Activity Planning and Sequencing

Activity planning turns a set of science and engineering goals into a time-ordered set of commands that fits inside the vehicle’s energy, thermal, data volume and pointing constraints. Almost all of it is done on the ground. The question every onboard scheduler answers is a narrower one: given the plan the ground built and the state the vehicle is actually in, what may be moved [1].

Six families of approach appear in the sections below. Ground activity planning and sequencing tools build the timed products the vehicle runs (MER, Mars 2020, InSight, Psyche, Europa Clipper). Onboard scheduling of a ground plan re-times it against live state (Mars 2020). Continuous planning and repair regenerates the plan as state diverges (EO-1 flew it; the rover versions have run only on testbeds). Task-network executives merge planner and executive (ASTERIA flew one). Explanation and ground simulation make a flexible plan reviewable. Multi-agent and goal-directed architectures (CADRE, the Resilient Spacecraft Executive) are pre-flight or simulated. A 1995 JPL position paper already framed the choice between repetitive-task automation, special-case automation and fully linked onboard automation [26].

The cost of ground conservatism

Ground sequencing assigns each activity a fixed start time and a duration estimated conservatively enough that an overrun almost never cancels the activity that depends on it, targeted at fewer than one plan in 100 [4]. A study of Mars Science Laboratory science campaigns found submaster sequences finishing on average more than 20 percent early. That early finish is not recovered: the rover stays awake, drawing power, until the next fixed start time arrives [1][4].

The onboard scheduler exists to spend that margin. On Perseverance it is also given a job the ground used to do: generating the wakeup, shutdown, preheat and maintenance heating activities, which on MSL the operations team created by hand [1].

The Mars 2020 onboard planner

Input is a plan file the ground uplinks in place of a timed sequence, holding on the order of a few hundred candidate activities, each with priority, sequence identifier, predicted duration, average and peak power rate, data generation rate, claimed unit resources, heating requirements, required sequence engine count, CPU state requirement, a cleanup sequence with its own predicted duration, earliest, preferred, latest and cutoff times, and dependencies on other activities [1][5]. Output is a schedule plus a generated wake, sleep and heating plan [3].

The algorithm is priority-first, greedy and non-backtracking [2][3]. For each activity in ground-assigned priority order it computes the valid temporal intervals for each constraint separately and intersects them, then in a second phase checks plan-wide energy, generates the wake and sleep activities, and generates preheat and maintenance heating whose duration depends on ambient temperature at the candidate start time [4]. If all of that succeeds the activity is placed, and once placed it is never moved or deleted within a run [2].

Constraints checked are the allowed start time windows, dependencies, unit resources, state requirements and effects, data volume, UHF communication interactions, minimum battery state of charge both globally and at handover, maximum peak power, minimum sleep time and minimum awake time [2].

What it costs on the RAD750

Perseverance runs a BAE RAD750 at 133 MHz with 128 MB of DRAM and 4 GB of NAND on a separate card, under VxWorks, with the flight software written in C and decomposed into VxWorks tasks communicating by priority-queued interprocess messages [1]. The onboard planner receives only a fraction of that machine.

Quantity	Value
RAD750 measured performance	200 to 300 MIPS
Contemporary 2016 Intel Core i7	over 300,000 MIPS, over 1000 times faster
Effective speed of the scheduler’s share	several thousand times slower than a typical laptop
Conservative estimated runtime budget per invocation	60 s
Target schedule generation time	on the order of a minute
Expected invocations per sol	15 to 20

Sources: [2] for the processor and runtime budget, [3] for the schedule generation time and invocation count.

The cost of the valid-interval computation scales as O(N^2) in the number of resource impacts, and because impacts are proportional to activities, scheduling N activities is an O(N^3) algorithm [3]. The plan is therefore truncated rather than solved. The full plan specification may define up to 100 activities spanning several sols, but the scheduler considers only the 40 highest priority at a time, the “considered set”, with the remainder held in a “hopper” and promoted as considered activities finish [3]. Backtracking was rejected for the same reason: it would raise runtime [2].

Two mechanisms recover some of what non-backtracking loses. A switch group is a set of activities differing only in how much time, energy or data volume they consume, of which exactly one must be scheduled; the Multiple Scheduler Invocation approach calls the scheduler repeatedly with the most consumptive option first, then the next, until a schedule containing all requested activities is produced, which is a heavily constrained form of backtracking [3]. An expanding activity is grown by binary search to the largest valid duration [1]. Throttling imposes an increasing wait between consecutive scheduler invocations.

Because the result is sensitive to the priority order and only one static priority set is configured per plan, the ground scheduler Copilot runs a Monte Carlo analysis of sol execution variability to choose the priorities, which outperformed handcrafted static strategies [3].

What the onboard planner is allowed to change

Deployment was phased, and each phase widens the executive’s authority [5]:

Phase	What the scheduler may do	Status as published
Master and Submaster	nothing; every activity is pinned to a start time on the ground, and separate sequences give exact heater timing	the pre-autonomy baseline [5]
Simple Planner 1, “Accordion”	expand or contract the schedule while keeping activity order and time ranges fixed; take full control of CPU awake and asleep state and of heating	first Mars use planned spring 2023, standard operations from August 2023 [5]
Simple Planner 2	drop the fixed order and time range; schedule opportunistic activities after mandatory ones when time, energy and data volume allow	expected ready 2024 [5]
Simple Planner 3	commit groups, where a set of activities is scheduled only if all fit, and single selection groups, where the best affordable of several options is taken	may be descoped [5]

Mandatory activities are exempt from several constraints, may be scheduled even where an optional one could not, and are not aborted for running past their duration; the planner may still move them so long as their constraints hold [1]. Optional activities must never displace a mandatory one and are aborted if they overrun.

The onboard planner was originally to enter operations about 180 sols after landing [3]. As of September 2020 it was on hold under project schedule pressure [3]. A systems retrospective written in 2022 states that it was not mature in the initial surface flight software releases and was likely to be used only in mission extensions [29]. The capability that did run from shortly after the February 2021 landing was the ground half: taking a time grounded plan from the COCPIT planning tool and generating the wake, sleep, preheat and maintenance heating activities [4].

The flight record is in [13]. Three First Time Activities between June and August 2023 exercised, in order, a stubbed half-hour plan with early and late starts, abort on overrun and a deliberate handover failure; the first activation of an Onboard Plan File with autonomous shutdown, wakeup and preheat; and the first full sol of engineering and science activities with a nominal handover back to the master sequence [13]. The onboard planner has been in control of Perseverance since 5 October 2023 apart from solar conjunction, after a verification campaign that satisfied 154 requirements across 32 verification activities in a tiered set of venues built on plans from sols 100 to 600 [28]. The benefit figures in the deployment record are extrapolations: about 71 sols of energy per Earth year modeled from as-run activities, and a Rapid Traverse campaign that could have taken 9 sols instead of 17 under Simple Planner 2, which had not flown [13]. A reconstruction of two sols with as-run durations puts the master plan at about 10.1 hours awake against 6.8 as run on sol 206, and an onboard schedule gaining about 10 percent state of charge where the master plan lost about 21 percent; the onboard side of that comparison is a reconstruction with hindsight, not an execution [29]. The verification report publishes no runtime, processor utilization or memory figure for the planner [28].

Explaining a non-backtracking scheduler

A greedy scheduler that refuses an activity gives no reason, and the operator has to work out which constraint to relax. Crosscheck reconstructs it [2][6]. It finds the earliest scheduling step at which the failure occurred by binary search over the steps, then identifies the minimal subsets of the activity’s constraints whose valid intervals have an empty intersection, and reports those as the reason together with candidate relaxations [2]. The subset search is worst case O(n 2^n) in the number of constraints, which is tractable only because on realistic plans the largest such minimal subset observed has been three. Crosscheck’s Failure Note field names the constraints that could not be satisfied and the Failure Step field names the activity in conflict; where an activity fails during the sleep and heating phase instead, the Failure Note reports the constraint violated in that phase, such as state of charge or peak power [6]. Copilot shares its core algorithms with the onboard planner, so the ground schedule is built the same way the rover would build it [6].

Ground simulation of a flexible plan is harder than of a pinned one. SSim, the uplink simulator, uses direct copies of the onboard planner flight software modules including the scheduler, plan controller and timeline libraries, with the power and thermal interfaces stubbed because the ground has no access to real battery and temperature state [5]. A Monte Carlo mode running ground scheduling 1000 or more times with different duration predictions was designed to show operators how often all activities schedule and how much they shift, but as published it had not completed development or entered the operations process.

MEXEC: planning and execution in one system

MEXEC represents the plan as a task network rather than a sequence. Each task carries an identifier, a command, a priority, its expected effects as pre, maintenance and post impacts on timelines, the constraints required for execution, control conditions that skip it, and contingencies [7]. Timelines come in atomic, state, claimable, cumulative and cumulative-rate types. The planner cycle commits tasks to the executive, consolidates past impacts on the timelines to reduce memory, schedules tasks in the scheduling window, and optionally repairs conflicts by iterative repair and shifts tasks toward preferred times.

The window structure is what keeps the planner and executive from fighting. The plan process interval must exceed the worst-case planner cycle duration; the commit window is at least that long and determines which tasks are handed to the executive; the plan horizon and the conflict checking window both start after the commit window, so the planner cannot alter a task that may already have begun executing.

MEXEC flew on the ASTERIA 6U CubeSat from 4 to 20 September 2019, commanding observations of HD219134, New York City, the Moon and Vesta [7]. ASTERIA had no spare memory, so buffers were shrunk to make room and MEXEC was fitted into 2 MB, which limited task networks to 100 tasks or fewer, against the few hundred candidate activities a Mars 2020 plan file carries [1]. ASTERIA sequences run to thousands of commands, so tasks issue stored sequences rather than individual commands [7]. All uploaded task networks scheduled and executed in order.

Two flight-observed defects are instructive. ASTERIA reported a stored sequence complete when the command that started it was dispatched, not when the sequence finished, and task durations were inferred from relative sequence command times, so a long-running sequence could let the next task dispatch on top of it; the fix was a precondition that no stored sequence be running [7]. Separately, the planner and executive truncate system time to the nearest second, and the imprecision occasionally caused a task to dispatch or commit one second late.

The second experiment ran on the ASTERIA testbed after contact with the spacecraft was lost [7]. A long-running momentum monitor task carried a maintenance constraint on momentum magnitude, set below the fault protection threshold. When momentum exceeded it, the task failed, the executive dispatched a momentum dump sequence as an immediate response, the planner removed the science observation tasks that depended on the monitor, and a contingency rescheduled the monitor and an observation setup task once momentum dropped to a safe level. The task network crossed the threshold four times and recovered every time without tripping a fault or entering safe mode.

Endurance: planning around 100 km per lunar day

The Endurance lunar rover concept requires roughly 2500 km over four years in the South Pole-Aitken Basin, at about 100 km per lunar day, which puts resource management rather than activity selection at the center of the onboard problem [8]. MEXEC is being adapted for it, the same executive flown on ASTERIA [8].

Two constructs carry most of the work. A supporting task is bound to its primary task either by a start-after-end constraint, as a preheat is to a drive, or by a contained constraint, as maintenance heating is to a drive, and the scheduler queries the functional autonomy layer for the preheat duration given the anticipated start time and current temperatures before deciding whether the group fits [8]. Battery recharge was first left implicit, with the battery charging during idle gaps, which made the scheduler start drives as early as possible and produced chattering between short drives and brief recharge intervals at low state of charge, each drive paying its preheat overhead again. The replacement inserts an explicit recharge task whose duration is computed from the target and current battery energy divided by the difference between generation and idle power, and which typically charges to 95 percent; its remaining defect is power shunting, charging past what the upcoming tasks need [8].

Architecture followed the same cost argument. Waypoint goals were originally handled directly by the system-level autonomy, which coupled it tightly to navigation and was described as fragile and computationally inefficient; a three-layer split inserting a Drive Manager between system autonomy and navigation leaves the scheduler with fewer tasks and events to reason about. Navigation is the competing consumer, and visual odometry load grows with the square of maximum driving speed [8], the same coupling between sensing cost and drive rate that constrains Mars rovers [1].

A field test on 2 October 2025 in the JPL Mars Yard ran 6.5 hours, of which 5.2 hours were autonomous driving covering 4.1 km on a 200-waypoint route at an average 0.8 km/h, with the remaining 1.3 hours spent on communication, simulated recharge, physical battery swaps and four unplanned navigation interventions [8]. All task networks executed as expected and driving resumed after every interruption, a rate the paper notes exceeds any teleoperated lunar or Mars rover flown to date.

Continuous planning on a rover testbed

The precursor is CLEaR, which put the CASPER continuous planner over the TDL task-level executive on the Rocky 7 and Rocky 8 testbeds in the JPL Mars Yard [9]. CASPER generates an activity sequence from science goals and rover state, monitors execution, and repairs the plan by iterative repair when projections change [9]. In the reported scenario the planner ordered science targets with a traveling salesman heuristic, then repaired the plan twice for memory oversubscription by deleting a lower-priority spectrometer read, and once for a navigation deviation after the rover veered off the planned path; a separate obstacle encounter was absorbed by the executive without replanning because enough time margin remained. Position error of 5 to 10 percent of distance traveled, and Sojourner’s average heading drift of 13 degrees per day, are cited as the reason the planner must tolerate the executive’s deviations [9]. No processor, memory or runtime figure is published for the testbed runs. The flown descendant of CASPER, on Earth Observing 1 [9], is covered under onboard science autonomy.

Ground sequencing as flown: MER, InSight and Psyche

The Mars Exploration Rover process fixed the pattern the later rovers inherited. Critical data downlinked in the Mars afternoon; science planning in the Science Activity Planner fed a Science Operations Working Group meeting at 1800 local mean solar time that delivered a preliminary activity plan by 2000; an Activity Plan Approval Meeting at 2345 doubled as shift handover; and 30 or more individual sequences were integrated into one load [14]. The multi-mission uplink suite (APGEN, RSVP, SEQGEN and others) was adapted for the rovers, with a design-time model of 84 activity types and 50 resources, which are model sizes and not flight usage [18]. A ten day field test with the FIDO rover timed a 225 minute science operations cycle that was built against direct-to-Earth data and refined when UHF relay data arrived 120 minutes in [12]. The Web Interface for Telescience that the Science Activity Planner descends from had earlier sequenced the Mars Polar Lander arm camera and commanded FIDO over the Internet in an April 1999 field test [24][27]. In 2011 MER plan persistence moved to cloud storage because a remote user at 25 ms latency saving a 300 tuple plan waited nearly 8 seconds, and infrastructure cost fell from about 40 thousand to about 13 thousand dollars a year [15]. These papers report timelines and costs; none reports plan quality or rework rates [12][15][18].

InSight and Psyche show where sequencing, not scheduling, becomes the constraint. About 200 sequences uplinked after landing showed that the number of onboard files drove command and data handling boot duration, so file count became an operational limit; InSight master sequences chain wake to wake, and a mismatch over 5 minutes between a wake time and the next master sequence safes the lander [20]. Psyche’s momentum management, driven by its Hall thrusters, recurs every 15 to 50 hours and sets the plan; an integrated plan holds 400 to 800 activities over a one month plan plus two month runout, and logic that earlier missions kept in flight software lives in reusable sequences (one carries 257 commands and 46 conditionals), authored in Python instead of a domain-specific language [19]. Its cost savings are asserted without hours or staffing figures [19].

Onboard planners that have flown on other spacecraft

Remote Agent combined the HSTS planner and scheduler, the RAPS executive and Livingstone mode identification, was demonstrated on a simulated Saturn orbit insertion in a five month effort, and was selected for the 1998 Deep Space One flight [16]; the reported timings come from one simplified scenario on 1990s hardware [16]. The planner that did operate routinely was CASPER on Earth Observing 1, in the Autonomous Sciencecraft Experiment, which reached full mission success on 14 May 2004 [23]. Its cost is on record: 4 MIPS of an 8 MIPS Mongoose V shared with the executive and image processing, a 32 MB heap, a code image cut from 12 MB to 5 MB, and a stated need for over 7800 activities to plan a full week in detail, which is why the planning horizon is short [17]. That is the same truncation argument as the Mars 2020 considered set, made a decade earlier on a different computer. The flown savings figures are projections from assumed science processes [21], and the paper’s title claim of improved science return is not measured [23]. EO-1 is an orbiter; no planetary surface robot has flown CASPER.

MEXEC began as a fail-operational science restart design for a Europa flyby: after a flight software reset, fault protection restarts it, the task network is restored and a valid remainder of the plan is rebuilt, taking under 0.1 seconds on a 200 MHz RAD750 testbed in small scenarios [32]. CADRE, planned for the Moon, elects a leader every ten seconds to plan for all rovers, holds planning state redundantly on a survivor, and treats formation radar drives as parent tasks that decompose into per-rover subtasks; it was validated in five venues up to cleanroom flight models, all pre-flight [30]. The Resilient Spacecraft Executive adds risk-aware deliberative planning over habitual and reflexive layers; its rover demonstration is a scripted simulation and the briefing gives no performance figures [25].

What the testbed and the flown record do not show

The CLEaR trace [9] was repeated in a 2005 briefing that describes depth-first branch and bound planning with continuous re-planning for over-subscription, under-subscription and opportunistic science alerts; it is a slide deck with no timing or success rate [22]. During the Rapid Traverse campaign (5063.4 m in 24 driving sols, 94.8 percent planned autonomously), strategic planners chose a 4.8 km route over a 2.4 km one because AutoNav could sustain 200 to 250 m per sol on it; the process was designed for that campaign and not for nominal operations [31].

The ground tools

APGEN, the Activity Plan Generator, is part of the Advanced Multi-Mission Operations System and has been applied to Cassini, Deep Impact and EPOXI, InSight, Juno, MRO and MSL [10]. Its scheduling is a two-pass algorithm: the first pass predicts the effect of the current activity plan on spacecraft states and ground resources, and the second examines where activities may be added, given intervals over which a scheduling condition holds for a required length of time. Models are written in a domain-specific language that acquired arrays, global variables, conditionals, loops and function calls over its life, and link to external tools including SPICE, the Multi-Mission Power Analysis Tool, the Dawn attitude control verification tool and the Telecom Forecast Predictor. The Cassini cruise adaptation, the first mission application, defined 61 resources, 345 activity types and 15 constraints [10]. Simulating one encounter sequence on the Deep Impact testbed took 36 hours [10].

Aerie replaces it, along with Blackbird, COCPIT and the German Space Operations Center’s PINTA and PLATO, with a web-deployed system whose mission models are written as an embedded domain-specific language in Java rather than a custom language, so missions can use existing libraries [11]. Simulation is discrete event with integer dense time and transactional concurrent activity semantics, so two activities acting in the same time step do not observe each other’s effects until the step ends. Scheduling is constructive and priority-first over goal types: coexistence, recurrence, cardinality and composites of those. It is deployed as containerized microservices over a PostgreSQL database with a GraphQL API, with synchronous collaboration over web sockets and asynchronous collaboration by branching and merging plans. Europa Clipper is the case study. No simulation runtime or plan size figure is published.

The stated limitation is the same one that constrains the onboard scheduler: the scheduler has limited access to the mission model, cannot infer an activity’s preconditions or effects from its definition, and must simulate to find out, so the eDSL sometimes cannot express a Europa Clipper scheduling goal [11]. Explainability is called out as the open problem there too.

What is not established

No published figure gives the Mars 2020 planner’s measured runtime on the RAD750: the 60 second budget is a conservative estimate and the cost of backtracking is unquantified [2][28]. The 28 percent MSL duration over-prediction is inherited from earlier productivity studies [2]. Crosscheck’s effect on operator trust has no user study [2]. The ground-side Monte Carlo robustness analysis had not entered operations as published [5]. Aerie reports no performance or plan size figure and its Europa Clipper workflow was prospective [11]. CLEaR is one scenario with injected faults and wheel odometry [9]. MEXEC and the Endurance concept have flown only on a CubeSat and in a field test [7][8]. Autonomy savings for Simple Planner 2 and 3 are modeled, not flown [13][29].

References

Rabideau, G. and Benowitz, E. (2017). Prototyping an Onboard Scheduler for the Mars 2020 Rover . International Workshop on Planning and Scheduling for Space. Source

BibTeX

@inproceedings{rabideau2017prototyping,
  title = {Prototyping an Onboard Scheduler for the Mars 2020 Rover},
  author = {Rabideau, Gregg and Benowitz, Ed},
  booktitle = {International Workshop on Planning and Scheduling for Space},
  year = {2017},
  url = {https://dataverse.jpl.nasa.gov/dataset.xhtml?persistentId=hdl:2014/47716}
}

Agrawal, J., Yelamanchili, A. and Chien, S. (2020). Using Explainable Scheduling for the Mars 2020 Rover Mission . arXiv preprint. Source

BibTeX

@article{agrawal2020explainable,
  title = {Using Explainable Scheduling for the Mars 2020 Rover Mission},
  author = {Agrawal, Jagriti and Yelamanchili, Amruta and Chien, Steve},
  journal = {arXiv preprint},
  year = {2020},
  doi = {10.48550/arxiv.2011.08733},
  abstract = {Understanding the reasoning behind the behavior of an automated scheduling system is essential to ensure that it will be trusted and consequently used to its full capabilities in critical applications. In cases where a scheduler schedules activities in an invalid location, it is usually easy for the user to infer the missing constraint by inspecting the schedule with the invalid activity to determine the missing constraint. If a scheduler fails to schedule activities because constraints could not be satisfied, determining the cause can be more challenging. In such cases it is important to understand which constraints caused the activities to fail to be scheduled and how to alter constraints to achieve the desired schedule. In this paper, we describe such a scheduling system for NASA's Mars 2020 Perseverance Rover, as well as Crosscheck, an explainable scheduling tool that explains the scheduler behavior. The scheduling system and Crosscheck are the baseline for operational use to schedule activities for the Mars 2020 rover. As we describe, the scheduler generates a schedule given a set of activities and their constraints and Crosscheck: (1) provides a visual representation of the generated schedule; (2) analyzes and explains why activities failed to schedule given the constraints provided; and (3) provides guidance on potential constraint relaxations to enable the activities to schedule in future scheduler runs.}
}

Rabideau, G., Wong, V., Gaines, D., Agrawal, J., Chien, S., Kuhn, S., Fosse, E. and Biehl, J. (2020). Onboard Automated Scheduling for the Mars 2020 Rover . International Symposium on Artificial Intelligence, Robotics and Automation in Space. Source

BibTeX

@inproceedings{rabideau2020automated,
  title = {Onboard Automated Scheduling for the Mars 2020 Rover},
  author = {Rabideau, Gregg and Wong, Vincent and Gaines, Daniel and Agrawal, Jagriti and Chien, Steve and Kuhn, Stephen and Fosse, Elyse and Biehl, James},
  booktitle = {International Symposium on Artificial Intelligence, Robotics and Automation in Space},
  year = {2020},
  url = {https://dataverse.jpl.nasa.gov/dataset.xhtml?persistentId=hdl:2014/53238}
}

Gaines, D., Chien, S., Rabideau, G., Kuhn, S., Wong, V., Yelamanchili, A., Towey, S., Agrawal, J., Chi, W., Connell, A., Davis, E. and Lohr, C. (2020). Onboard Planning for the Mars 2020 Perseverance Rover . International Symposium on Artificial Intelligence, Robotics and Automation in Space. Source

BibTeX

@inproceedings{gaines2020onboard,
  title = {Onboard Planning for the Mars 2020 Perseverance Rover},
  author = {Gaines, Daniel and Chien, Steve and Rabideau, Gregg and Kuhn, Stephen and Wong, Vincent and Yelamanchili, Amruta and Towey, Shannon and Agrawal, Jagriti and Chi, Wayne and Connell, Andrea and Davis, Evan and Lohr, Colette},
  booktitle = {International Symposium on Artificial Intelligence, Robotics and Automation in Space},
  year = {2020},
  doi = {10.48577/jpl.fpifzq},
  abstract = {The Mars 2020 Mission is developing a scheduler for use onboard the Mars 2020 Perseverance Rover. The purpose of this scheduler is to adjust the activities to account for variances in onboard resources (e.g. available energy) or execution (e.g. activity failures or activities taking shorter or longer than expected). The onboard scheduler is a priority-first non backtracking scheduler. The scheduling problem is challenged by: (1) limited onboard computing resources; (2) scheduler/execution interactions; (3) energy management including scheduling of wakeup and shutdown; (4) and thermal management (preheat and maintenance heating for mechanical actuation). We overview how these challenges are addressed in the scheduler design. We also discuss the ground software being developed to support operations with onboard autonomy. We also describe the current status and target deployment timeline.}
}

Connell, A. and Hurst, M. (2023). Ground Software to Support Autonomous Onboard Scheduling for Mars Perseverance Rover . IEEE Aerospace Conference. Source

BibTeX

@inproceedings{connell2023ground,
  title = {Ground Software to Support Autonomous Onboard Scheduling for Mars Perseverance Rover},
  author = {Connell, Andrea and Hurst, Matthew},
  booktitle = {IEEE Aerospace Conference},
  address = {Big Sky, Montana},
  year = {2023},
  doi = {10.48577/jpl.lrhct0},
  abstract = {The flight software for the Mars Perseverance Rover is capable of autonomous onboard scheduling. The goal of this capability is to reduce spacecraft idle time and unnecessary heating to leave more resources for science observations. Enabling this first-of-its-kind onboard scheduling requires tight coordination between the Mission Operations team, ground software, and flight software.This paper will focus on the ground software tools that have been created or enhanced by the Mars 2020 mission in support of autonomous onboard scheduling. The following challenges were encountered: automating the creation of planning constraints to reduce tedious and error-prone manual work, while providing enough flexibility for the team to handle unique scenarios; providing visibility to allow the Operations team to build trust in a variable execution system in a way that is digestible within a tight planning timeline; using flight software simulation to validate the files that will be sent to the spacecraft in a realistic manner, even though we cannot truly predict what will happen on the rover; after plan execution on the rover, analyzing the data returned from the spacecraft to understand what did happen, and being able to use that information to predict initial conditions for the next planning cycle.}
}

Yelamanchili, A., Agrawal, J., Chien, S., Biehl, J., Connell, A., Guduri, U., Hazelrig, J., Ip, I., Maxwell, K., Steadman, K. and Towey, S. (2021). Ground-based Automated Scheduling for Operations of the Mars 2020 Rover Mission . International Conference on Space Operations, SpaceOps-2021,6,x1385. Source

BibTeX

@inproceedings{yelamanchili2021ground,
  title = {Ground-based Automated Scheduling for Operations of the Mars 2020 Rover Mission},
  author = {Yelamanchili, Amruta and Agrawal, Jagriti and Chien, Steve and Biehl, James and Connell, Andrea and Guduri, Usha and Hazelrig, James and Ip, Iris and Maxwell, Kimberly and Steadman, Kimberly and Towey, Shannon},
  booktitle = {International Conference on Space Operations},
  number = {SpaceOps-2021,6,x1385},
  address = {Cape Town, South Africa},
  year = {2021},
  url = {https://dataverse.jpl.nasa.gov/dataset.xhtml?persistentId=hdl:2014/54568}
}

Troesch, M., Mirza, F., Hughes, K., Rothstein-Dowden, A., Bocchino, R., Donner, A., Feather, M., Smith, B., Fesq, L., Barker, B. and Campuzano, B. (2020). MEXEC: An Onboard Integrated Planning and Execution Approach for Spacecraft Commanding . Workshop on Integrated Execution / Goal Reasoning. Source

BibTeX

@inproceedings{troesch2020mexec,
  title = {MEXEC: An Onboard Integrated Planning and Execution Approach for Spacecraft Commanding},
  author = {Troesch, Martina and Mirza, Faiz and Hughes, Kyle and Rothstein-Dowden, Ansel and Bocchino, Robert and Donner, Amanda and Feather, Martin and Smith, Benjamin and Fesq, Lorraine and Barker, Brian and Campuzano, Brian},
  booktitle = {Workshop on Integrated Execution / Goal Reasoning},
  year = {2020},
  url = {https://ai.jpl.nasa.gov/public/papers/IntEx-2020-MEXEC.pdf}
}

Ingham, M., Rouquette, N., Ardito, S., Bandyopadhyay, S., Hasnain, Z., Hasseler, T., Huffman, B., Patterson, K., Rabideau, G. and Vaquero, T. S. (2026). Autonomous Onboard Planning, Execution and Resource Management for the Endurance Lunar Rover . AIAA SCITECH Forum. Source

BibTeX

@inproceedings{ingham2026autonomous,
  title = {Autonomous Onboard Planning, Execution and Resource Management for the Endurance Lunar Rover},
  author = {Ingham, Michel and Rouquette, Nicolas and Ardito, Steven and Bandyopadhyay, Saptarshi and Hasnain, Zaki and Hasseler, Tristan and Huffman, Bennett and Patterson, Katharine and Rabideau, Gregg and Vaquero, Tiago Stegun},
  booktitle = {AIAA SCITECH Forum},
  year = {2026},
  doi = {10.48577/jpl.gy1l0y},
  abstract = {The Endurance mission concept requires a lunar rover to drive roughly 2500 kilometers, maintaining a high rate of traverse on the order of 100 kilometers per lunar day. The long distance and high traverse rate of this proposed rover mission will require a system with unprecedented levels of onboard autonomy. Specifically, it will require onboard reasoning capabilities to provide management of critical state and resources, including power/energy and thermal state. To achieve the ambitious mission objectives, anomalies related to the system or the environment state will have to be overcome quickly, with limited ground-in-the-loop interruptions. In this paper, we review the architecture and implementation of system-level autonomy software that will enable the required responsive behavior, with particular focus on flexible execution functions provided by an onboard activity planning and execution capability called MEXEC (Multi-mission EXECutive). We introduce a new set of scenarios for the FY2025 field test campaign, in which our system-level autonomy software has been augmented with behavioral software providing power and thermal management functions. Finally, we present results from testing the new integrated autonomy capabilities, both in simulation and in field tests.}
}

Estlin, T., Fisher, F., Gaines, D., Chouinard, C., Schaffer, S. and Nesnas, I. (2002). Continuous Planning and Execution for an Autonomous Mars Rover . International NASA Workshop on Planning and Scheduling for Space. Source

BibTeX

@inproceedings{estlin2002continuous,
  title = {Continuous Planning and Execution for an Autonomous Mars Rover},
  author = {Estlin, Tara and Fisher, Forest and Gaines, Daniel and Chouinard, Caroline and Schaffer, Steve and Nesnas, Issa},
  booktitle = {International NASA Workshop on Planning and Scheduling for Space},
  year = {2002},
  url = {https://dataverse.jpl.nasa.gov/dataset.xhtml?persistentId=hdl:2014/10027}
}

Maldague, P. F., Wissler, S., Lenda, M. and Finnerty, D. (2014). APGEN Scheduling: 15 Years of Experience in Planning Automation . AIAA SpaceOps Conference. Source

BibTeX

@inproceedings{maldague2014apgen,
  title = {APGEN Scheduling: 15 Years of Experience in Planning Automation},
  author = {Maldague, Pierre F. and Wissler, Steve and Lenda, Matthew and Finnerty, Daniel},
  booktitle = {AIAA SpaceOps Conference},
  year = {2014},
  doi = {10.2514/6.2014-1809},
  abstract = {In this paper, we discuss the scheduling capability of APGEN (Activity Plan Generator), a multi-mission planning application that is part of the NASA AMMOS (Advanced Multi- Mission Operations System), and how APGEN scheduling evolved over its applications to specific Space Missions. Our analysis identifies two major reasons for the successful application of APGEN scheduling to real problems: an expressive DSL (Domain-Specific Language) for formulating scheduling algorithms, and a well-defined process for enlisting the help of auxiliary modeling tools in providing high-fidelity, system-level simulations of the combined spacecraft and ground support system.}
}

Dailis, M., Ferguson, E., Camargo, C. and Maillard, A. (2023). Aerie: A Modern Multi-Mission Planning, Scheduling, and Sequencing System . International Workshop on Planning and Scheduling for Space. Source

BibTeX

@inproceedings{dailis2023aerie,
  title = {Aerie: A Modern Multi-Mission Planning, Scheduling, and Sequencing System},
  author = {Dailis, Matthew and Ferguson, Eric and Camargo, Chris and Maillard, Adrien},
  booktitle = {International Workshop on Planning and Scheduling for Space},
  year = {2023},
  doi = {10.48577/jpl.bzznxs},
  abstract = {Aerie is a new open source multi-mission planning, scheduling, and sequencing system for space applications.We briefly describe the impetus behind the creation of Aerie, comment on its position within the landscape of other planning systems, and highlight some key capabilities that make it well suited for distributed, cross-team collaboration. We also a present a short case study describing the use of Aerie in the uplink planning process for Europa Clipper and illustrate some of the benefits of making Aerie an open source project.}
}

Backes, P. G., Norris, J. S., Powell, M. W., Vona, M. A., Steinke, R. and Wick, J. V. (2003). The science activity planner for the Mars Exploration Rover mission: FIDO field test results . IEEE Aerospace Conference. Source

BibTeX

@inproceedings{backes2003science,
  title = {The science activity planner for the Mars Exploration Rover mission: FIDO field test results},
  author = {Backes, P. G. and Norris, Jeffrey S. and Powell, Mark W. and Vona, Marsette A. and Steinke, R. and Wick, Justin V.},
  booktitle = {IEEE Aerospace Conference},
  volume = {8},
  pages = {8_3525-8_3539},
  publisher = {IEEE},
  year = {2003},
  doi = {10.1109/aero.2003.1235538}
}

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}
}

Mishkin, A. H. and Laubach, S. (2006). From prime to extended mission : evolution of the Mars Exploration Rover tactical uplink process. . AAS/AIAA Astrodynamics Specialist Conference. Source

BibTeX

@inproceedings{mishkin2006prime,
  title = {From prime to extended mission : evolution of the Mars Exploration Rover tactical uplink process.},
  author = {Mishkin, Andrew H. and Laubach, Sharon},
  booktitle = {AAS/AIAA Astrodynamics Specialist Conference},
  publisher = {JPL Open Repository},
  year = {2006},
  url = {https://dataverse.jpl.nasa.gov/dataset.xhtml?persistentId=hdl:2014/39820}
}

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.}
}

Pell, B., Bernard, D. E., Chien, S., Gat, E., Muscettola, N., Nayak, P. P., Wagner, M. D. and Williams, B. C. (1996). A Remote Agent Prototype for Spacecraft Autonomy . Space Sciencecraft Control and Tracking in the New Millennium. Source

BibTeX

@inproceedings{pell1996remote,
  title = {A Remote Agent Prototype for Spacecraft Autonomy},
  author = {Pell, Barney and Bernard, Douglas E. and Chien, Steve and Gat, Erann and Muscettola, Nicola and Nayak, P. P. and Wagner, Michael D. and Williams, Brian C.},
  booktitle = {Space Sciencecraft Control and Tracking in the New Millennium},
  series = {SPIE Proceedings},
  volume = {2810},
  pages = {74-90},
  year = {1996},
  doi = {10.1117/12.255150},
  abstract = {NASA has recently announced the New Millennium Program (NMP) to develop 'faster, better, cheaper' spacecraft in order to establish a 'virtual presence' in space. A crucial element in achieving this vision is onboard spacecraft autonomy, requiring us to automate functions which have traditionally been achieved on ground by humans. These include planning activities, sequencing spacecraft actions, tracking spacecraft state, ensuring correct functioning, recovering in cases of failure and reconfiguring hardware. In response to these challenging requirements, we analyzed the spacecraft domain to determine its unique properties and developed an architecture which provided the required functionality. This architecture integrates traditional real-time monitoring and control with constraint-based planning and scheduling, robust multi-threaded execution, and model-based diagnosis and reconfiguration. In a five month effort we successfully demonstrated this implemented architecture in the context of an autonomous insertion of a simulated spacecraft into orbit around Saturn, trading off science and engineering goals, and achieving the mission goals in the face of any single point of hardware failure. This scenario turned out to be among the most complex handled by each of the component technologies. As a result of this success, the integrated architecture has been selected to control the first NMP flight, Deep Space One, in 1998. It will be the first AI system to autonomously control an actual spacecraft.}
}

Tran, D., Chien, S., Rabideau, G. and Cichy, B. (2004). Flight Software Issues in Onboard Automated Planning: Lessons Learned on EO-1 . International Workshop on Planning and Scheduling for Space. Source

BibTeX

@inproceedings{tran2004flight,
  title = {Flight Software Issues in Onboard Automated Planning: Lessons Learned on EO-1},
  author = {Tran, Daniel and Chien, Steve and Rabideau, Gregg and Cichy, Benjamin},
  booktitle = {International Workshop on Planning and Scheduling for Space},
  year = {2004},
  url = {https://dataverse.jpl.nasa.gov/dataset.xhtml?persistentId=hdl:2014/37970}
}

Cheung, K.-M., Ko, A., Page, D., Bixler, J. and Lever, S. (2004). Design and architecture of planning and sequence system for Mars Exploration Rover (MER) Operations . Space OPS Conference. Source

BibTeX

@inproceedings{cheung2004design,
  title = {Design and architecture of planning and sequence system for Mars Exploration Rover (MER) Operations},
  author = {Cheung, K-M. and Ko, A. and Page, D. and Bixler, J. and Lever, S.},
  booktitle = {Space OPS Conference},
  year = {2004},
  doi = {10.2514/6.2004-356-434}
}

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.}
}

Khan, S., Ridenhour, F., Schaffer, M., Lever, S., Roth, B., Edgington, S., Hwang, P. and Cloutier, K. (2021). Reducing Risk of InSight Surface Operations Through High-Fidelity Command Sequence Modeling . IEEE Aerospace Conference. Source

BibTeX

@inproceedings{khan2021reducing,
  title = {Reducing Risk of InSight Surface Operations Through High-Fidelity Command Sequence Modeling},
  author = {Khan, Shaheer and Ridenhour, Forrest and Schaffer, Michael and Lever, Scott and Roth, Brian and Edgington, Scott and Hwang, Pauline and Cloutier, Kyle},
  booktitle = {IEEE Aerospace Conference},
  pages = {1-10},
  publisher = {IEEE},
  year = {2021},
  doi = {10.1109/aero50100.2021.9438410},
  abstract = {Simulating spacecraft behavior is crucial for the success of deep space missions. Many previous deep space missions have made use of ground-simulation of sequenced commanding, at speeds far greater than real time, to predict spacecraft state over time through the execution of onboard sequences. This type of modeling can be done at any fidelity, and many missions often need to decrease fidelity to reduce cost and complexity. NASA JPL's Interior Exploration using Seismic Investigations, Geodesy and Heat Transport (InSight) mission expanded the scope of ground modeling considerably, which has led to numerous benefits over past implementations. InSight is the first JPL mission to expand the scope of ground modeling to include the uplink of files from Earth to the spacecraft, rather than making the simplification that any command sequences already exist onboard the spacecraft. The advantages of modeling the uplink of files are numerous. First, it allows for accurate modeling of the onboard filesystem of the spacecraft at all points in time, meaning that all file loads and deletions throughout the mission are modeled at the exact moment they are predicted to actually happen. Second, operators can be more certain that dependencies between sequences are not broken due to the dynamic nature of the filesystem as files are deleted, copied, and uplinked. Lastly, spacecraft filesystem tracking allows for management of sequences prior to uplink, limiting the uplink to only new sequences. During daily tactical operations of a spacecraft on Mars, a model is only useful if the results can be interpreted quickly. In this fast-paced environment, it is essential that command products are modeled and reviewed, errors are found and diagnosed, and new command products are redelivered, remodeled, re-reviewed in a timely manner. It is impossible to review the entire model and therefore the results of the model must be condensed and presented in a fashion that is intuitive, easy-to-navigate, complete, and trustworthy. InSight developed a number of innovative sequence review products that are designed to provide operators with the information required to quickly assess the validity of command products and diagnose potential issues. Together, these products provide a complete, yet succinct picture of the command and sequence model to the operators and facilitate a quick assessment of all sequence command products. This paper will cover sequencing innovations made for InSight surface operations as well as lessons learned for future missions.}
}

Sherwood, R. L., Chien, S., Tran, D., Cichy, B., Castano, R., Davies, A. and Rabideau, G. (2005). Intelligent systems in space: the EO-1 Autonomous Sciencecraft . Infotech@Aerospace. doi.org/10.2514/6.2005-6917

BibTeX

@misc{sherwood2005intelligent,
  title = {Intelligent systems in space: the EO-1 Autonomous Sciencecraft},
  author = {Sherwood, Robert L. and Chien, Steve and Tran, Daniel and Cichy, Benjamin and Castano, Rebecca and Davies, Ashley and Rabideau, Gregg},
  booktitle = {Infotech@Aerospace},
  year = {2005},
  doi = {10.2514/6.2005-6917},
  abstract = {The Autonomous Sciencecraft Software (ASE) is currently flying onboard the Earth Observing One (EO-1) Spacecraft. This software enables the spacecraft to autonomously detect and respond to science events occurring on the Earth. The package includes software systems that perform science data analysis, deliberative planning, and runtime robust execution. Because of the deployment to the EO-1 spacecraft, the ASE software has stringent constraints of autonomy and limited computing resources. We describe these constraints and how they are reflected in our operations approach. A summary of the final results of the experiment is also included. This software has demonstrated the potential for space missions to use onboard decision-making to detect, analyze, and respond to science events, and to downlink only the highest value science data. As a result, ground-based mission planning and analysis functions have been greatly simplified, thus reducing operations cost.}
}

Chouinard, C., Estlin, T., Gaines, D. and Fisher, F. (2005). Intelligent rover decision-making in response to exogenous events. Source

BibTeX

@other{chouinard2005,
  title = {Intelligent rover decision-making in response to exogenous events},
  author = {Chouinard, C. and Estlin, T. and Gaines, D. and Fisher, F.},
  year = {2005},
  url = {https://dataverse.jpl.nasa.gov/dataset.xhtml?persistentId=hdl:2014/37757}
}

Chien, S., Sherwood, R., Tran, D., Cichy, B., Rabideau, G., Castano, R., Davies, A., Mandl, D., Frye, S., Trout, B., Shulman, S. and Boyer, D. (2005). Using Autonomy Flight Software to Improve Science Return on Earth Observing One . Journal of Aerospace Computing, Information, and Communication, 4. Source

BibTeX

@article{chien2005using,
  title = {Using Autonomy Flight Software to Improve Science Return on {Earth Observing One}},
  author = {Chien, Steve and Sherwood, Rob and Tran, Daniel and Cichy, Benjamin and Rabideau, Gregg and Castano, Rebecca and Davies, Ashley and Mandl, Dan and Frye, Stuart and Trout, Bruce and Shulman, Seth and Boyer, Darrell},
  journal = {Journal of Aerospace Computing, Information, and Communication},
  volume = {2},
  number = {4},
  pages = {196--216},
  year = {2005},
  doi = {10.2514/1.12923},
  abstract = {NASA’s Earth Observing One Spacecraft (EO-1) has been adapted to host an advanced suite of onboard autonomy software designed to dramatically improve the quality and timeliness of science-data returned from remote-sensing missions. The Autonomous Sciencecraft Experiment (ASE) enables the spacecraft to autonomously detect and respond to dynamic scientifically interesting events observed from EO-1’s low earth orbit. ASE includes software systems that perform science data analysis, mission planning, and runtime robust execution. In this article we describe the autonomy flight software, as well as innovative solutions to the challenges presented by autonomy, reliability, and limited computing resources. I.}
}

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}
}

Ingham, M. D. (2018). Resilient Risk-Aware Autonomy. ntrs.nasa.gov/citations/20210006363

BibTeX

@misc{ingham2018resilient,
  title = {Resilient Risk-Aware Autonomy},
  author = {Ingham, Michel D.},
  year = {2018},
  url = {https://ntrs.nasa.gov/citations/20210006363},
  abstract = {UNKNOWN}
}

Horvath, J. C. (1995). Spacecraft Autonomy Issues: Present and Future. ntrs.nasa.gov/citations/20060041958

BibTeX

@misc{horvath1995spacecraft,
  title = {Spacecraft Autonomy Issues: Present and Future},
  author = {Horvath, Joan C.},
  year = {1995},
  url = {https://ntrs.nasa.gov/citations/20060041958}
}

Backes, P., Tso, K., Norris, J., Tharp, G. and Kim, W. (2000). An Operations System for a Planetary Rover Prototype . IEEE International Conference on Robotics and Automation. Source

BibTeX

@inproceedings{backes2000operations,
  title = {An Operations System for a Planetary Rover Prototype},
  author = {Backes, P. and Tso, K. and Norris, J. and Tharp, G. and Kim, W.},
  booktitle = {IEEE International Conference on Robotics and Automation},
  year = {2000},
  url = {https://ntrs.nasa.gov/citations/20060032194},
  abstract = {A distributed operations system, the Web Interface for Telescience (WITS), is being developed for use in future planetary lander and rover mission operations.}
}

Parjan, S. and Gaines, D. (2024). In OBP We Trust: Verification and Validation of the M2020 On Board Planner Flight Software . IEEE Aerospace Conference. Source

BibTeX

@inproceedings{parjan2024obp,
  title = {In OBP We Trust: Verification and Validation of the M2020 On Board Planner Flight Software},
  author = {Parjan, Shreya and Gaines, Dan},
  booktitle = {IEEE Aerospace Conference},
  address = {Big Sky, Montana},
  year = {2024},
  doi = {10.48577/jpl.67lmez},
  abstract = {With a planned deployment to the Perseverance roverin October 2023, the Mars 2020 On Board Planner (OBP) flightsoftware (FSW) will allow the rover to conserve energy and au-tonomously perform more activities when surplus resources (i.e.energy, data volume, time) are available. This functionality iscritical to support efficient resource management and maximizethe productivity of the vehicle as the rover’s battery degradesover time, particularly as the Mars Sample Return mission(MSR) may rely on Perseverance to facilitate sample deliveryto the return lander. Up until OBP’s deployment, operatorshave used the Master/Sub-Master paradigm (M/SM) for allMars missions to-date, in which rigid plans are uplinked to therover based on highly conservative models of activity resourceconsumption and duration that can unnecessarily constrain on-board plans. Thus, OBP must be robust enough to merit thetrust of mission science and operations personnel and preservethe health and safety of the spacecraft. To this end, we havecompleted a thorough verification and validation (V&V) cam-paign of the first iteration of OBP that will be deployed to therover as part of new Simple Planner (SP) operations paradigm.The campaign heavily relied on software testing to ensure thatcore flight software capabilities were met and that the softwarecomprehensively covered potential edge cases. In this paper,we motivate the need for a thorough V&V campaign going intothe deployment of On Board Planner to the Perseverance rover,contextualize how OBP integrates with the rest of the vehicle’sflight software, and leverage three case studies to evidence howtesting provided critical support towards the successful infusionof onboard scheduling software on the rover.}
}

Kuhn, S. R. (2022). Should've Could've: Progress in the Systems Engineering of the Mars2020 On-Board Planner . IEEE Aerospace Conference. Source

BibTeX

@inproceedings{kuhn2022should,
  title = {Should've Could've: Progress in the Systems Engineering of the Mars2020 On-Board Planner},
  author = {Kuhn, Stephen R},
  booktitle = {IEEE Aerospace Conference},
  pages = {1-15},
  publisher = {IEEE},
  year = {2022},
  doi = {10.1109/aero53065.2022.9843824},
  abstract = {Mars rover operations has traditionally controlled behavior using meticulously compiled, so-called “Master” sequences that enforce deterministic timing and ordering of activities. While the approach is effective at managing risk and complexity, it can also be inefficient. Extended operations of the Mars2020 rover envisions a fundamentally different scheduling and execution approach. Atomic activities are constrained by mission operators in time, resource usage, and a structure of dependencies. The rover flight software will create and re-create schedules, responding to available energy, data volume, actual activity durations and execution status, observed temperatures, and other on-board state. Schedules are expected to change substantially in the course of execution. The solution space is prohibitively large and probabilistic in nature. A range of emergent behavior is accessible depending on the interaction between actual on-board state and a given formulation of constraints. For years, the specification and implementation of this capability have been considered and refined, as testing and analysis have mapped the contours of the system. Many concepts that could have been adopted were rejected, and in some cases, emergent properties have been exposed and beaten back with approaches to steer the behavior. This paper discusses some of the design history and underlying reasoning in the systems engineering of the first autonomous activity planner on Mars.}
}

Rabideau, G., Russino, J., Branch, A., Dhamani, N., Vaquero, T. S., Chien, S., de la Croix, J.-P. and Rossi, F. (2025). Planning, scheduling, and execution on the Moon: the CADRE technology demonstration mission . arXiv preprint. Source

BibTeX

@article{rabideau2025planning,
  title = {Planning, scheduling, and execution on the Moon: the CADRE technology demonstration mission},
  author = {Rabideau, Gregg and Russino, Joseph and {Branch, Andrew} and Dhamani, Nihal and Vaquero, Tiago Stegun and Chien, Steve and de la Croix, Jean-Pierre and Rossi, Federico},
  journal = {arXiv preprint},
  pages = {1727-1735},
  year = {2025},
  doi = {10.65109/ilfn7216},
  abstract = {NASA's Cooperative Autonomous Distributed Robotic Exploration (CADRE) mission, slated for flight to the Moon's Reiner Gamma region in 2025/2026, is designed to demonstrate multi-agent autonomous exploration of the Lunar surface and sub-surface. A team of three robots and a base station will autonomously explore a region near the lander, collecting the data required for 3D reconstruction of the surface with no human input; and then autonomously perform distributed sensing with multi-static ground penetrating radars (GPR), driving in formation while performing coordinated radar soundings to create a map of the subsurface. At the core of CADRE's software architecture is a novel autonomous, distributed planning, scheduling, and execution (PS&amp;E) system. The system coordinates the robots' activities, planning and executing tasks that require multiple robots' participation while ensuring that each individual robot's thermal and power resources stay within prescribed bounds, and respecting ground-prescribed sleep-wake cycles. The system uses a centralized-planning, distributed-execution paradigm, and a leader election mechanism ensures robustness to failures of individual agents. In this paper, we describe the architecture of CADRE's PS&amp;E system; discuss its design rationale; and report on verification and validation (V&amp;V) testing of the system on CADRE's hardware in preparation for deployment on the Moon.}
}

Rankin, A., Del Sesto, T., Hwang, P., Justice, H., Maimone, M., Verma, V. and Graser, E. (2023). Perseverance Rapid Traverse Campaign . IEEE Aerospace Conference. Source

BibTeX

@inproceedings{rankin2023perseverance,
  title = {Perseverance Rapid Traverse Campaign},
  author = {Rankin, Arturo and Del Sesto, Tyler and Hwang, Pauline and Justice, Heather and Maimone, Mark and Verma, Vandi and Graser, Evan},
  booktitle = {IEEE Aerospace Conference},
  pages = {1-16},
  address = {Big Sky, Montana},
  year = {2023},
  doi = {10.1109/aero55745.2023.10115835},
  abstract = {Over the first 13 months of the Mars 2020 mission, the Perseverance rover traversed nearly 5 km along the Jezero Crater floor. Near the end of that period, the Science team was anxious to relocate to the ancient Delta region near the crater rim, over 5 km away. A Rapid Traverse Campaign was planned that would prioritize use of Perseverance's autonomous navigation software to drive at an unprecedented high pace and minimize science activities. The Rapid Traverse Campaign started in March 2022 and lasted 31 Martian days. During the campaign, Perseverance drove over 5 km in 24 drives, during which its autonomy software planned 94.8% of its overall driving, enabling it to set several new planetary rover driving records. Perseverance exceeded the longest daily drive distance record achieved by a previous planetary rover (219 meters) 11 times and set new records for the longest multi-sol drive distance in a single plan (528.7 meters) and the longest continuation drive (699.9 meters) by operating without human drive path input during 3 sols of driving. This paper details the planning and execution of the Rapid Traverse Campaign.}
}

Verma, V., Gaines, D., Rabideau, G., Schaffer, S. and Joshi, R. (2017). Autonomous Science Restart for the Planned Europa Mission with Lightweight Planning and Execution . International Conference on Automated Planning and Scheduling. Source

BibTeX

@inproceedings{verma2017autonomous,
  title = {Autonomous Science Restart for the Planned Europa Mission with Lightweight Planning and Execution},
  author = {Verma, Vandi and Gaines, Dan and Rabideau, Gregg and Schaffer, Steve and Joshi, Rajeev},
  booktitle = {International Conference on Automated Planning and Scheduling},
  publisher = {JPL Open Repository},
  year = {2017},
  url = {https://dataverse.jpl.nasa.gov/dataset.xhtml?persistentId=hdl:2014/48030}
}