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The Mars Pathfinder Mission was a great engineering achievement for the National Aeronautics and Space Administration (NASA) and the Jet Propulsion Laboratory (JPL) which built the Pathfinder Spacecraft and the Sojourner Rover. The mechanical design of the mission hardware was critical to the success of the complex entry sequence and landed operations.
Mars Science Laboratory "Curiosity" is NASA's most recent mission to Mars, launched in November 2011, and landed in August 2012. It is a subcompact car-sized nuclear powered rover designed for a long duration mission, with an extensive suite of science instruments. Entry, descent and landing used a unique "skycrane" concept. This report describes the propulsive maneuvering operations during cruise from Earth to Mars, to control attitudes and to target the vehicle for entry. The propulsion subsystem, mission operations, and flight performance are discussed. All trajectory control maneuvers were well within accuracy requirements, and all turns and spin corrections were nominal.
This viewgraph presentation provides an overview of past and possible future applications for artifical intelligence (AI) in astronaut instruction and training. AI systems have been used in training simulation for the Hubble Space Telescope repair, the International Space Station, and operations simulation for the Mars Exploration Rovers. In the future, robots such as may work as partners with astronauts on missions such as planetary exploration and extravehicular activities.
The Mars Science Laboratory (MSL) is a spacecraft being developed by the Jet Propulsion Laboratory (JPL) for the purpose of in-situ exploration on the surface of Mars. The objective of MSL is to explore and quantitatively assess a local region on the Martian surface as a habitat for microbial life, past or present. This objective will be accomplished through the assessment of the biological potential of at least one target environment, the characterization of the geology and geochemistry of the landing region, an investigation of the planetary process relevant to past habitability, and a characterization of surface radiation. For this purpose, MSL incorporates a total of ten scientific instruments for which functions are to include, among others, atmospheric and descent imaging, chemical composition analysis, and radiation measurement. The Flight Software (FSW) system is responsible for all mission phases, including launch, cruise, entry-descent-landing, and surface operation of the rover. Because of the essential nature of flight software to project success, each of the software modules is undergoing extensive testing to identify and correct errors.
The search for life (extinct or extant) and potentially habitable bodies in our solar system and beyond is one of the 12 priority science questions outlined in the National Acadamies’ 2022 decadal survey [5]. Extraterrestrial destinations containing liquid water present an opportunity to search for life as we know it, and in recent years an increasing number of such locations have been discovered within our solar system. Several Jovian moons—Europa, Ganymede, and Callisto [10]—and the Saturnian moons Enceladus [8] and Titan [9] are known or suspected to harbor massive subsurface oceans. Of these "ocean worlds", Europa is the focus of at least one planned NASA orbiter mission, Europa Clipper [4], and an early lander mission concept, the Europa Lander [2, 3]. Whereas most robotic missions to the Moon and Mars (e.g. orbiters, rovers, landers) to date have had ground controllers on Earth tightly involved in mission operations, missions to more distant worlds will require a high degree of onboard autonomy due to long communication lags and blackouts, harsh environments (radiation, cold), and more limited battery and hardware life. The past decade has seen great advances in both AI technologies and computing scalability and performance that offer promising solutions for spacecraft autonomy and motivate the software system and research programs described in this paper. The Ocean Worlds Autonomy Testbed for Exploration, Research, and Simulation (OceanWATERS) [1], which has been in development at the NASA Ames Research Center since 2018, is a virtual environment for testing lander autonomy solutions. It is built on the Robot Operating System (ROS), runs on consumer-grade Linux workstations, and was released as open source in 2020. OceanWATERS provides a physical and visual simulation of a prototypical lander in a Europa-like environment (Figure 1). The lander was modeled after requirements and specifications made in JPL’s Europa Lander Study of 2016 [3]. Simulated lander systems include stereo cameras and spotlights mounted on an antenna mast that pans and tilts, a 6 degrees of freedom (DoF) robotic arm with a force-torque sensor and two interchangeable end effectors, and a battery pack power system. The environment consists of multiple terrain models including a highly detailed model sourced from the FROST dataset [11], simulation of surrounding planetary bodies based on an ephemeris model, and lighting from the sun with associated surface illumination, reflectance, and shadows. Operations supported by OceanWATERS include panoramic and directed imaging of the environment and lander workspace, Cartesian and joint-level arm commanding, grinding of the terrain surface (e.g. digging a trench), and scooping of ground material (Figure 2) which can be discarded or collected as science samples in a receptacle that can be emptied (science operations themselves are not simulated). These operations are realized as ROS Actions and are complimented by a wide selection of telemetry that is continually produced by each lander subsystem. The power system model is driven by the open-source Generic Software Architecture for Prognostics (GSAP) [11] that predicts the battery’s remaining useful life and other characteristics. As a testbed for high-level autonomy, OceanWATERS provides an execution framework based on PLEXIL [12], an open-source plan specification language and execution engine developed largely at Ames. NASA's initial development of OceanWATERS, as well the Ocean Worlds Lander Autonomy Testbed (OWLAT) [6], a complimentary physical testbed developed at JPL, was the first step in a plan for realizing candidate onboard autonomy solutions for such planetary landers. In 2020 NASA solicited applications for its Autonomous Robotics Research for Ocean Worlds (ARROW) program, and in 2021 the similar Concepts for Ocean worlds Life Detection Technology (COLDTech) program. Collectively six research teams, based in universities and companies across the United States, were awarded grants to develop and demonstrate autonomy solutions on OceanWATERS and OWLAT. These 1–2-year projects have now finished or are nearing completion, and a wide variety of autonomy challenges in ocean world surface missions were addressed. Prototyped and demonstrated solutions have included autonomous discovery, response and adaptation to system faults and unexpected environmental events, world model synthesis through perception, plan synthesis using learned models, methods to optimize sample target selection and prioritize science data transmission, extension of PLEXIL for stochastic decision-making, and an integration of a model of JPL’s mission-ready COLDArm [7]. Technologies used in these projects include many forms of machine learning, causal reasoning, automated planning, Markov decision processes, formal methods, and other advanced techniques. A more detailed summary of the ARROW and COLDTech projects is given herein. OceanWATERS has had significant enhancements since its open-source release in 2020. Many of its new features were driven or shaped by feedback from the ARROW and COLDTech teams and requirements of their projects. In support of enabling autonomous adaptation to spacecraft faults (a specific capability solicited by both programs), a fault injection and detection framework was developed that supports a wide and growing range of fault types such as locked joints, image loss, and battery failures. The power system model was completed and integrated into the simulator, starting as a single-cell battery model and later upgraded to a multi-cell model with associated faults such as cell disconnection. Arm/terrain interaction was improved by adding a force-torque sensor and associated faults, and an analytic dig force model based on the Balovnev bucket force equations. Environment fidelity was increased by modeling terrain deformation resulting from digging and scooping; visual improvements were made in textures, lighting, and shadows. To facilitate interoperation with OWLAT, a unified command and telemetry interface between the testbeds was developed at the ROS level, along with a PLEXIL interface. The number of lander operations was greatly expanded (e.g. with Cartesian-based arm and antenna movement), and a framework was designed for users to build their own lander actions. A GUI for PLEXIL plan selection was created (Figure 3), and an expansive set of plans were added, such as those that illustrate patterns for fault handling. This paper provides a self-contained high-level description of OceanWATERS, focusing on more detailed coverage of the aforementioned enhancements. It provides a high-level summary of the projects undertaken by participants in the ARROW and COLDTech programs and how these efforts have helped shape OceanWATERS. Finally, potential future work and directions for the testbed are listed, as likely informed by the recent planetary science decadal survey [5].
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The Surface Attitude Positioning and Pointing (SAPP) subsystem of the Mars Perseverance rover keeps track of the rover’s position and attitude on the surface of Mars. The SAPP Downlink Engineering Operations team members receive data from the rover on a daily basis. They must interpret the data to make sure the rover is staying safe and to support uplink planning. The SAPP team keeps track of the error growth in the rover’s attitude estimate due to noise in the Rover Inertial Measurement Unit’s (RIMU) gyroscopes used to propagate that attitude estimate whenever the rover is moving. Whenever this error grows to a particular threshold, SAPP is responsible for updating the onboard attitude knowledge using the RIMU’s accelerometers to estimate rover roll and pitch and sun imaging to estimate rover yaw, thereby reducing this attitude estimation error. Accurate attitude estimation is required so that the rover can successfully point its High Gain Antenna (HGA) to receive information from Earth and as a backup to the Mars orbiters used for sending data from the rover to Earth, point instruments on its Remote Sensing Mast (RSM), and support safe movement and placement of instruments by the rover’s ARM relative to the Martian surface. The Mars 2020 Engineering Operations team has been working to increase the operational efficiency of the mission and eventually move to a five-hour timeline for daily operations. In pursuit of this goal, the SAPP Engineering Operations team has automated their downlink process by developing a centralized Jupyter notebook to analyze the data received daily from the rover. The SAPP downlink Jupyter notebook automatically collects the data relevant to the SAPP subsystem and visualizes this information in plots and tables that can be easily read by downlink operators to aid them in assessing the status of the subsystem. Various Application Programming Interfaces (APIs) have been incorporated into the downlink daily notebook to automate the collection and posting of data, such as gathering and posting data products to the cloud. The SAPP team has also developed a SAPP downlink software library that includes functions to aid the notebook in processing data. In addition to assessing the SAPP subsystem on a daily basis, operators need to assess the long-term trending behavior of the subsystem over time. An automated trending process has been developed to collect information from the daily notebooks in order to plot and analyze that data in a centralized place. These daily and trending processes have expedited the SAPP downlink assessment and laid the groundwork to completely automate the SAPP downlink process so that SAPP operators are unnecessary unless something unexpected occurs. This paper will provide an overview of the functions that the SAPP subsystem carries out on a daily basis, and will then dive into the automations that have been developed for daily and trending downlink assessment. An assessment of the downlink efficiency will be provided, along with a summary of lessons learned and work to go. Finally, the authors will discuss how these types of automated spacecraft health assessments could be more broadly used within mission operations.
The Phoenix Mars Scout Lander will launch in August 2007 and land on the northern plains of Mars in May of 2008. In a departure from traditional planetary surface mission operations, it will have no direct-to-Earth communications capability and will rely entirely on Mars-orbiting relays in order to facilitate command and control as well as the return of science and engineering data. The Mars Exploration Rover missions have demonstrated the robust data-return capability using this architecture, and also have demonstrated the capability of using this method for command and control. The Phoenix mission will take the next step and incorporate this as the sole communications link. Operations for 90 Sols will need to work within the constraints of Odyssey and Mars Reconnaissance Orbiter communications availability, anomalies must be diagnosed and responded to through an intermediary and on-board fault responses must be tolerant to loss of a relay. These and other issues pose interesting challenges and changes in paradigm for traditional space operations and spacecraft architecture, and the approach proposed for the Phoenix mission is detailed herein.
The Science Activity Planner (SAP) is the science planning tool used for the Mars Exploration Rover (MER) mission. This paper begins with an overview of the software developed for MER and how it was used for science downlink analysis and activity planning.
NASA has continued interest in developing power sources which are capable of operating at low temperatures (-20 C and below) to enable future missions, such as the Mars Rover and Lander. Thus, under a program sponsored by the Mars Exploration Program, we have been involved in developing Li-ion batteries with improved low temperature performance. To accomplish this task, the focus of the research has been upon the development of advanced electrolyte systems with improved low temperature properties. This had led to the identification of a carbonate-based electrolyte, consisting of 1.0 M LiPF6 in EC + DEC + DMC (33:33:34), which has been shown to have excellent performance at -20 C in Li-ion AA-size prototype cells. Other groups are also actively engaged in developing electrolytes which can result in improved low temperature performance of Li-ion cells, including Polystor, Yardney, and Covalent. In addition to developing cells capable of operation at -20 C, there is continued interest in systems which can successfully operate at even lower temperatures (less than -30 C) and at high discharge rates (greater than C/2). Thus, we are currently focusing upon developing advanced electrolytes which are highly conductive at low temperatures and will result in cells capable of operation at -40 C. One approach to improve the low temperature conductivity of ethylene carbonate-based electrolytes involves adding co-solvents which will decrease the viscosity and extend the liquid range. Candidate solvent additives include formates, acetates, cyclic and aliphatic ethers, lactones, as well as other carbonates. Using this approach, we have prepared a number of electrolytes which contain methyl formate (MF), methyl acetate (MA), ethyl acetate (EA), ethyl proprionate (EP), and 1,2-dimethoxyethane (DME), some of which have been characterized and reported. Other groups have also reported electrolytes based on mixtures of carbonates and acetates. In the present study, electrolytes which have been identified to have good low temperature conductivity and stability were incorporated into lithium-graphite cells for evaluation. Using various electrochemical methods, including ac impedence and DC micropolarization techniques, the film formation characteristics of graphite electrodes in contact with various lectrolyte formulations was investigated.
The design of the science planning process for a space science mission needs to find a balance between operational and resource constraints and scientific decision-making. Science planning has previously been characterized as either predictive or reactive. Predictive science planning is needed when constraints drive science activities to be planned far in advance. For example, a combination of long one-way light time plus high-stakes science decisions drove the Cassini-Huygens mission to Saturn to have an extremely predictive planning process. On the other extreme, reactive science planning is needed when constraints drive science activities to be planned based on the results of the previous plan. For example, the Mars Exploration Rover mission interacted with the surface of Mars, and so the planning team needed to know the state of the rover at the end of each planning cycle before starting the next cycle. Operational and resource constraints that require management on intermediate timescales has led to the development of a science planning process between these two extremes. For example, the Mars Science Laboratory is a technically complex rover and has a parallel predictive process that allows the operations team to manage engineering constraints several days in advance while maintaining the reactive tactical planning process similar to that of MER. The Mars 2020 Perseverance rover is a technically complex rover in the MSL style, but has an added layer of science complexity: it is tasked with collecting a returnable cache of scientifically valuable samples of Mars within prime mission. Thus, the science planning process also needs to accommodate high-stakes longer-term science decisions in the style of Cassini. In order to balance the push-pull of these constraints, we have developed a science campaign-focused operational paradigm for Mars 2020 Perseverance that allows for both predictive planning to accommodate technological complexity and high-stakes science decisions as well as reactive planning to accommodate the realities of interacting with the martian surface. This paradigm influenced the design of operational processes and operational tools.
The Multi-Mission Radioisotope Thermoelectric Generator (MMRTG) has been providing power to the Mars Science Laboratory (MSL) rover, Curiosity, for surface operations since 2012. The Jet Propulsion Laboratory (JPL) has employed the Life Performance Prediction Model (LPPM) to generate predictions and outputs based upon flight data from the rover and experimental data from the thermoelectric (TE) couples. In order to make these predictions, LPPM requires various spacecraft inputs relevant to RTG performance such as fin root temperature, load voltage, general purpose heat source (GPHS) inventory, etc., combined with couple data such as thermoelectric properties, interface degradation, and sublimation properties. With tens of thousands of hours of flight data to date, LPPM can not only track performance to date, but also make predictions on the power output of the MMRTG through the 17-year end-of-design-life (EODL). These predictions are useful for mission planners and surface operators who rely on the MMRTG for power in order to perform the critical science necessary during the mission. This paper will showcase the power data extracted from the flight electronics, comparing it to the predictions and outputs generated from LPPM.
Through fly-by, orbiter, rover, and even crewed missions, National Aeronautics and Space Administration (NASA) has been extremely successful in exploring planetary bodies throughout our Solar System. The focus on increasingly complex Mars orbiter and rover missions has helped us understand how Mars has evolved over time and whether life has ever existed on the red planet. However, large strategic knowledge gaps (SKGs) still exist in our understanding of the evolution of the Solar System (e.g. the Lunar Exploration Analysis Group, Small Bodies Analysis Group, and Mars Exploration Program Analysis Group). Sending humans to these bodies is a critical part of addressing these SKGs in order to transition to a new era of planetary exploration by 2050.
Future robotic exploration missions will require autonomy in order to accomplish mission goals for operational efficiency and science return. For example, it will require three communication cycles for the Mars Exploration Rovers, Spirit and Opportunity, to place an instrument on a science target. Reducing this time necessitates highly accurate navigation, obstacle avoidance, target tracking, target analysis, manipulation, and fault diagnosis. Technologies to address these and other operational elements are currently being developed at NASA and within academia. However, infusion into missions has always been a difficult task for researchers. In order to keep risk down, mission managers are reluctant to include new technologies unless they have undergone extensive testing and verification under flight-realistic conditions. Furthermore, infusion of new technologies into missions is made more difficult by the variety of software frameworks under which these technologies are developed. Missions would like to see competing solutions demonstrated on a common platform so that they can compare performance and choose the solution best suited to their application.
Lunar construction is an expanding field within NASA’s Moon to Mars objectives that presents many challenges and requires innovative and reliable forms of autonomous operations on the surface of the Moon to further the technologies needed for human space exploration. Marshal Space Flight Center’s (MSFC) Advanced Concepts Office (ACO) addressed Lunar Infrastructure Objective LI-4 l by developing a Pre-Phase A, integrated architecture to inform a demonstration for lunar construction operations. The ACO study traded three architectures that would survey and prepare a construction area to build a landing pad out of lunar regolith using MMPACT (Moon-to-Mars Planetary Autonomous Construction Technology) platforms, rovers, and navigation outposts. The main trades examined navigation for the system/architecture, options for rover navigation, battery vs continuous tether power for the MMPACT robotic arm, and assigning site prep functionality to the rovers vs the platforms. Results of the study determined that the best options for the scenario provided would be local navigation (more accurate and continuous), a combination of Light Detection and Ranging (LiDAR) for initial site mapping with subsequent Smart Video Guidance Sensors (SVGS) to save power for construction, and using tethered power to decrease mission duration. The team also concluded that assigning site prep functionality to either the rovers or the platforms has benefits and challenges; future studies could explore having that functionality on both the rovers and platforms. Lastly, the team provided a Concept of Operations (ConOps) timeline that can be used in real-time ground demonstrations to explore the mission timeline, construction processes, autonomous operations, and communication systems that can be tested using MSFC’s lunar regolith field and Lunar Utilization Control Area (LUCA).
There have been 11 missions to the Moon this century, 10 of which have been orbital, from 5 different space agencies. China became the third country to successfully soft-land on the Moon in 2013, and the second to successfully remotely operate a rover on the lunar surface. We now have significant global datasets that, coupled with the 1990s Clementine and Lunar Prospector missions, show that the sample collection is not representative of the lithologies present on the Moon. The M3 data from the Indian Chandrayaan-1 mission have identified lithologies that are not present/under-represented in the sample collection. LRO datasets show that volcanism could be as young as 100 Ma and that significant felsic complexes exist within the lunar crust. A multi-decadal sample return campaign is the next logical step in advancing our understanding of lunar origin and evolution and Solar System processes.
This paper discusses the design concept of the Mars Rover/Sample Return (MRSR) mission. Special consideration is given to the issues of the power source, the scale and performance of the mobility subsystem, the requirements of the sampling subsystem, and the degree of automation, as well as to the features and the orbit design of a Mars orbiting vehicle (MOV) supporting the landed operations. The details of the integrated aeroshell configuration, that includes the rover, the lander, and the MOV during the Mars orbit insertion phase are described, and the diagrams of the MRSR mission and its elements are presented.
It is suggested that the system operations will be one of the most expensive parts of the Mars mission, and that, in order to reduce their cost, they should be considered during the conceptual phase of the Space Exploration Initiative (SEI) program. System operations of Space Station Freedom, Lunar outpost, and Mars Rover Sample Return are examined in order to develop a similar concept for the manned Mars mission. Factors that have to be taken into account include: (1) psychological stresses caused by long periods of isolation; (2) the effects of boredom; (3) the necessity of onboard training to maintain a high level of crew skills; and (4) the 40-min time delays between issuing and receiving a command, which make real-time flight control inoperative and require long-term decisions to be made by the ground support.