons that have been learned from implementation of the MER environmental assurance program are documented in this paper. They include both technical and programmatic lessons that would be helpful in improving implementation of the environmental program for future projects.
Hoffman, Alan R↗
Automatic Command Sequence Generation
Automatic Sequence Generator (Autogen) Version 3.0 software automatically generates command sequences for the Mars Reconnaissance Orbiter (MRO) and several other JPL spacecraft operated by the multi-mission support team. Autogen uses standard JPL sequencing tools like APGEN, ASP, SEQGEN, and the DOM database to automate the generation of uplink command products, Spacecraft Command Message Format (SCMF) files, and the corresponding ground command products, DSN Keywords Files (DKF). Autogen supports all the major multi-mission mission phases including the cruise, aerobraking, mapping/science, and relay mission phases. Autogen is a Perl script, which functions within the mission operations UNIX environment. It consists of two parts: a set of model files and the autogen Perl script. Autogen encodes the behaviors of the system into a model and encodes algorithms for context sensitive customizations of the modeled behaviors. The model includes knowledge of different mission phases and how the resultant command products must differ for these phases. The executable software portion of Autogen, automates the setup and use of APGEN for constructing a spacecraft activity sequence file (SASF). The setup includes file retrieval through the DOM (Distributed Object Manager), an object database used to store project files. This step retrieves all the needed input files for generating the command products. Depending on the mission phase, Autogen also uses the ASP (Automated Sequence Processor) and SEQGEN to generate the command product sent to the spacecraft. Autogen also provides the means for customizing sequences through the use of configuration files. By automating the majority of the sequencing generation process, Autogen eliminates many sequence generation errors commonly introduced by manually constructing spacecraft command sequences. Through the layering of commands into the sequence by a series of scheduling algorithms, users are able to rapidly and reliably construct the desired uplink command products. With the aid of Autogen, sequences may be produced in a matter of hours instead of weeks, with a significant reduction in the number of people on the sequence team. As a result, the uplink product generation process is significantly streamlined and mission risk is significantly reduced. Autogen is used for operations of MRO, Mars Global Surveyor (MGS), Mars Exploration Rover (MER), Mars Odyssey, and will be used for operations of Phoenix. Autogen Version 3.0 is the operational version of Autogen including the MRO adaptation for the cruise mission phase, and was also used for development of the aerobraking and mapping mission phases for MRO.
Fisher, Forest↗
MGS and Odyssey - relay satellites for the MER mission
Both Mars Global Surveyor (MGS) and Odyssey are currently in low altitude, nearly circular and highly inclined orbits about Mars. Thus, they are available adn compartible to serve as relay satellites for the Mars Exploration Rovers (MER) mission. Consequently, the MER project developed requirements for MGS to be overhead for MER-A (Spirit) at Gusev crater, at maximum elevation, mudway between lander separation and initial touchdown; in time, this was specified as 01/04/04. 04:24:55 UTC/SCET with a 30 sec tolerance.
Mars Exploration Rover (MER)↗
Software Reuse in the Planetary Context: The JPL/MIPL Mars Program Suite
Reuse greatly reduces development costs. Savings can be invested in new/improved capabilities Or returned to sponsor Worth the extra time to "do it right" Operator training greatly reduced. MIPL MER personnel can step into MSL easily because the programs are familiar. Application programs much easier to write. Can assume core capabilities exist already. Multimission Instrument (Image) Processing Lab at MIPL Responsible for the ground-based instrument data processing for (among other things) all recent in-situ Mars missions: Mars Pathfinder Mars Polar Lander (MPL) Mars Exploration Rovers (MER) Phoenix Mars Science Lab (MSL) Responsibilities for in-situ missions Reconstruction of instrument data from telemetry Systematic creation of Reduced Data Records (RDRs) for images Creation of special products for operations, science, and public outreach In the critical path for operations MIPL products required for planning the next Sol s activities
Software Design↗
Polyphony: A Workflow Orchestration Framework for Cloud Computing
Cloud Computing has delivered unprecedented compute capacity to NASA missions at affordable rates. Missions like the Mars Exploration Rovers (MER) and Mars Science Lab (MSL) are enjoying the elasticity that enables them to leverage hundreds, if not thousands, or machines for short durations without making any hardware procurements. In this paper, we describe Polyphony, a resilient, scalable, and modular framework that efficiently leverages a large set of computing resources to perform parallel computations. Polyphony can employ resources on the cloud, excess capacity on local machines, as well as spare resources on the supercomputing center, and it enables these resources to work in concert to accomplish a common goal. Polyphony is resilient to node failures, even if they occur in the middle of a transaction. We will conclude with an evaluation of a production-ready application built on top of Polyphony to perform image-processing operations of images from around the solar system, including Mars, Saturn, and Titan.
Space Exploration,↗
Martian Analogue Sample Characterization and Spectral Library Development at the Johnson Space Center
An extensive collection of Martian analogue samples housed at the Johnson Space Center is the focus of ongoing research by the JSC Mars soil genesis group and their collaborators. Because the major element composition of Martian meteorites and in situ analyses of Martian soils and rocks indicate that Mars is predominantly an iron-rich basaltic world, the focus of active sample collection and analysis is basaltic materials and their hydrolytic (both aqueous and hydrothermal) and sulfatetic alteration products. Described below are the scope of the JSC Mars analogue sample collection, the characterization process, and plans to incorporate the data into spectral libraries for the Mars 2003 Mars Exploration Rover (MER) and Mars 2005 Mars Reconnaissance Orbiter (MRO) CRISM missions.
Morris, Richard V.↗
Control in Unmanned NASA Missions
This viewgraph presentation reviews several general aspects of control for NASA space missions. Included in the review are the Entry, Descent and Landing (EDL) for both Mars Exploration Rover (MER), and Mars Science Laboratory (MSL); and the control of the MER Rover. Also included is discussion about control issues in relation to formation flying, adaptive optics and precision wavefront control, sensors for environmental control- ENose. Essential developments in the areas of control are important for increase in Autonomy/Automation is essential for deep space manned missions. Other areas of concern are robustness and the need to address life support system control issues.
wavefront control↗
MSLICE Science Activity Planner for the Mars Science Laboratory Mission
MSLICE (Mars Science Laboratory InterfaCE) is the tool used by scientists and engineers on the Mars Science Laboratory rover mission to visualize the data returned by the rover and collaboratively plan its activities. It enables users to efficiently and effectively search all mission data to find applicable products (e.g., images, targets, activity plans, sequences, etc.), view and plan the traverse of the rover in HiRISE (High Resolution Imaging Science Experiment) images, visualize data acquired by the rover, and develop, model, and validate the activities the rover will perform. MSLICE enables users to securely contribute to the mission s activity planning process from their home institutions using off-the-shelf laptop computers. This software has made use of several plug-ins (software components) developed for previous missions [e.g., Mars Exploration Rover (MER), Phoenix Mars Lander (PHX)] and other technology tasks. It has a simple, intuitive, and powerful search capability. For any given mission, there is a huge amount of data and associated metadata that is generated. To help users sort through this information, MSLICE s search interface is provided in a similar fashion as major Internet search engines. With regard to the HiRISE visualization of the rover s traverse, this view is a map of the mission that allows scientists to easily gauge where the rover has been and where it is likely to go. The map also provides the ability to correct or adjust the known position of the rover through the overlaying of images acquired from the rover on top of the HiRISE image. A user can then correct the rover s position by collocating the visible features in the overlays with the same features in the underlying HiRISE image. MSLICE users can also rapidly search all mission data for images that contain a point specified by the user in another image or panoramic mosaic. MSLICE allows the creation of targets, which provides a way for scientists to collaboratively name features on the surface of Mars. These targets can also be used to convey instrument-pointing information to the activity plan. The software allows users to develop a plan of what they would like the rover to accomplish for a given time period. When developing the plan, the user can input constraints between activities or groups of activities. MSLICE will enforce said constraints and ensure that all mission flight rules are satisfied.
Powell, Mark W.↗
Hypersonic and Supersonic Static Aerodynamics of Mars Science Laboratory Entry Vehicle
This paper describes the analysis of continuum static aerodynamics of Mars Science Laboratory (MSL) entry vehicle (EV). The method is derived from earlier work for Mars Exploration Rover (MER) and Mars Path Finder (MPF) and the appropriate additions are made in the areas where physics are different from what the prior entry systems would encounter. These additions include the considerations for the high angle of attack of MSL EV, ablation of the heatshield during entry, turbulent boundary layer, and other aspects relevant to the flight performance of MSL. Details of the work, the supporting data and conclusions of the investigation are presented.
Dyakonov, Artem A.↗
Next-generation Strategies for Human Lunar Sorties
The science community has had success in remote field experiences using two distinctly different models for humans-in-the-loop: the Apollo Science Support team (science backroom), and the robotic exploration of Mars. In the Apollo experience, the science team helped train the crew, designed geologic traverses, and made real-time decisions by reviewing audio and video transmissions and providing recommendations for geologic sampling. In contrast, the Mars Exploration Rover (MER) and Mars Science Lab (MSL) missions have been conducted entirely robotically, with significant time delays between science- driven decisions and remote field activities. Distinctive operations methods and field methodologies were developed for MER/MSL [1,2] because of the reliance on the "backroom" science team (rather than astronaut crew members) to understand the surroundings. Additionally, data are relayed to the team once per day, giving the team many hours or even days to assimilate the data and decide on a plan of action.
Cohen, B. A.↗
Multi-Mission Terrain Classifier for Safe Rover Navigation and Automated Science
We previously presented Soil Property and Object Classification (SPOC), a machine learning-based terrain classifier for Mars rovers, for automatically segmenting rover images by its surface type such as sand and bedrock. This paper presents a number of practical improvements to pave the way for potential future onboard deployment. First, we achieved 97.0% overall pixel accuracy, evaluated against the classification generated by human experts on images from Mars Science Laboratory (MSL) missions. The substantial increase in accuracy was primarily enabled by the sheer volume of data used for training; we created a new large-scale dataset of Martian terrain labels, namely AI4Mars, which contains more than 400k labels contributed by citizen scientists for 50k images taken by the Mars Exploration Rovers (MER) and Mars Science Laboratory (MSL) rover. Second, we demonstrated that SPOC can quickly adapt to a new mission landed on a previously unseen site. Specifically, we pretrained a model with MER and MSL data from the AI4Mars dataset and then adapted to the Mars 2020 Rover (M2020) by feeding a small volume of data between Sol 0 and 157; the adapted model was tested on Sol 200-203 and resulted in 84.2% overall pixel accuracy and 93.4% reliability (recall) for detecting sand, the most concerning class for rover’s traversability. Third, we found that pretraining can substantially mitigate the decline of accuracy over time. We showed that the performance of a SPOC model pretrained with the ImageNet dataset and then trained by MSL images only up to Sol 390 remains comparable to a model trained by images up to Sol 1689 on the test data after Sol 1689. Fourth, we reimplemented SPOC with a light-weight convolutional neural network (CNN), MobileNetV2, which typically runs within tens of milliseconds (ms) on mobile processors such as Qualcomm’s Snapdragon. Finally, we released the AI4Mars dataset to the public to encourage open innovation.
Ono, Masahiro↗
WATER ON MARS: EVIDENCE FROM MER MISSION RESULTS
The Mars Exploration Rover (MER) mission landed two rovers on Mars, equipped with a highly-capable suite of science instruments. The Spirit rover landed on the inside Gusev Crater on January 5, 2004, and the Opportunity rover three weeks later on Meridiani Planum. This paper summarizes some of the findings from the MER rovers related to the NASA science strategy of investigating past and present water on Mars.
Landis, Geoffrey A.↗
Mixed-initiative activity plan generator for Mars Exploration Rovers mission (MER)
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MER Mars rover activity plan↗
Automated Targeting for the MER Rovers
The Autonomous Exploration for Gathering Increased Science System (AEGIS) will soon provide automated targeting for remote sensing instruments on the Mars Exploration Rover (MER) mission, which currently which currently has two rovers exploring the surface of Mars. Currently, targets for rover remote-sensing instruments, especially narrow field-of-view instruments (such as the MER Mini- TES spectrometer or the 2011 Mars Science Laboratory (MSL) Mission ChemCam Spectrometer), must be selected manually based on imagery already on the ground with the operations team. AEGIS enables the rover flight software to analyze imagery onboard in order to autonomously select and sequence targeted remote-sensing observations in an opportunistic fashion. In this paper, we first provide some background information on the larger autonomous science framework in which AEGIS was developed. We then describe how AEGIS was specifically developed and tested on the JPL FIDO rover. Finally we discuss how AEGIS will be uploaded and used on the Mars Exploration Rover (MER) mission in early 2009.
rover autonomy↗