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Space Robotics

Ground Operations Tooling

The software that runs a planetary rover is not the flight software. It is the set of ground tools a few dozen people use, once per sol, inside a shift whose length is fixed and whose inputs arrive on the relay schedule. What the tools cost is measured in sols of science lost, not in milliseconds. The environment makes this hard in three ways: the downlink that informs a plan arrives hours before the plan must be uplinked, the team is spread across institutions and time zones, and a command error cannot be corrected until the next pass.

The flown tools fall into a few families. Sequence editors and simulators (RSVP, and its physics models) build and rehearse command sequences [1][13][14]. Activity planners (the Science Activity Planner, Maestro, COCPIT) hold the plan that the sequences are derived from [2][17][6]. Glue software joins the primary tools into a uplink process [20]. Web and cloud data systems distribute downlink products to remote users [11][12][19]. A smaller set of work has not flown: ground-side automated planning prototypes [8][9][10], ground tool designs for onboard commanding [27][28], and teleoperation interfaces tested in simulators or laboratories [29][30][31]. The sections below keep that distinction explicit.

RSVP, the sequencing tool

Section titled “RSVP, the sequencing tool”

Before landing, the MER tactical uplink process was a planned sequence of downlink analysis, science activity planning, integrated planning, sequence generation, validation and approval [16]. Pathfinder sequences totaled about 100 commands per sol; MER sequences can exceed 1000 commands for one sol, which is the scale change RSVP was built for [13].

The Rover Sequencing and Visualization Program was MER’s command-level editor, built as independent applications plugged into an executive and coordinating by messages over a message-passing library derived from the Oak Ridge PVM implementation [1]. The architecture choice was deliberate: clean enforced interfaces between applications let components be written in different languages, developed independently, and distributed across machines for load balancing.

Two components carry the work. RoSE, the Rover Sequence Editor, is the textual command editor, data-driven from a command dictionary held as a configuration file so the editing interfaces are constructed at startup rather than compiled in; it assigns command identifiers, launches the other applications, and reads and writes sequences in an XML-based rover markup format [1]. HyperDrive is the three-dimensional editor: it displays the terrain, moves the rover through it, and is specialized for driving, arm motion and imaging commands, simulating the result and storing it for playback.

The validation is what makes it more than a text editor. RSVP simulates a sequence using the same flight software that runs on the rover, giving collision checks, trajectory generation error detection, and accurate duration estimates [1]. Duration estimates from HyperDrive feed back into the sequence timeline through the message bus. Perseverance’s tool keeps the same principle of validating against a model of the vehicle, and moves it into a browser [2].

The simulation inside RSVP is a physics-based model built on ROAMS configuration kinematics, with pseudo-dynamics for duration estimates, and it rehearses mobility and arm sequences before uplink [14]. The reported flight agreement figures, 5 percent of travel distance and 0.1 degree, are summary statements without sample sizes [14]. A separate slip model adds a sliding vector to the no-slip kinematic simulation; on a simulated 14 degree slope it reproduces about 14 percent downslope slip on straight arcs, and the authors call it preliminary [15]. Targeting depends on coordinate frames (Rover, Local Level and Site) indexed by a Rover Motion Counter, against a 0.5 cm targeting accuracy requirement for MER in-situ instrument placement [21].

Planning tools before COCPIT

Section titled “Planning tools before COCPIT”

MER science planning ran through the Science Activity Planner, and JPL’s Maestro and Ensemble work extended it with spatial indexing of rover products, distributed data access and a virtual field test loop [17]. MER used a one-sol tactical timeline with a single science team leader, which Cassini’s multi-investigator, multi-sequence planning does not share [18]. The glue between the five primary uplink tools (Science Activity Planner, MAPGEN, Rover Sequence Editor, SEQGEN and seq_translate) was a growing set of small scripts, CoUGAR, that began because trial runs of the uplink process ran hours long [20]. For MSL, the engineering operations lead describes the roles, process and verification testing that prepared the team and the early characterization phase after the August 2012 landing [22].

Downlink products moved to the cloud as well: MSL image processing replaced the MER in-house pipeline with Amazon Simple Workflow, S3 and SimpleDB [19]. The Web Interface for Telescience is a Java client and server tool serving distributed clients by RMI, with downlink visualization and uplink activity planning [11]. Its third generation separates a core package from mission-specific extensions for several lander and rover programs [12]. Neither WITS paper reports a measured quantity, and the multi-mission cost saving is asserted, not shown [11][12].

COCPIT, the collaborative planning tool

Section titled “COCPIT, the collaborative planning tool”

Perseverance’s planning tool is a web application: JavaScript over a node.js server, with Backbone.js for the model and view layer, D3 and SVG for the timeline, a Redis database holding plan transactions as they are made, plan state synchronized between clients over web sockets, and deployment into Docker containers on Amazon ECS through Terraform, behind a single sign-on layer, exporting into an S3 and Elasticsearch data lake shared with the rest of the ground data system [2], a move driven by the same distributed-team problem that MER solved by relocating its persistence layer [6].

It replaced MSLICE, the MSL planning interface, and inherited the collaborative timeline, constraint and planning-unit structures of Playbook, the NASA Ames tool it was built on [2]. It continuously verifies constraints, models power and data, predicts when data will downlink to a passing orbiter, assigns instrument targets, records science intent, and translates the plan into the sequences bundled for uplink.

One interface covers five planning phases: parcel development, where reusable activities and components are defined and validated; strategic planning for activities never executed on Mars before; campaign implementation, producing a look-ahead plan across 3 to 5 sols; tactical uplink for the next planning cycle; and tactical downlink, showing the currently executing sol against a moving Mars time indicator [2]. Being genuinely multi-user and remote-first mattered because the team was distributed across North America and Europe from the start of operations.

The tactical timeline is the binding constraint

Section titled “The tactical timeline is the binding constraint”

A Perseverance tactical planning shift is baselined at 9.5 hours and can start anywhere in the window from 6 am to midnight Pacific time, sliding within that window so that planning follows fresh data despite the offset between the Mars sol and the Earth day [7]. Most of the science team are not full-time on operations, which on previous missions meant only a small subset of the team could participate at all; splitting decisions between a predictive supratactical process at fixed Earth times and the reactive tactical process is the response to that.

MSL’s productivity study is the quantitative account of where the sols go. It combined structured interviews with operations personnel and analysis of three campaigns: Pahrump Hills over 19 sols, Artist’s Drive over 24 sols and 567 m, and Marias Pass over 24 sols and 130 m [3]. The identified drivers, in order of impact:

  1. Ground in the loop for target selection and drive planning, the largest single factor, because activities that generate the data a decision needs must happen before the decisional relay pass. Restricted sols, where that constraint bites hardest, made up 41 percent of the mission [3].
  2. The capacity of the tactical timeline to fill a multi-sol plan. Total activity across a multi-sol plan is lower than across the same number of single-sol plans, because developing and validating the command products takes time the shift does not have.
  3. Ground in the loop to respond to an activity outcome, for instance re-doing an observation whose lighting or targeting turned out wrong, which costs extra sols of driving and repetition.
  4. Inaccurate prediction of available vehicle resources, which restricts what gets planned; conservative activity estimates leave allocated margin unused and the vehicle idle.
  5. Communication between science and engineering and within the team.
  6. Science team engagement, degraded by part-time participation.

The resource cost of multi-sol planning is measured, and it is the cost the tactical shift imposes rather than one the vehicle imposes [3][7]:

Plan lengthFlight computer duration usedEnergy used
2-sol plans against 1-sol12 percent less7 percent less
3-sol plans against 1-sol15 percent less11 percent less

Source: [3]. Reducing the overhead was estimated to free 72 hours of additional campaign activity over the 19 sols at Pahrump Hills, 62 hours at Artist’s Drive and 69 hours at Marias Pass.

The study’s own conclusion is that the fixes are not tooling fixes: let the engineering team work effectively without knowing the exact vehicle state, let the vehicle detect a failed activity and respond onboard instead of waiting for the ground, plan multiple sols with less overhead, model resources more accurately, and improve the communication path between science and engineering [3]. The highest-satisfaction campaign, Pahrump Hills, produced fewer productive sols than the others; the walkabout strategy of a coarse survey followed by detailed revisits took longer and gave the team time to digest the data. The arm statistics for the same period show what that buys in placements [4].

What the tools cost the arm

Section titled “What the tools cost the arm”

Robotic arm activities were planned on about half of MSL’s first 200 sols, with contact science in more than 40 of them [4]. The output over those 200 sols was 6 samples, 5 by scoop and 1 by drill, and 19 portions delivered: 7 to the sample analysis instrument, 4 to the mineralogy instrument and 8 to the observation tray [4]. Placement accuracy after teach point calibration put the drill within 1 to 1.5 cm of target, and the sol 180 mini-drill hole center within 2 to 3 mm of the target selected in the microscopic imager. The drill preload test on sols 170 and 171 held preload on an overnight target for 24 hours and 40 minutes [4]. Microscopic imaging used three standoff regimes: 20 to 25 cm for context, 5 to 10 cm for stereo, and about 1 cm for high resolution [4].

Parallelism, and the hour per shift it returns

Section titled “Parallelism, and the hour per shift it returns”

Perseverance was required to drive 50 percent farther than Curiosity, 15 km in a Mars year, and to collect more than twice as many samples, which made overlapping activities a requirement rather than an optimization [5]. The flight software supports it: over 100 tasks share the 133 MHz RAD750 under the VxWorks priority preemptive scheduler with rate monotonic priorities, and up to 16 sequences can execute in parallel.

The measured return, tracked by a tool built to identify executed parallel activities automatically, was 329 hours 52 minutes 58 seconds of cumulative parallelism by sol 570, about 30 to 35 minutes per sol returned to science on the vehicle, and about 1 hour per shift of tactical operations time across uplink and downlink combined [5]. A one hour saving per shift compounds across thousands of planning cycles. Parallel activities in operational use include neutron spectrometer sensing while driving, instrument cooling overlapped with other work, and imaging during communication window preparation.

Parallelism has to be certified rather than assumed. A surface electromagnetic interference test campaign produced a source and victim matrix of interactions, rated by operations priority and available test data, and a working group reviews new parallelism needs monthly, tests candidate scenarios on testbeds, and validates them in flight [5].

On sol 516, sample processing ran in parallel with a communication pass; the image compression background task handling a CacheCam image lost CPU to higher-priority communication tasks, the automatic camera power off triggered before the readout completed, and the image data was lost [5].

Where the tools moved to reduce latency

Section titled “Where the tools moved to reduce latency”

MER’s planning tool history is a sequence of attempts to serve operators who were no longer in the building. The Science Activity Planner was centralized on JPL workstations with file-based persistence over NFS, fast because it sat next to its storage and with no integrated search [6]. Desktop sharing over a VPN was tried and performed badly for cross-country and international users [6]. Maestro, on the Eclipse Rich Client Platform, moved to a client and server split, but its Hibernate persistence layer decomposed a plan into hundreds of interconnected rows, and at 25 ms of network latency a 300-row save took 8 seconds; a REST server reduced the round trips but added its own latency.

The resolution was to stop treating a plan as a relational structure. Plans are stored as blobs in S3, named by the hash of their contents, with a metadata row in SimpleDB holding that hash; the blob is written before the metadata transaction commits, so a crash between the two leaves an orphan rather than a corrupt database, and a daemon deletes orphans older than a day [6]. Plan searches then returned in tens of milliseconds including network latency, the largest plans retrieved in seconds, and save time grew roughly linearly with activity count with a much smaller constant than the relational implementation.

The infrastructure cost fell with it. The legacy configuration ran two REST servers at $1340 per month plus about $2500 per month of storage and backup for 2.5 TB, roughly $40,000 per year; the cloud configuration was $412.50 per month of storage with backup, $18 per month for the load balancer, $191 per month for one large machine running continuously and $340 per month for ten large machines running 21 hours a week [6]. The usage pattern is what made the old arrangement wasteful: heavy use for about 8 hours every two days, on hardware provisioned for the peak [6], against a shift that is now baselined at 9.5 hours [7].

Automated ground planning, and moving authority onboard

Section titled “Automated ground planning, and moving authority onboard”

Ground-side automation of sequence generation has been prototyped but not flown. A 2000 prototype used the ASPEN planner to generate command sequences for a proposed Mars 2003 rover built on a refurbished Sojourner; that mission did not fly [8]. The model had 27 state variables and 162 activities, 63 of which decompose directly into rover commands; it regenerated the plans for two actual Sojourner sols with results described as very similar to the human sequences, and one 136-activity sample ran in 9 seconds on a workstation [9][10]. Two sols and one run are the entire evidence.

Mars 2020 moved part of this authority into flight. Simple Planner, the operational use of the Onboard Planner, was rolled out in two phases: the first keeps the heritage activity order but lets activities grow, shrink and shift to recover idle time, and the second adds reordering, drive extension and opportunistic activities [26]. Its benefit figures, including 71 sols of energy per Earth year, are modeled extrapolations rather than measurements [26]. In the first 210 sols the rover drove 2663.65 m over 68 drives and became the first planetary rover to cover more than half its distance autonomously [25]. The Mars 2020 ground data system design drivers were set before launch as requirements and targets, not measurements [24].

A JPL study of what changes when commanding becomes goal and priority based surveys current planning and analysis tools, lists what they lack, and proposes new ones; it reports that implementation and integration are ongoing, so none has been used on a mission [27]. A companion workflow splits intent capture from downlink interpretation and adds an outcome prediction tool that clusters Monte Carlo rollouts of what the onboard executive might do; it was evaluated with eight participants and one scenario, with no control condition [28]. MER flight software updates are the other ground-heavy operation: a full load is about 8 MB of uplink product and the February 2005 patch about 2 MB, done on quiet Uplink Days followed by a Build Day [23].

Teleoperation interfaces outside the rover tactical loop

Section titled “Teleoperation interfaces outside the rover tactical loop”

Rover planning is store-and-forward. Earlier and parallel work addressed continuous or supervised control. The ten-level scale of automation used by the supervisory control literature comes from a 1978 report on undersea teleoperators [32]. A graphical operator interface for ground-based telerobotic inspection was built for Space Station Freedom, with no operator workload or detection data [31]. DLR’s MARCO framework drove the Space Station arm simulator to show ground operation of the arm, against a simulator and not flight hardware [30]. A virtual reality interface for plan-then-execute satellite servicing was compared with keyboard and mouse using nine teleoperators; no reported difference reached p=0.05 [29]. None of the interfaces in this group has operated a planetary rover.

Recent operations records, sequencing formats and tooling upkeep

Section titled “Recent operations records, sequencing formats and tooling upkeep”

Operations records now document what the ground process produced rather than only how it was built. Perseverance’s first extended mission is reported through sol 1266, with 30 km driven and 28 samples, and traces four steer-motor hard-brake events between sols 897 and 1052 to the grouped-brake avionics change [33]. The following report covers the crater rim ascent from sol 1267 to sol 1720 (September 2024 to December 2025) as cumulative totals with no uncertainty [34]. The first sample depot was built between sols 652 and 693: 207.93 m driven and 10 sample tubes dropped, from a single campaign on benign terrain [35].

Maintenance of ground software is reported for MSL. A planner team consolidated a decade of ad-hoc scripts in Perl, C, bash, JavaScript and Python 2 into a managed suite; the paper gives no before-and-after measurement of shift duration or error rate [36]. For deep space missions the sequencing format itself is changing: Psyche authors sequences in Python and plans around thruster momentum management with 15 to 50 hours between events, and its cost savings are stated without numbers [37]. SeqJSON replaces the line-oriented SASF and SATF sources on Europa Clipper and Psyche and converts back so SEQGEN still works; no error rate is reported [38].

Onboard planning and multi-rover automation continue the shift of authority described above. The Mars 2020 Onboard Planner status report covers flight software constraints, three commissioning activities in 2023 and an operations readiness test, with no scheduling performance result [39]. An ASPEN-based generator for three simulated cooperating rovers closes an analyze-plan-execute loop in simulation only [40]. A review of three ESA-funded AI systems for rover autonomy, covering rock detection, terrain classification and multi-robot human coordination, reports some headline gains that are arithmetic on its own figures [41].

Teleoperation under latency has a measured result for the Artemis Lunar Terrain Vehicle: average speed including stops fell from 3.24 kph at 0 s round trip delay to 2.56 at 4 s, 2.03 at 6 s and 1.76 at 8 s, in simulator studies with no vehicle or regolith [42].

Comparison

Section titled “Comparison”
RSVP, MERCOCPIT, Mars 2020
Formdesktop applications over a message bus [1]web application, node.js and Redis, on AWS [2]
Usersrover planners at JPL [1]distributed international team, remote-first [2]
Validationsimulation using the flight software itself, collision and trajectory checks [1]continuous constraint verification, power and data modeling [2]
Sequence outputrover markup language files [1]plans translated into bundled uplink sequences [2]
Planning phases coveredtactical sequencing [1]parcel development, strategic, campaign implementation, tactical uplink, tactical downlink [2]

What is not established

Section titled “What is not established”

The tool literature is mostly descriptive. COCPIT is described without any timeline, rework or user-study measurement, so its claimed shorter uplink is a design goal [2]. The RSVP and WITS papers report no timing, defect or operator data [1][13][11][12], and the CoUGAR claims of shorter shifts and fewer errors are not quantified [20]. The MSL productivity study covers three driving and remote-sensing campaigns from one mission, its productive-sol labeling is a team judgment, and the recoverable-hours figures are estimates [3]. The MER cloud migration rests on one client and one network with no variance [6]. The parallelism return is an average over a total, without distribution or trend [5]. Whether any ground tool change shortens a tactical shift is not shown by controlled comparison in the held sources.

References

  1. Maxwell, S. A., Cooper, B. K., Hartman, F. R., Wright, J. R., Yen, J. and Leger, C. (2004). The Design and Architecture of the Rover Sequencing and Visualization Program (RSVP). doi.org/10.2514/6.2004-621-417
    BibTeX
    @misc{maxwell2004design,
  title = {The Design and Architecture of the Rover Sequencing and Visualization Program (RSVP)},
  author = {Maxwell, Scott A. and Cooper, Brian K. and Hartman, Frank R. and Wright, John R. and Yen, Jeng and Leger, Chris},
  howpublished = {Presentation at the International Symposium on Artificial Intelligence, Robotics and Automation in Space},
  year = {2004},
  doi = {10.2514/6.2004-621-417},
  abstract = {The Rover Sequencing and Visualization Program (RSVP) is a suite of applications for composing, visualizing, and simulating sequences of spacecraft commands. The successor to the Rover Control Workstation software used in JPL's highly successful 1997 Mars Pathfinder mission, RSVP provides fast command editing and highly accurate simulation for Mars rover missions. Though it shares some conceptual and architectural similarities with its 1997 predecessor, RSVP was implemented from scratch for JPL's 2003-04 Mars Exploration Rover (MER) missions. RSVP is the software used to “drive” the MER rovers on the Martian surface, and was also used to command them during their cruise phase.}
}
  2. Deliz, I., Connell, A., Joswig, C., Kanefsky, B. and Marquez, J. (2022). COCPIT: Collaborative Activity Planning Software for Mars Perseverance Rover . IEEE Aerospace Conference, 20220003012. Source
    BibTeX
    @inproceedings{deliz2022cocpit,
  title = {{COCPIT}: Collaborative Activity Planning Software for {Mars} {Perseverance} Rover},
  author = {Deliz, Ivy and Connell, Andrea and Joswig, Chet and Kanefsky, Bob and Marquez, Jessica},
  booktitle = {IEEE Aerospace Conference},
  number = {20220003012},
  pages = {1-13},
  institution = {NASA},
  address = {Big Sky, Montana},
  year = {2022},
  doi = {10.1109/aero53065.2022.9843397},
  abstract = {Since landing on the Martian surface, the Perseverance rover has relied on a distributed team to generate commands for exploring its new environment each sol (Martian day). The team uses a complex suite of software tools to accomplish this challenging task in time for the next window of opportunity to send commands to the rover. A key piece of this software ecosystem is COCPIT (Component-based Campaign Planning, Implementation, and Tactical). COCPIT is part of the next generation of planning and scheduling software tools developed by NASA's Jet Propulsion Laboratory in partnership with NASA's Ames Research Center. COCPIT is a web-based application that allows users to collaboratively view and update the Perseverance rover's activity plans, continuously verify that the plan satisfies constraints, assign targets for directing scientific instruments, document science intent, and model power and data resources. Mars Surface Operations requires diverse expertise from team members within the Engineering, Science, Robotic, and Instrument Operations groups, distributed across North America and Europe. In order to improve efficiency and reduce risk, all teams are able to review and edit their activities simultaneously and see the effects on the plan in its entirety. As part of the Ground Data System (GDS) tool suite, COCPIT is responsible for the activity plan. It provides specialized views that allow operators to understand where there may be room for additional observations, see whether any planning constraints are being violated, and confirm that energy usage and data generation are within the defined limits. It contains details such as which filters a camera will use for a given observation, what the resolution of the images should be, where to store the data onboard, and how long the observation is expected to take. It predicts when specific data will be downlinked from the rover to a passing orbiter, so that the team knows when to expect that data on Earth for evaluation in future planning. Ultimately the information from the COCPIT plan is translated to sequences that will be bundled and radiated to Perseverance for execution. The COCPIT tool is used throughout all planning phases.}
}
  3. Gaines, D., Doran, G., Justice, H., Rabideau, G., Schaffer, S., Verma, V., Wagstaff, K., Vasavada, A., Huffman, W., Anderson, R., Mackey, R. and Estlin, T. (2017). A Case Study of Productivity Challenges in Mars Science Laboratory Operations . SpaceOps Conference. Source
    BibTeX
    @inproceedings{gaines2017case,
  title = {A Case Study of Productivity Challenges in Mars Science Laboratory Operations},
  author = {Gaines, Daniel and Doran, Gary and Justice, Heather and Rabideau, Gregg and Schaffer, Steve and Verma, Vandi and Wagstaff, Kiri and Vasavada, Ashwin and Huffman, William and Anderson, Robert and Mackey, Ryan and Estlin, Tara},
  booktitle = {SpaceOps Conference},
  year = {2017},
  url = {https://dataverse.jpl.nasa.gov/dataset.xhtml?persistentId=hdl:2014/46525}
}
  4. Robinson, M., Collins, C., Leger, P., Carsten, J., Tompkins, V., Hartman, F. and Yen, J. (2013). In-Situ Operations and Planning for the Mars Science Laboratory Robotic Arm: The First 200 Sols . IEEE International Conference on Systems, Man and Cybernetics. Source
    BibTeX
    @inproceedings{robinson2013situ,
  title = {In-Situ Operations and Planning for the Mars Science Laboratory Robotic Arm: The First 200 Sols},
  author = {Robinson, Matthew and Collins, Curtis and Leger, Paul and Carsten, Joseph and Tompkins, Vandana and Hartman, Frank and Yen, Jeng},
  booktitle = {IEEE International Conference on Systems, Man and Cybernetics},
  volume = {tbd},
  pages = {153-158},
  year = {2013},
  doi = {10.1109/sysose.2013.6575259},
  abstract = {The Robotic Arm (RA) has operated for more than 200 Martian solar days (or sols) since the Mars Science Laboratory rover touched down in Gale Crater on August 5, 2012. During the first seven months on Mars the robotic arm has performed multiple contact science sols including the positioning of the Alpha Particle X-Ray Spectrometer (APXS) and/or Mars Hand Lens Imager (MAHLI) with respect to rocks or loose regolith targets. The RA has supported sample acquisition using both the scoop and drill, sample processing with CHIMRA (Collection and Handling for In- Situ Martian Rock Analysis), and delivery of sample portions to the observation tray, and the SAM (Sample Analysis at Mars) and CHEMIN (Chemistry and Mineralogy) science instruments. This paper describes the planning and execution of robotic arm activities during surface operations, and reviews robotic arm performance results from Mars to date.}
}
  5. Siegfriedt, R. S., Girerd, A., Hazelrig, J., Gaines, D., Fosse, E., Appakonam, A., Waldram, N., Plave, A. and Rozek, M. (2023). Enabling Parallel Activities for Mars 2020 Rover Surface Operations . IEEE Aerospace Conference. Source
    BibTeX
    @inproceedings{siegfriedt2023enabling,
  title = {Enabling Parallel Activities for Mars 2020 Rover Surface Operations},
  author = {Siegfriedt, Rebekah S. and Girerd, Andre and Hazelrig, James and Gaines, Daniel and Fosse, Elyse and Appakonam, Ashwin and Waldram, Nicholas and Plave, Andrew and Rozek, Matthew},
  booktitle = {IEEE Aerospace Conference},
  address = {Big Sky, Montana},
  year = {2023},
  doi = {10.48577/jpl.obe5e4},
  abstract = {Level 1 requirements levied on the Mars 2020 mission from NASA Headquarters challenged the Mars 2020 surface mission (also known as the Perseverance rover) to perform significantly better relative to the 2012 Mars Science Lab (MSL) surface mission (also known as the Curiosity rover). In one and a quarter Mars years, the Perseverance rover was expected to drive 50 percent farther and collect over twice as many samples as its predecessor, Curiosity. In order to reach these requirements, Mars 2020 came up with five guiding principles, two of which were “Be fast, be flexible: perform flight and ground functions more quickly” and “Do more science: increase the time that the vehicle is actively pursuing science on Mars''. These guiding principles required the mission to lay the groundwork early on for more surface activities to be performed in parallel, such as thinking while driving. Parallelism enabled both direct efficiency gains, such as completing multiple activities simultaneously during a given time frame, and indirect gains, such as increased available energy and better informed tactical planning due to more data being available sooner. These efficiency gains allowed the rover to complete more complex plans and better handle adversity such as data return disruptions. Although the mission planned to test and approve most of the parallel activities in the development phase, limitations on budget, time and personnel drove the majority of parallel activity testing and approvals to occur in the operations phase. Ideally, any activity can run concurrently with any other activity, but constraints such as Electromagnetic Interference (EMI), hardware safety, and resource utilization prevent some activities from running in parallel. In order to coordinate these behaviors on the rover, the mission needed an efficient and streamlined mechanism to manage them on the ground and maximize the amount of time the rover could pursue science on Mars. In response to this challenge, the Mars 2020 Surface Operations Team overhauled a legacy product from MSL called the Parallelism Matrix. Over the course of the primary mission, the Mars 2020 Parallelism Team made the following improvements: established a process to triage untested activity parallelism, enabled automatic updates to the matrix after downlinked data was analyzed, created a thorough testing campaign to investigate and approve untested parallel interactions, and provided a tool to autonomously track executed parallel activities as soon as data hit the ground.This paper will discuss: similarities and differences between the Perseverance and Curiosity missions with respect to maximizing science exploration time, Mars 2020’s new Flight Software (FSW) capabilities that enabled more parallelism, the Surface EMI testing campaign, the Surface Operations Parallelism Working Group efforts, Surface Operations parallelism tools and processes, the current state of parallel activity efforts on Mars 2020, the measurable benefits and achievements of parallelism, and the lessons learned and recommendations for the future.}
}
  6. Joswig, J. C. and Shams, K. S. (2011). Redefining Tactical Operations for MER Using Cloud Computing . IEEE Aerospace Conference. Source
    BibTeX
    @inproceedings{joswig2011redefining,
  title = {Redefining Tactical Operations for MER Using Cloud Computing},
  author = {Joswig, Joseph C. and Shams, Khawaja S.},
  booktitle = {IEEE Aerospace Conference},
  pages = {1-7},
  address = {Big Sky, Montana},
  year = {2011},
  doi = {10.1109/aero.2011.5747555},
  abstract = {The Mars Exploration Rover Mission (MER) includes the twin rovers, Spirit and Opportunity, which have been performing geological research and surface exploration since early 2004. The rovers' durability well beyond their original prime mission (90 sols or Martian days) has allowed them to be a valuable platform for scientific research for well over 2000 sols, but as a by-product it has produced new challenges in providing efficient and cost-effective tactical operational planning. An early stage process adaptation was the move to distributed operations as mission scientists returned to their places of work in the summer of 2004, but they would still came together via teleconference and connected software to plan rover activities a few times a week. This distributed model has worked well since, but it requires the purchase, operation, and maintenance of a dedicated infrastructure at the Jet Propulsion Laboratory. This server infrastructure is costly to operate and the periodic nature of its usage (typically heavy usage for 8 hours every 2 days) has made moving to a cloud based tactical infrastructure an extremely tempting proposition. In this paper we will review both past and current implementations of the tactical planning application focusing on remote plan saving and discuss the unique challenges present with long-latency, distributed operations. We then detail the motivations behind our move to cloud based computing services and as well as our system design and implementation. We will discuss security and reliability concerns and how they were addressed.}
}
  7. Milkovich, S. M., Stack, K. M., Sun, V. Z., Maxwell, K., Kronyak, R., Schnadt, S. L., Steadman, K. and Spanovich, N. (2022). Balancing Predictive and Reactive Science Planning for Mars 2020 Perseverance . IEEE Aerospace Conference. Source
    BibTeX
    @inproceedings{milkovich2022balancing,
  title = {Balancing Predictive and Reactive Science Planning for Mars 2020 Perseverance},
  author = {Milkovich, Sarah M. and Stack, Kathryn M. and Sun, Vivian Z. and Maxwell, Kimberly and Kronyak, Rachel and Schnadt, Sara L. and Steadman, Kimberly and Spanovich, Nicole},
  booktitle = {IEEE Aerospace Conference},
  pages = {1-12},
  address = {Big Sky, Montana},
  year = {2022},
  doi = {10.1109/aero53065.2022.9843572},
  abstract = {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.}
}
  8. Sherwood, R., Estlin, T., Chien, S., Rabideau, G., Engelhardt, B. and Mishkin, A. (2000). An Automated Rover Command Generation Prototype for the Mars 2003 Marie Curie Rover . SpaceOps. ntrs.nasa.gov/citations/20060032782
    BibTeX
    @misc{sherwood2000automated,
  title = {An Automated Rover Command Generation Prototype for the Mars 2003 Marie Curie Rover},
  author = {Sherwood, R. and Estlin, T. and Chien, S. and Rabideau, G. and Engelhardt, B. and Mishkin, A.},
  journal = {SpaceOps},
  year = {2000},
  url = {https://ntrs.nasa.gov/citations/20060032782},
  abstract = {This paper discusses a proof-of-concept prototype for ground-based automatic generation of validated rover command sequences from high-level science and engineering activities.}
}
  9. Sherwood, R., Mishkin, A., Estlin, T., Chien, S., Backes, P., Cooper, B., Maxwell, S. and Rabideau, G. (2001). Autonomously generating operations sequences for a Mars Rover using AI-based planning . IEEE/RSJ International Conference on Intelligent Robots and Systems. Source
    BibTeX
    @inproceedings{sherwood2001autonomously,
  title = {Autonomously generating operations sequences for a Mars Rover using AI-based planning},
  author = {Sherwood, Rob and Mishkin, Andrew and Estlin, Tara and Chien, Steve and Backes, Paul and Cooper, Brian and Maxwell, Scott and Rabideau, Gregg},
  booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
  volume = {2},
  pages = {803-808},
  publisher = {IEEE},
  year = {2001},
  doi = {10.1109/iros.2001.976267},
  abstract = {This paper discusses a proof-of-concept prototype for ground-based automatic generation of validated rover command sequences. This prototype is based on ASPEN (Automated Scheduling and Planning Environment). This Artificial Intelligence (AI) based planning and scheduling system will automatically generate a command sequence that will execute: within resource constraints and satisfy flight rules. An automated planning and scheduling system encodes rover design knowledge and uses the search and reasoning techniques to automatically generate low-level command sequences while respecting the rover operability constraints. This prototype planning system has been field-tested using the Rocky-7 rover at JPL, and will be field-tested on more complex rovers to prove its effectiveness before transferring the technology to flight operations for an upcoming NASA mission. The goal-driven commanding of planetary rovers greatly reduces the requirements for highly skilled rover engineering personnel. This in turn greatly reduces mission operations costs and permits a faster response to changes in rover states.}
}
  10. Sherwood, R., Mishkin, A., Estlin, T., Chien, S., Engelhardt, B., Cooper, B. and Rabideau, G. (2001). A tool for autonomous ground-based rover planning . Aerospace. Source
    BibTeX
    @inproceedings{sherwood2001tool,
  title = {A tool for autonomous ground-based rover planning},
  author = {Sherwood, R. and Mishkin, A. and Estlin, T. and Chien, S. and Engelhardt, Barbara and Cooper, B. and Rabideau, Gregg},
  booktitle = {Aerospace},
  publisher = {JPL Open Repository},
  year = {2001},
  url = {https://dataverse.jpl.nasa.gov/dataset.xhtml?persistentId=hdl:2014/16522}
}
  11. Backes, P., Tso, K., Norris, J. S. and Tharp, G. (2000). Internet-based Ground Operations for Mars Lander and Rover Missions . Beyond Webcams. Source
    BibTeX
    @inproceedings{backes2000internet,
  title = {Internet-based Ground Operations for Mars Lander and Rover Missions},
  author = {Backes, P. and Tso, K. and Norris, Jeffrey S. and Tharp, Greg},
  booktitle = {Beyond Webcams},
  pages = {219-232},
  publisher = {The MIT Press},
  year = {2000},
  doi = {10.7551/mitpress/1588.003.0019}
}
  12. Backes, P. G., Norris, J. S., Powell, M. W. and Vona, M. A. (2002). Multi-mission activity planning for Mars Lander and Rover missions . IEEE Aerospace Conference. Source
    BibTeX
    @inproceedings{backes2002multi,
  title = {Multi-mission activity planning for Mars Lander and Rover missions},
  author = {Backes, P. G. and Norris, Jeffrey S. and Powell, Mark W. and Vona, Marsette A.},
  booktitle = {IEEE Aerospace Conference},
  volume = {2},
  pages = {877-886},
  publisher = {IEEE},
  year = {2002},
  doi = {10.1109/aero.2004.1367687},
  abstract = {The Web interface for telescience (WITS) provides downlink data visualization and uplink activity planning for multiple Mars lander and rover missions. WITS also provides a ground data system (GDS) for terrestrial rover operations. The architecture of the WITS system that enables its multi-mission use is described. WITS has been used as the GDS for the Rocky7, FIDO, and Rocky8 rovers at JPL. It was used for command sequence generation for the Mars Polar Lander mission robotic arm and robotic arm camera are to be used for science activity planning in the 2003 Mars Exploration Rover (MER) mission. It is also planned for use in the 2007 Phoenix Mars lander mission and 2009 Mars science laboratory (MSL) rover mission. WITS is currently being integrated with the Mission Data System (MDS) for use in the MSL mission and with the coupled layer architecture for robotic autonomy (CLARAty) system for use as the GDS for terrestrial technology development landers and rovers.}
}
  13. Wright, J., Hartman, F., Cooper, B., Maxwell, S., Yen, J. and Morrison, J. (2005). Driving on the surface of Mars with the rover sequencing and visualization program . International Conference on Instrumentation, Control and Information Technology. Source
    BibTeX
    @inproceedings{wright2005driving,
  title = {Driving on the surface of Mars with the rover sequencing and visualization program},
  author = {Wright, J. and Hartman, F. and Cooper, B. and Maxwell, S. and Yen, Jeng and Morrison, Jack},
  booktitle = {International Conference on Instrumentation, Control and Information Technology},
  publisher = {JPL Open Repository},
  year = {2005},
  url = {https://dataverse.jpl.nasa.gov/dataset.xhtml?persistentId=hdl:2014/37796}
}
  14. Yen, J., Cooper, B., Hartman, F., Maxwell, S., Wright, J. and Leger, C. (2005). Physical-based simulation for Mars Exploration Rover tactical sequencing . IEEE International Conference on Systems, Man and Cybernetics. Source
    BibTeX
    @inproceedings{yen2005physical,
  title = {Physical-based simulation for Mars Exploration Rover tactical sequencing},
  author = {Yen, Jeng and Cooper, Brian and Hartman, Frank and Maxwell, Scott and Wright, John and Leger, Chris},
  booktitle = {IEEE International Conference on Systems, Man and Cybernetics},
  volume = {1},
  pages = {462-467},
  publisher = {IEEE},
  year = {2005},
  doi = {10.1109/icsmc.2005.1571189},
  abstract = {The Mars Exploration Rover (MER) mission has returned tremendous scientific information on a daily basis, owing to the efficient sequencing capability of the ground system tools. For planning the mobility and instrument deployment device (IDD) sequences, physical-based simulation is applied to achieve fast and effective sequencing of complex rover and IDD maneuvers. The sequence rehearsal tool of the Rover Sequencing and Visualization Program (RSVP) is based on modeling and simulation of the multi-body mechanical systems. Using configuration kinematics (CK) and 3D terrain models, a methodology was developed to support a real-time, interactive graphics mode for the visualization tool. The sequence simulation is carried out using the on-board flight software modules for realistic rover behavior. This enables the scientists and rover planners to effectively develop the command sequences to maximize the science return of the MER mission while maintaining rover safety. This paper describes the innovative numerical algorithms and the command sequence simulation used by the MER mission for planning surface operations.}
}
  15. Yen, J. (2007). Slip validation and prediction for Mars Exploration Rovers . JPL Open Repository. Source
    BibTeX
    @inproceedings{yen2007slip,
  title = {Slip validation and prediction for Mars Exploration Rovers},
  author = {Yen, Jeng},
  publisher = {JPL Open Repository},
  year = {2007},
  url = {https://dataverse.jpl.nasa.gov/dataset.xhtml?persistentId=hdl:2014/41348}
}
  16. Dias, W. C., Mishkin, A. H. and Bass, D. S. (2002). Mars Exploration Rover (MER) surface mission tactical uplink concepts, processes, and tools . SpaceOps Conference. Source
    BibTeX
    @inproceedings{dias2002mars,
  title = {Mars Exploration Rover (MER) surface mission tactical uplink concepts, processes, and tools},
  author = {Dias, W. C. and Mishkin, A. H. and Bass, D. S.},
  booktitle = {SpaceOps Conference},
  year = {2002},
  url = {https://ntrs.nasa.gov/citations/20060030824}
}
  17. Norris, J. S., Powell, M. W., Fox, J. M., Rabe, K. J. and Shu, I.-H. (2005). Science operations interfaces for Mars surface exploration . IEEE International Conference on Systems, Man and Cybernetics. Source
    BibTeX
    @inproceedings{norris2005science,
  title = {Science operations interfaces for Mars surface exploration},
  author = {Norris, Jeffrey S. and Powell, Mark W. and Fox, Jason M. and Rabe, Kenneth J. and Shu, I-Hsiang},
  booktitle = {IEEE International Conference on Systems, Man and Cybernetics},
  volume = {2},
  pages = {1365-1371},
  year = {2005},
  doi = {10.1109/icsmc.2005.1571337},
  abstract = {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. The overview of SAP is then followed by a report on a number of new development efforts that aim to improve on the capabilities of SAP for future missions. The selected areas discussed herein include the application of geographical information systems in tactical downlink analysis, new strategies for distributed data access and planning support, a virtual field test capability to support “what if?” scenarios for new technologies and mission concepts, and the use of agile development methods to improve the development of Mars surface mission support software as a whole.}
}
  18. Mishkin, A. and Larsen, B. (2006). Implementing distributed operations: a comparison of two Deep Space Missions . SpaceOps Conference. Source
    BibTeX
    @inproceedings{mishkin2006implementing,
  title = {Implementing distributed operations: a comparison of two Deep Space Missions},
  author = {Mishkin, Andrew and Larsen, Barbara},
  booktitle = {SpaceOps Conference},
  year = {2006},
  doi = {10.2514/6.2006-5768},
  abstract = {Two very different deep space exploration missions--Mars Exploration Rover and Cassini--have made use of distributed operations for their science teams. In the case of MER, the distributed operations capability was implemented only after the prime mission was completed, as the rovers continued to operate well in excess of their expected mission lifetimes; Cassini, designed for a mission of more than ten years, had planned for distributed operations from its inception. The rapid command turnaround timeline of MER, as well as many of the operations features implemented to support it, have proven to be conducive to distributed operations. These features include: a single science team leader during the tactical operations timeline, highly integrated science and engineering teams, processes and file structures designed to permit multiple team members to work in parallel to deliver sequencing products, web-based spacecraft status and planning reports for team-wide access, and near-elimination of paper products from the operations process. Additionally, MER has benefited from the initial co-location of its entire operations team, and from having a single Principal Investigator, while Cassini operations have had to reconcile multiple science teams distributed from before launch. Cassini has faced greater challenges in implementing effective distributed operations. Because extensive early planning is required to capture science opportunities on its tour and because sequence development takes significantly longer than sequence execution, multiple teams are contributing to multiple sequences concurrently. The complexity of integrating inputs from multiple teams is exacerbated by spacecraft operability issues and resource contention among the teams, each of which has their own Principal Investigator. Finally, much of the technology that MER has exploited to facilitate distributed operations was not available when the Cassini ground system was designed, although later adoption of web-based and telecommunication tools has been critical to the success of Cassini operations.}
}
  19. Knight, D. and Powell, M. (2013). Feeding people's curiosity: leveraging the cloud for automatic dissemination of Mars images . International Symposium on Space Flight Dynamics. Source
    BibTeX
    @inproceedings{knight2013feeding,
  title = {Feeding people's curiosity: leveraging the cloud for automatic dissemination of Mars images},
  author = {Knight, David and Powell, Mark},
  booktitle = {International Symposium on Space Flight Dynamics},
  publisher = {JPL Open Repository},
  year = {2013},
  url = {https://dataverse.jpl.nasa.gov/dataset.xhtml?persistentId=hdl:2014/44942}
}
  20. Pack, M. and Laubach, S. (2014). Evolution of the scope and capabilities of uplink support software for Mars surface operations . IEEE Aerospace. Source
    BibTeX
    @inproceedings{pack2014evolution,
  title = {Evolution of the scope and capabilities of uplink support software for Mars surface operations},
  author = {Pack, Marc and Laubach, Sharon},
  booktitle = {IEEE Aerospace},
  publisher = {JPL Open Repository},
  year = {2014},
  url = {https://dataverse.jpl.nasa.gov/dataset.xhtml?persistentId=hdl:2014/45560}
}
  21. Powell, M. W., Crockett, T., Fox, J. M., Joswig, J. C., Norris, J. S., Rabe, K. J., McCurdy, M. and Pyrzak, G. (2006). Targeting and Localization for Mars Rover Operations . IEEE International Conference on Information Reuse and Integration. Source
    BibTeX
    @inproceedings{powell2006targeting,
  title = {Targeting and Localization for Mars Rover Operations},
  author = {Powell, Mark W. and Crockett, Thomas and Fox, Jason M. and Joswig, Joseph C. and Norris, Jeffrey S. and Rabe, Kenneth J. and McCurdy, Michael and Pyrzak, Guy},
  booktitle = {IEEE International Conference on Information Reuse and Integration},
  pages = {23-27},
  year = {2006},
  doi = {10.1109/iri.2006.252382},
  abstract = {In this work we discuss how the quality of localization knowledge impacts the remote operation of rovers on the surface of Mars. We look at the techniques of localization estimation used in the Mars pathfinder and Mars exploration rover missions. We discuss the virtues and shortcomings of existing approaches and new improvements in the latest operations tools used to support the Mars exploration rover missions and rover technology development tasks at the Jet Propulsion Laboratory. We conclude with future directions we plan to explore in improving the localization knowledge available for operations and more effective targeting of rovers and their instrument payloads}
}
  22. Samuels, J. A. (2013). The preparation for and execution of engineering operations for the Mars Curiosity Rover Mission . Embedded Online Conference. Source
    BibTeX
    @inproceedings{samuels2013preparation,
  title = {The preparation for and execution of engineering operations for the Mars Curiosity Rover Mission},
  author = {Samuels, Jessica A.},
  booktitle = {Embedded Online Conference},
  publisher = {JPL Open Repository},
  year = {2013},
  url = {https://dataverse.jpl.nasa.gov/dataset.xhtml?persistentId=hdl:2014/43981}
}
  23. Greco, M. E. and Snyder, J. F. (2005). Operational Modification of the Mars Exploration Rovers Flight Software . IEEE International Conference on Systems, Man and Cybernetics. Source
    BibTeX
    @inproceedings{greco2005operational,
  title = {Operational Modification of the Mars Exploration Rovers Flight Software},
  author = {Greco, Martin E. and Snyder, Joseph F.},
  booktitle = {IEEE International Conference on Systems, Man and Cybernetics},
  address = {Waikoloa, Hawaii},
  year = {2005},
  url = {https://dataverse.jpl.nasa.gov/dataset.xhtml?persistentId=hdl:2014/37496}
}
  24. Wilson, M., Trosper, J. and Abilleira, F. (2017). NASA Mars 2020 landed mission development. dataverse.jpl.nasa.gov/dataset.xhtml
    BibTeX
    @misc{wilson2017nasa,
  title = {NASA Mars 2020 landed mission development},
  author = {Wilson, Michael and Trosper, Jennifer and Abilleira, Fernando},
  year = {2017},
  url = {https://dataverse.jpl.nasa.gov/dataset.xhtml?persistentId=hdl:2014/45965}
}
  25. Verma, V., Hartman, F., Rankin, A., Maimone, M., Del Sesto, T., Toupet, O., Graser, E., Myint, S., Davis, K., Klein, D., Koch, J., Brooks, S., Bailey, P., Justice, H., Dolci, M. and Ono, H. (2022). First 210 Solar Days of Mars 2020 Perseverance Robotic Operations: Mobility, Robotic Arm, Sampling, and Helicopter . IEEE Aerospace Conference. Source
    BibTeX
    @inproceedings{verma2022first,
  title = {First 210 Solar Days of Mars 2020 Perseverance Robotic Operations: Mobility, Robotic Arm, Sampling, and Helicopter},
  author = {Verma, Vandi and Hartman, Frank and Rankin, Arturo and Maimone, Mark and Del Sesto, Tyler and Toupet, Olivier and Graser, Evan and Myint, Steven and Davis, Kevin and Klein, Douglas and Koch, Justin and Brooks, Sawyer and Bailey, Philip and Justice, Heather and Dolci, Marco and Ono, Hiro},
  booktitle = {IEEE Aerospace Conference},
  pages = {1-20},
  address = {Big Sky, Montana},
  year = {2022},
  doi = {10.1109/aero53065.2022.9843204}
}
  26. Waldram, N., Sosland Siegfriedt, R., Chien, S., Gaines, D., Hazelrig, J., Connell, A., Reich, K., Parjan, S. and Francis, R. (2024). "Crawl, Walk, Then Run - Simple Planner's Incremental Roll-Out in Perseverance Operations . IEEE Aerospace Conference. Source
    BibTeX
    @inproceedings{waldram2024crawl,
  title = {"Crawl, Walk, Then Run - Simple Planner's Incremental Roll-Out in Perseverance Operations},
  author = {Waldram, Nick and Sosland Siegfriedt, Rebekah and Chien, Steve and Gaines, Dan and Hazelrig, James and Connell, Andrea and Reich, Kevin and Parjan, Shreya and Francis, Raymond},
  booktitle = {IEEE Aerospace Conference},
  publisher = {JPL Open Repository},
  year = {2024},
  doi = {10.48577/jpl.mrefnq}
}
  27. Castano, R., Rossi, F., Vaquero, T. S., Verma, V., Allard, D., Amini, R., Barrett, A., Choukroun, M., Davidoff, S., Dhamani, N., Francis, R., Hofstadter, M., Huffmann, B., Ingham, M., Jasour, A., Jorritsma, M., Van Wyk, E. and Rabideau, G. (2023). Operating Deep Space Autonomous Spacecraft: Ground Processes and Tools for Operability and Trust . SpaceOps The International Conference on Space Operations. Source
    BibTeX
    @inproceedings{castano2023operating,
  title = {Operating Deep Space Autonomous Spacecraft: Ground Processes and Tools for Operability and Trust},
  author = {Castano, Rebecca and Rossi, Federico and Vaquero, Tiago Stegun and Verma, Vandi and Allard, Dan and Amini, Rashied and Barrett, Anthony and Choukroun, Mathieu and Davidoff, Scott and Dhamani, Nihal and Francis, Raymond and Hofstadter, Mark and Huffmann, Bennett and Ingham, Michel and Jasour, Ashkan and Jorritsma, Marijke and Van Wyk, Ellen and Rabideau, Gregg},
  booktitle = {SpaceOps The International Conference on Space Operations},
  publisher = {JPL Open Repository},
  year = {2023},
  doi = {10.48577/jpl.xefelw}
}
  28. Rossi, F., Vaquero, T. S., Jorristma, M., Van Wyk, E., Huffman, B. W., Allard, D. A., Dhamani, N. N., Davidoff, S., Jasour, A., Barrett, A. C., Amini, R., Choukroun, M., Francis, R., Hofstadter, M., Ingham, M. D., Verma, V. and Castano, R. (2023). Workflows, user interfaces, and algorithms for operations of autonomous spacecraft . IEEE Aerospace Conference. Source
    BibTeX
    @inproceedings{rossi2023workflows,
  title = {Workflows, user interfaces, and algorithms for operations of autonomous spacecraft},
  author = {Rossi, Federico and Vaquero, Tiago Stegun and Jorristma, Marijke and Van Wyk, Ellen and Huffman, Bennett W. and Allard, Dan A. and Dhamani, Nihal N. and Davidoff, Scott and Jasour, Ashkan and Barrett, Anthony C. and Amini, Rashied and Choukroun, Mathieu and Francis, Raymond and Hofstadter, Mark and Ingham, Michel D. and Verma, Vandi and Castano, Rebecca},
  booktitle = {IEEE Aerospace Conference},
  publisher = {JPL Open Repository},
  year = {2023},
  doi = {10.48577/jpl.25kbpu}
}
  29. Pryor, W., Wang, L. J., Chatterjee, A., Vagvolgyi, B. P., Deguet, A., Leonard, S., Whitcomb, L. L. and Kazanzides, P. (2023). A Virtual Reality Planning Environment for High-Risk, High-Latency Teleoperation . IEEE International Conference on Robotics and Automation, 20230007291. Source
    BibTeX
    @inproceedings{pryor2023virtual,
  title = {A Virtual Reality Planning Environment for High-Risk, High-Latency Teleoperation},
  author = {Pryor, Will and Wang, Liam J. and Chatterjee, Arko and Vagvolgyi, Balazs P. and Deguet, Anton and Leonard, Simon and Whitcomb, Louis L. and Kazanzides, Peter},
  booktitle = {IEEE International Conference on Robotics and Automation},
  number = {20230007291},
  pages = {11619--11625},
  institution = {NASA},
  year = {2023},
  doi = {10.1109/icra48891.2023.10161029},
  abstract = {Teleoperation of robots in space is challenging due to high latency and limited workspace visibility. Previously, the Interactive Planning and Supervised Execution (IPSE) and Augmented Virtuality systems were developed to reduce failure risk. These tools were visualized on a 3D da Vinci surgical console and operated using the da Vinci manipulators or visualized on conventional monitors and operated with a keyboard and mouse. Experimental studies indicated operator preference for the latter. In this work, we develop a 3D virtual reality (VR) interface for IPSE, implemented on a Meta Quest 2 head-mounted display (HMD), and evaluate it against the prior 2D, keyboard-and-mouse-based interface. The results demonstrate improved operator load with the 3D VR interface, with no decrease in task performance, while also providing cost and portability benefits compared to the conventional 2D interface.}
}
  30. Landzettel, K., Brunner, B., Schreiber, G., Steinmetz, B.-M. and Dupuis, E. (2001). MSS Ground Control Demo with MARCO . International Symposium on Artificial Intelligence, Robotics and Automation in Space. Source
    BibTeX
    @inproceedings{landzettel2001mss,
  title = {MSS Ground Control Demo with MARCO},
  author = {Landzettel, Klaus and Brunner, Bernhard and Schreiber, Gerhard and Steinmetz, Bernhard-Michael and Dupuis, Eric},
  booktitle = {International Symposium on Artificial Intelligence, Robotics and Automation in Space},
  address = {St-Hubert, Quebec},
  year = {2001},
  url = {https://roboshare.esa.int/i-SAIRAS/isairas2001/papers/Paper_EU047.pdf}
}
  31. Kim, W. S., Tso, K. S. and Hayati, S. (1993). A Graphical Operator Interface for a Telerobotic Inspection System. dataverse.jpl.nasa.gov/api/access/datafile/36023
    BibTeX
    @misc{namekim1993graphical,
  title = {A Graphical Operator Interface for a Telerobotic Inspection System},
  author = {Kim, W. S. and Tso, K. S. and Hayati, S.},
  year = {1993},
  url = {https://dataverse.jpl.nasa.gov/api/access/datafile/36023}
}
  32. Sheridan, T. B. and Verplank, W. L. (1978). Human and Computer Control of Undersea Teleoperators . Man-Machine Systems Laboratory, Department of Mechanical Engineering, Massachusetts Institute of Technology, DTIC AD-A057655. Source
    BibTeX
    @techreport{sheridan1978human,
  title = {Human and Computer Control of Undersea Teleoperators},
  author = {Sheridan, Thomas B. and Verplank, William L.},
  number = {DTIC AD-A057655},
  institution = {Man-Machine Systems Laboratory, Department of Mechanical Engineering, Massachusetts Institute of Technology},
  year = {1978},
  doi = {10.21236/ada057655},
  abstract = {This is a review of factors pertaining to man-machine interaction in remote control of undersea vehicles, especially their manipulators and sensors. Emphasis is placed on human operator control of such teleoperator systems as a function of degree of automation, sensor-control integration and task demands for underwater search, object recovery and manipulation. Models of operator- computer performance are considered, particularly with respect to human supervisory control of semiautonomous systems. Sections of the report discuss: teleoperated submersible vehicles or work platforms; undersea tasks and how they can be analyzed; relative roles of human and computer or other control elements; control hardware (sensors, communication, propulsion, manipulation, control station) and how it affects the human controller; control software for computer- aided manipulation, including a review of various languages and algorithms presently available; human operator performance in manipulator control (a review of what we now know); present and prospective theoretical models of supervisory control; and finally, the needs for research in this area.}
}
  33. Verma, V., Maimone, M., Kaplan, K., Thiel, E., Rothenberger, N., Carsten, J., Rankin, A., Schaler, E., Graser, E., Balabanska, N., Kuhn, S. and Dor, H. (2025). Robotic Operations During Perseverance's First Extended Mission . IEEE Aerospace Conference. Source
    BibTeX
    @inproceedings{verma2025robotic,
  title = {Robotic Operations During Perseverance's First Extended Mission},
  author = {Verma, Vandi and Maimone, Mark and Kaplan, Kyle and Thiel, Ellen and Rothenberger, Noah and Carsten, Joseph and Rankin, Arturo and Schaler, Ethan and Graser, Evan and Balabanska, Nadya and Kuhn, Stephen and Dor, Harel},
  booktitle = {IEEE Aerospace Conference},
  publisher = {JPL Open Repository},
  year = {2025},
  doi = {10.48577/jpl.dq0jhi}
}
  34. Maimone, M., Verma, V., Rankin, A., Kaplan, K., Carsten, J., Schaler, E., Boroson, E., Graser, E., Srinivasan, T., Nash, J. and Chiu, D. (2026). Roving on the Edge: Robotic Operations Power Perseverance's Ascent of Jezero Crater Rim . Annual AAS Guidance, Navigation and Control Conference. Source
    BibTeX
    @inproceedings{maimone2026roving,
  title = {Roving on the Edge: Robotic Operations Power Perseverance's Ascent of Jezero Crater Rim},
  author = {Maimone, Mark and Verma, Vandi and Rankin, Arturo and Kaplan, Kyle and Carsten, Joseph and Schaler, Ethan and Boroson, Elizabeth and Graser, Evan and Srinivasan, Thirupathi and Nash, Jeremy and Chiu, Darwin},
  booktitle = {Annual AAS Guidance, Navigation and Control Conference},
  year = {2026},
  url = {https://www-robotics.jpl.nasa.gov/media/documents/2026_RO_AAS_final.pdf}
}
  35. Verma, V., Maimone, M., Kaplan, K., Srinivasan, T., Del Sesto, T., Cohen, B., Rankin, A. and Maki, J. (2024). Robotic Operations for the First Sample Depot on Mars . IEEE Aerospace Conference. Source
    BibTeX
    @inproceedings{verma2024robotic,
  title = {Robotic Operations for the First Sample Depot on Mars},
  author = {Verma, Vandi and Maimone, Mark and Kaplan, Kyle and Srinivasan, Thirupathi and Del Sesto, Tyler and Cohen, Brooklin and Rankin, Arturo and Maki, Justin},
  booktitle = {IEEE Aerospace Conference},
  address = {Big Sky, Montana},
  year = {2024},
  doi = {10.48577/jpl.jc7h9l},
  abstract = {The Perseverance rover has completed a very successful2.5 years on Mars. It has filled 24 of the 43 sample tubes itbrought to Mars and completed the Three Forks Sample Depoton January 28, 2023, where it deposited 10 of these samples onthe surface of Mars. Each deposited sample was sealed within aReturnable Sample Tube Assembly (RSTA) and attached gloveassembly (RGA). Creation of this sample depot has satisfied allof the prime mission requirements. The Mars Sample Returnmission aims to bring some of the samples that Perseverancecollects from Mars to Earth, either via direct delivery from thePerseverance rover to the Sample Retrieval Lander (SRL), orby deploying Sample Recovery Helicopters (SRH) to fly to thesamples and collect them. This paper describes the strategicplanning and tactical execution of the mobility, robotic arm,sampling and imaging activities that led to the very successfuldepot creation. A number of factors had to be consideredincluding SRH access, communication obstructions, view forimaging, and contingency handling. It discusses how the strategicplanning arrived at the plan for alternating two main typesof sols: drop and image, and drive and photoshoot. Drop andimage choreographed moves between the Perseverance externalrobotic arm and the Adaptive Caching Assembly (ACA) insidethe rover, which has a second robotic arm - the Sample HandlingArm - to image the sample before and after depositing it on theground. Drive and photoshoot consists of backing up and takingmid-drive images of the dropped sample. During the 42 Martiandays (sols) that it took to create and document the Three Forksdepot, the rover drove 207.93 meters, dropped 10 sample RGAs,and took 4000 images. Completion of this sample depot ensuresthat there will be samples for the Mars Sample Return missionto bring back to Earth for the first time.}
}
  36. Newman, E., Dor, H., Hans, M., Curtis, A., Platt, F., Hemmings, R. and Okoye, O. (2024). Maintaining Mars Rover Operations Software On A Budget . IEEE Aerospace Conference. Source
    BibTeX
    @inproceedings{newman2024maintaining,
  title = {Maintaining Mars Rover Operations Software On A Budget},
  author = {Newman, Emily and Dor, Harel and Hans, Michael and Curtis, Aaron and Platt, Flynn and Hemmings, Rory and Okoye, Onyinyechi},
  booktitle = {IEEE Aerospace Conference},
  publisher = {JPL Open Repository},
  year = {2024},
  doi = {10.48577/jpl.mwx71r}
}
  37. Khan, S., Lawler, C., Nicholas, A., Ortiz, L., Mukherji, S., Nowicki, R., Redfield, B., Voskanian, V., Mak, C., Ruderman, E. and Mallamaci, M. (2024). Psyche: Cost-Saving Innovations in Development of Planning and Sequencing Systems . IEEE Aerospace Conference. Source
    BibTeX
    @inproceedings{khan2024psyche,
  title = {Psyche: Cost-Saving Innovations in Development of Planning and Sequencing Systems},
  author = {Khan, Shaheer and Lawler, Christopher and Nicholas, Austin and Ortiz, Luis and Mukherji, Shubhodeep and Nowicki, Robert and Redfield, Benjamin and Voskanian, Vicken and Mak, Carter and Ruderman, Evan and Mallamaci, Michael},
  booktitle = {IEEE Aerospace Conference},
  publisher = {JPL Open Repository},
  year = {2024},
  doi = {10.48577/jpl.f53vga},
  abstract = {NASA Jet Propulsion Lab (JPL)’s mission Psyche will be the first of its kind in many ways: the first to explore a metal asteroid, the first to fly a Hall Effect thruster in deep space, and the first to demonstrate Deep Space Optical Communication (DSOC). Challenges presented by firsts of these nature require powerful, yet agile ground and onboard systems to be addressed in a timely and effective manner. This paper examines the ways that innovations in Psyche’s planning and sequencing systems address both common challenges faced by many missions and unique challenges arising from first ever-in-flight activities. Psyche will be the first mission to operate using JPL’s new common core flight software Flight Core Product Line (FCPL), and the usage of capabilities provided by FCPL, such as real-time telemetry reactivity, multi-level logic, and complex math operations, in Psyche’s reusable sequences enables new levels of onboard autonomy. Augmentation of FCPL’s new capabilities through a user-friendly abstraction layer in ground software, an effort undertaken in conjunction with NASA’s Europa Clipper mission, enables even more complexity in reusable sequences, as well as streamlines and simplifies the ground operation and review process. The combination of increased onboard capability in the reusable sequences with Psyche’s agile planning and scheduling ground software allows Psyche to quickly develop new solutions to complex problems and represents an advancement from the approach of previous missions. An example of one of these solutions that takes full advantage of ground and onboard capabilities is presented: a unique momentum management strategy that addresses the challenge of the “swirl torque” that arises from the use of Hall Effect Thrusters in space. In order to address the challenges of testing these advanced reusable sequences, Psyche developed a robust and comprehensive automated regression testing framework, as well as an intelligent sequence branch analysis tool, that enables quick and reliable reusable sequence re-testing against new Flight Rules and constraints that are identified, resulting in significant risk reduction and cost saving, especially when compared to the manual approach undertaken by previous missions. Analysis of these tools will prove useful to future missions that develop blocks, reusable sequences, or other onboard behaviors. A multi-mission process to parse command dictionaries into a set of Python command classes allows Psyche’s sequencers to develop sequences directly in Python, a significant improvement over the previous approach of sequencing in Domain Specific Languages (DSLs) such as Sequence Activity Type Files (SATFs). This approach of developing Python command classes provides the ability to develop sequences in any Integrated Development Environment of their choice, and results in significant cost savings by avoiding the need to develop sequence editing graphical interfaces, as have been developed for missions in the past. A comparison of this approach to sequencing against approaches by other missions such as Perseverance, InSight, Dawn, and Europa Clipper is provided. Benefits, drawbacks, and work to go for each of these innovations are discussed and lessons from Psyche’s development phase are provided.}
}
  38. Schaffer, M. and Khan, S. (2023). JSON-Based Sequencing For Deep Space Missions . International Society for Optical Engery. Source
    BibTeX
    @inproceedings{schaffer2023json,
  title = {JSON-Based Sequencing For Deep Space Missions},
  author = {Schaffer, Michael and Khan, Shaheer},
  booktitle = {International Society for Optical Engery},
  publisher = {JPL Open Repository},
  year = {2023},
  doi = {10.48577/jpl.fge4yq}
}
  39. Chien, S. (2023). MARS 2020 ONBOARD PLANNER - UPDATE AND PREPARATIONS FOR OPERATIONS . Root. doi.org/10.48577/jpl.clhai1
    BibTeX
    @misc{chien2023mars,
  title = {MARS 2020 ONBOARD PLANNER - UPDATE AND PREPARATIONS FOR OPERATIONS},
  author = {Chien, Steve},
  journal = {Root},
  year = {2023},
  doi = {10.48577/jpl.clhai1},
  abstract = {No abstract available.}
}
  40. Rabideau, G., Estlin, T. and Chien, S. (2024). Working Together: Automatic Generation of Command Sequences for Multiple Cooperating Rovers. dataverse.jpl.nasa.gov/api/access/datafile/41944
    BibTeX
    @misc{rabideau2024working,
  title = {Working Together: Automatic Generation of Command Sequences for Multiple Cooperating Rovers},
  author = {Rabideau, G. and Estlin, T. and Chien, S.},
  year = {2024},
  url = {https://dataverse.jpl.nasa.gov/api/access/datafile/41944}
}
  41. Luna, C., Field, R. L. and Kay, S. (2025). AI-Enabled Capabilities to Facilitate Next-Generation Rover Surface Operations . arXiv. Source
    BibTeX
    @article{luna2025ai,
  title = {AI-Enabled Capabilities to Facilitate Next-Generation Rover Surface Operations},
  author = {Luna, Cristina and Field, Robert L. and Kay, Steven},
  journal = {arXiv},
  year = {2025},
  doi = {10.48550/arxiv.2510.05985},
  abstract = {Current planetary rovers operate at traverse speeds of approximately 10 cm/s, fundamentally limiting exploration efficiency. This work presents integrated AI systems which significantly improve autonomy through three components: (i) the FASTNAV Far Obstacle Detector (FOD), capable of facilitating sustained 1.0 m/s speeds via computer vision-based obstacle detection; (ii) CISRU, a multi-robot coordination framework enabling human-robot collaboration for in-situ resource utilisation; and (iii) the ViBEKO and AIAXR deep learning-based terrain classification studies. Field validation in Mars analogue environments demonstrated these systems at Technology Readiness Level 4, providing measurable improvements in traverse speed, classification accuracy, and operational safety for next-generation planetary missions.}
}
  42. Litaker, H. L., Li, Z. Q., Beaton, K. H. and Lewis, J. F. (2024). Lunar Terrain Vehicle (LTV) Remote Teleoperation Studies Under Four Lunar Communication Latencies . IEEE Aerospace Conference, NASA/TM-20240001217. Source
    BibTeX
    @inproceedings{litaker2024lunar,
  title = {Lunar Terrain Vehicle (LTV) Remote Teleoperation Studies Under Four Lunar Communication Latencies},
  author = {Litaker, Harry L. and Li, Zu Qun and Beaton, Kara H. and Lewis, John F.},
  booktitle = {IEEE Aerospace Conference},
  number = {NASA/TM-20240001217},
  pages = {1-21},
  institution = {NASA Johnson Space Center},
  year = {2024},
  doi = {10.1109/aero63441.2025.11068604},
  abstract = {Remotely operating a lunar rover from Earth while subject to an Earth-Moon time delay of multiple seconds could result in a dangerous state where the vehicle is either damaged or lost. Providing the right capabilities to the remote operator to manage inherent communication latencies will be important for remote driving to be successful. NASA conducted two studies to investigate the average speed and number of kilometers per day that an operator on Earth could teleoperate a notional Artemis unpressurized rover with minimal remote operator capabilities under 0, 4, 6 and 8-second communication delay. The primary goal of these studies was to understand if an Artemis Lunar Terrain Vehicle (L TV) could cover 6 kilometers (km) in 24 hours when operated remotely. During the April 2023 evaluation, operators used an in-house simulation of the lunar surface South Pole to teleoperate a NASA government reference LTV. Operators viewed the surrounding terrain via a single, rover mast-mounted, high-resolution camera with pan/tilt/zoom capabilities; continuous communication was provided throughout all testing. Additions in the August 2023 evaluation provided remote operators with an operator-selected rate limiter to enable finer sensitivity in the hand controller and a predictive circle function to better assist operators with predicting the path the vehicle could take. All operators were able to successfully navigate and drive through six different types of terrain and five planned traverse scenarios using natural lighting under all communication delays. Results for average speeds for each communication delay was computed then used to derive the total time needed to cover 6, 8, and 20 km. Generally, remote operators drove slower when subjected to a communication latency. Subjective workload assessments revealed that while operating in a latency the overall workload significantly increased with mental demand, frustration, and performance being the primary contributing factors. Driving strategies in the 0-s delay did not vary significantly among operators; however, in the 4-s delay condition, three different driving strategies were identified. Differences in the 6-s and 8-s latency conditions indicated the operators use of the cruise control to maintain speed was more apparent. Operators started to define more specific parameters in driving strategies for general operations. This consisted of setting the vehicle into a low-speed cruise mode of approximately 1-1.5 kilometers per hour (kph). Operators noticed driving performance of the vehicle seemed to be much harder at slower speeds 0.4-0.8 kph; however, the vehicle was more responsive at speeds of 2.9-3.6 kph. Regardless of communication delay, operators used both the horizontal translation rails and the vehicle fenders as guides to predict a path for the vehicle through heavily concentrated terrain features. Additionally, operators took advantage of the predictive circle indicator on the navigation display and over 95% of the operator's navigation used the mast camera 180-degree panning function for ground truthing in terms of boulders and craters. These studies provided a “first-look” answer to a potential system requirement; however, considerable general knowledge was gained to begin to understand what it will take to make a successful lunar rover teleoperation mission.}
}