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Attitude and Trajectory Control

Attitude and trajectory control has to point a vehicle, and move it along a path, with no ground operator in the loop and with actuators, sensors and models that are only partly known before flight. The deadline is shortest in entry, descent and landing: about seven minutes of flight, a vehicle that reconfigures itself about a dozen times, and a landing that cannot be commanded from Earth [1]. The same problem recurs at other timescales: a 1.8 kg rotorcraft whose bare airframe is unstable [6][7], a rover that adjusts wheel speeds eight times a second [12], and an orbiter that must hold pointing on fewer reaction wheels than it was built with [18].

Seven families cover the corpus. What has flown is stated for each.

FamilyWhat it controlsStatus in the corpus
Entry attitude and bank controlLift vector direction of an unguided-shape capsuleFlown: MSL, Mars 2020 [2][3]
Powered descent guidanceThrust profile to a target pointFlown: Mars 2020 polynomial guidance [11], SLIM explicit guidance [10]; convex optimization not flown on Mars [4][9]
Hazard-relative and map-relative retargetingDivert to a safe siteFlown on Mars 2020 [11]; lidar hazard detection analyzed only [5][23]
Rotorcraft flight controlAttitude of an aerodynamically unstable vehicleFlown: Ingenuity [6][7][14]
Surface vehicle controlWheel speed, steering, path trackingFlown: Curiosity traction control [12]; planner and terramechanics work in simulation and laboratory [22][31][32]
Orbiter attitude control and fault reconfigurationPointing with reduced actuatorsPaper designs [18]
Manipulator and proximity controlArm and chaser motion on a moving baseAnalysis and laboratory testbeds [28][29][30]

The Mars 2020 EDL flight software is three components: a timeline engine that prepares and coordinates events, a navigation mode commander that manages the filter configuration and either propagates the descent IMU or fuses it with the Ka-band terrain descent sensor radar, and an EDL GNC mode commander that reconfigures the vehicle and guides it down [1]. All three run in the 64 Hz rate group, called in that order so the state knowledge is current before guidance uses it. The navigation filter integrates a 200 Hz descent IMU and corrects with a six-beam radar cycled at 20 Hz; it was changed from MSL to model the local gravity field, which departed from the onboard model by almost 450 microg near Gale, and to edit sandy radar returns that corrupted the MSL velocity estimate during sky crane [19].

The timeline is built from timechains, sequences of events anchored either to an absolute time or relative to another anchor, with actions such as pyro firings at each timepoint [1]. Triggers rather than fixed times drive guidance start, heading alignment start, straighten up and fly right, chute deployment, heatshield ejection, engine priming, backshell separation and touchdown. MSL established the pattern, but its guidance heading alignment and parachute deployment triggers were all atmospheric-relative-velocity-magnitude triggers on inertially propagated velocity, not a range trigger; heatshield separation used a variant of the same, and backshell separation was an altitude against vertical velocity trigger fed by the landing radar [3].

Effectors [1]:

EffectorCountOutput
Descent reaction control thrusters4 clusters of 2about 150 N in blowdown during the exo-atmospheric phase, about 275 N regulated during entry
Mars Landing Engines8throttleable to about 3200 N
Terrain descent sensor1Ka-band radar, 6 pencil beams
Entry balance masses6ejected during straighten up and fly right

Two capabilities were added over Curiosity: a range trigger for parachute deployment, worth a 40 percent smaller landing ellipse, and terrain relative navigation with safe target selection, so the vehicle can land around hazards inside that ellipse [1]. Both were driven by science site access rather than by a control deficiency. Two Curiosity anomalies were also mitigated: parachute deployment exciting structural resonances into unexpectedly large IMU rates, and an out-of-spec touchdown velocity. The vision side of terrain relative navigation is covered under terrain relative navigation. The paper reports the mode transition record, not the accuracy of what was commanded [1]; the broader as-flown reconstruction is an 830 page, 25 chapter JPL compendium whose entry guidance chapter is the reference for performance against prediction [24].

Entry guidance: modulating a lift vector nobody can steer directly

Section titled “Entry guidance: modulating a lift vector nobody can steer directly”

The entry capsule has no control surfaces. Its center of mass is displaced from the axis of symmetry so that it trims at a non-zero angle of attack and generates lift, and the guidance law steers only the direction of that lift vector, by rolling the capsule about its velocity vector [3]. MSL was the first Mars mission to fly guided entry. Before it, the five US Mars landings flew Viking-derived ballistic entry systems sized for the 0.6 to 1 t class, and a 40 to 80 t human-class lander is a different control problem [25].

The guidance law is derived from the Apollo command module final phase algorithm, adapted to Mars: it issues bank angle commands to control range-to-go and cross-range error by adjusting drag acceleration, and when cross-range error exceeds a threshold it commands a bank reversal [2]. Perseverance entered with a lift-to-drag ratio of 0.24 at Mach 24, and the range control phase lasts about 3 minutes, ending at parachute deploy near Mach 1.8 [2].

The controller underneath is three single-axis channels, bank, angle of attack and sideslip, each with a feedforward path for large commanded turns and a phase plane feedback path operating on attitude and rate deadbands. A bank reversal is a large turn, so the bank profiler plans it as a single-axis accelerate, coast, decelerate maneuver and the feedforward path executes it. The bank channel’s phase plane is tuned for tracking; the angle of attack and sideslip channels are tuned for rate damping and minimum fuel. Gains and deadbands are scheduled by flight mode and capsule speed. Thruster logic converts the desired torque into pulse width modulation of the 8 thrusters [2], which deliver about 275 N each in regulated mode [1].

Measured deadbands and rates from the flight reconstruction:

QuantityValue
Capsule spin rate at cruise stage separationabout 12 deg/s
Exo-atmospheric attitude deadbandabout 2 deg
Angle of attack and sideslip deadband during entry10 deg
Rate damping engagement threshold6 deg/s
Observed oscillatory mode rates during entryup to about 2 deg/s
Straighten up and fly right bank slew180 deg

Source: [2].

Perseverance landed on 18 February 2021 inside a required 8 by 7 km ellipse [2]. The flight reconstruction reports the spindown feedforward profile decelerating faster than intended, which engaged the feedback path and reduced the control action, and the same on the acceleration part of the turn to entry. During entry, an unexpected initial bank command cost more fuel than predicted, but overall fuel use stayed in family with the EDL Monte Carlo expectations, and bank control errors sat well inside the watermarks from the verification and validation campaign [2]. Trim angles drifted a few degrees over the entry as expected, and each balance mass ejection was followed by the controller adjusting its predicted trim angle of attack. This is one flight, reported by the team that built the controller [2].

Entry also fixes the atmosphere the vehicle flew through. Wind during the Mars 2020 parachute and powered descent has been inferred by fitting a trajectory model to the reconstructed path between 12 km and 2.2 km altitude, which is a fit and not a direct measurement [26]. Navigation of the same vehicle from Earth to the entry interface, including the entry parameter updates that set the starting state for these controllers, is described in the navigation team’s own briefing [34].

Powered descent guidance is a minimum-fuel trajectory optimization with a non-convex control constraint: the engines cannot be throttled below a lower bound, so the feasible thrust magnitude set is an annulus rather than a ball [4]. Lossless convexification relaxes that into a second order cone program whose optimum provably coincides with the original problem’s, which matters operationally because interior point methods for second order cone programs solve in polynomial time with a deterministic convergence criterion, and a general nonlinear program run onboard does not. An outer search over time of flight wraps the inner cone program, and the 2008 enhancements need 4 solves for it [4]. A survey of roughly 400 works places this inside a wider convex-optimization literature that spans landing, rendezvous, reorientation and manipulation, with solve times quoted secondhand from the original papers [9].

The published cost is 2 to 3 seconds for a typical fuel-optimal solution using the MATLAB implementation on a 2.16 GHz Intel processor [4], a ground workstation rather than a flight part. Every result in that paper is simulation, and the onboard table lookup approximation it proposes is stated to be under development. Set against a 64 Hz EDL rate group on a RAD750 [1], the gap is the reason Mars 2020 flies a polynomial guidance law rather than an onboard optimizer. Learning-based work targets the same gap: a transformer model that predicts the active constraints and an initial guess for six degree of freedom successive convexification cut mean solve time by 66 percent over 545 Mars test cases, from a baseline mean of 14.61 s, and ran inference in 4.59 ms against 894 ms for linear interpolation of a lookup table; the benchmark hardware is not named [20].

What has flown is a different set of algorithms. The Mars 2020 powered descent flew an MSL heritage controller through a 586.57 m terrain relative divert with a maximum off-vertical angle of 38.9 degrees [11]. It held the vertical phase ground track inside a 30 by 30 cm box, needed a 5.5 m altitude correction in vertical descent, and exposed a modeling error visible only in flight, an inferred bias of about 95 N per engine. Terrain relative navigation accuracy is not established by that report. SLIM, the JAXA lunar lander, flew an explicit powered descent guidance algorithm and landed within 100 m of its target site in January 2024; its boost-start-time correction was fitted on simulated data [10]. For Mars, the propellant arithmetic is that each additional kilometer of divert capability costs about 3 percent of propellant mass fraction, roughly 63 kg for an MSL class vehicle, and correcting parachute wind drift alone costs about 65 kg per kilometer [23].

Before guidance of this kind existed, terminal guidance was closed-form. A 1961 NASA report derives rendezvous guidance relations from simplified relative equations of motion and exercises the loop on an IBM 704 for a 400 nautical mile circular orbit station; it was simulation only [21].

Hazard-relative and map-relative retargeting

Section titled “Hazard-relative and map-relative retargeting”

Detecting a hazard and diverting to a safe site is a sensing and geometry problem before it is a control problem, and the driving parameter is not the sensor. Analysis for the Autonomous Landing and Hazard Avoidance Technology project set the hazards to detect at rocks taller than 0.3 m and slopes steeper than 5 degrees, for a lander about 15 m in diameter [5]. Because rock size distributions are exponential, the vehicle’s own hazard tolerance dominates every other parameter: raising it sharply reduces the number of hazards, the area that must be imaged, and the number of lidar samples across that area. That analysis used synthetic terrain and Monte Carlo only, with no hardware [5]. The Mars equivalent was solved by moving the problem into descent imagery and a prior map rather than a lidar [1].

Worked parameter sets show the trade [5]:

0.3 m hazard0.5 m hazard
Detection slant rangeabout 500 mabout 1000 m
Ground sample distancefiner0.3 m
Touchdown error requiredtighter, feature tracking or hazard relative navigation likely neededplus or minus 2.5 m, which may remove that need
Opportunity for repeat passesonemultiple

Source: [5], Section 6. Both assume a 45 degree trajectory angle, a 90 by 90 m touchdown area so that only a small divert is required, and a 3 s hazard detection data collection window. The system still lands in 20 percent rock abundance, covering most feasible lunar sites [5]. Hazard map areas from 45 by 45 m to 360 by 360 m were analyzed, with no divert fuel cost published for any of them [5].

The passive alternative matches descent images to an orbital map. A field comparison of two such algorithms on imagery from ten flights found the correlation-based method produced solutions for over 97 percent of mid-day images with mean error under 15 m, with performance degrading outside mid-day lighting; processing was on a workstation after the fact [23]. Crater-landmark matching was studied earlier in a simulator built to size the camera field of view and the landmark count, assuming perfect image-to-database matching [33]. In-flight divert targeting has also reached rotorcraft: Ingenuity’s 2022 software release added a landing hazard detection that picks the frame region with the fewest tracked features, generated at 3 Hz, together with a digital elevation map in the navigation filter [16].

A Mars rotorcraft is not a small Earth helicopter. Air density in the 0.014 to 0.02 kg per cubic meter range gives a blade Lock number of 0.3 to 0.6 against roughly 4 on Earth, which inverts the sign of the rotor’s contribution to angular damping: the vehicle’s roll and pitch response is self-amplifying rather than self-damping [6]. Control authority comes from upper and lower swashplates giving 22 degrees of total collective range and cyclic control, on a vehicle under 1.8 kg with the rotor speed fixed by density in the 2600 to 2800 RPM band [6]. Collective actuator limits are -4.5 to 17.5 degrees and cyclic plus or minus 10 degrees, on a 1.2 m two-bladed coaxial rotor [7].

The structural design is set by the control bandwidth. Blades and hub were made unusually stiff so the rotating flap frequency sits at about 80 to 90 Hz, which puts every resonant mode well above the control system’s bandwidth; the low-order rotor model used for control design is valid to about 10 Hz [6]. Model errors found in ground testing were all in the direction of less damping than predicted. Apparent inertia measured about 60 percent above the rigid-rotor case against a predicted 50 percent, attributed to distributed higher-order rotor flexing that the single-hinge model does not capture; phase lag in the moments was 11 degrees against 8 degrees predicted [6]. The first controlled flight in Mars atmospheric conditions ran in JPL’s 25 foot space simulator in May 2016, in a CO2 atmosphere at 0.0175 kg per cubic meter. The authors state that low density and Mars gravity cannot be combined in a chamber [6]. A 2019 proposal would fly a vacuum chamber on Mars-gravity parabolas to combine them; it contains one simulation and nothing built or flown [8].

Flight results followed. The Ingenuity team’s summary through flight 13 covers the transition from technology demonstration to operations demonstration, flight control performance and the software updates [14]. Over flights 14 to 33 the rotor speed had to be raised from 2537 to 2800 rpm to compensate for summer density loss, a speed above anything tested on Earth, which moved the limiting factor from power to motor temperature [17]. Navigation attitude telemetry was later inverted to retrieve wind: tilt in steady hover gave a wind profile from 3 to 24 m, with speeds near 25 m/s on flight 59 where mesoscale models allowed about 15 m/s, a discrepancy the authors attribute to upwind terrain roughness; the retrieval depends on a steady-state aerodynamic model and covers seven flights [27].

Flights 68 and 69, in December 2023, were the first dedicated system identification flights of an aerial vehicle on another planet: frequency sweeps of 0.1 to 10 Hz, 22 s long, at 0.6 to 0.7 degree amplitude were injected into the swashplate cyclic during out-and-back legs at 10 m/s commanded groundspeed [7]. Above about 1 to 1.5 Hz the identified roll response matched the pre-flight physics model within the Cramer-Rao bounds, which validates the ground test campaign for that band, and the roll control derivative was 8.4 against 8.0 modeled on the Flight 69 out leg [7].

Below crossover, wind fed back through the stabilizing controller biased the Direct Method result, giving an angle-of-attack derivative of 0.96 against 0.15 modeled, and the Joint-Input-Output method recovered roughly half of the bias [7]. Wind inferred from cyclic effort reached 15 m/s horizontal airspeed with no instability or sustained control saturation, the same inverse approach that attitude-based wind retrievals use [7][27]. Coherence alone did not flag the bias, and roll damping and the speed derivatives could not be identified, so the flights do not independently identify the full lateral model.

The concept follow-on, a Mars Sample Recovery Helicopter, carries the Ingenuity architecture to a larger vehicle that takes off from a lander deck, with a 35 cm landing-knowledge requirement from map-relative localization and curved-path guidance [15]. Every value for it is a requirement or design target, and the 35 cm figure is one of them.

Wheel-level control on Mars is flown on Curiosity. Traction Control uses only proprioceptive sensing, the rocker, bogie, differential and attitude angles, to estimate each wheel’s terrain contact angle and command no-slip wheel rates at 8 Hz [12]. Ground testing measured a 19 percent average resultant load reduction on the leading front wheels. The flight comparison covered 483 sols across different terrain, so the benefit in flight is confounded by terrain [12]. The earlier conference version compared 44 sols of adjacent driving and reported 11.2 percent longer traverse time with control enabled; those flight numbers were later revised [13]. Steering on loose soil has a terramechanics model with a side-force term, validated on FJS-1 lunar simulant at 1 g in a laboratory only [32].

Trajectory-level control for rovers has not flown beyond waypoint driving. CRESCENT, the planner for the three CADRE lunar rovers, runs a 5.12 m global horizon at 1 Hz and a 0.64 m local horizon at 5 Hz over signed distance fields; its numbers are from simulation and an Earth analog yard, written before the rovers flew [22]. An energy-constrained planner tracked by model predictive control for an RTG and solar rover is simulation only [31].

Orbiter attitude control, manipulators and proximity operations

Section titled “Orbiter attitude control, manipulators and proximity operations”

Cassini flew on three of four reaction wheels after the articulable fourth wheel was moved onto a degraded one. A 2012 study showed that two surviving wheels plus four thrusters could meet science pointing in the two operational modes, using two different hybrid architectures; neither was flown, and either would have needed flight software and fault protection changes [18].

For manipulators, the equations of motion of a free-floating spacecraft with an arm are derived in closed form with a five-mode classification of maneuvering, analytically with no test article [30]. For flexible long-reach arms, an end-point controller estimates base motion from strain gauges instead of sensing the end effector, validated on a single planar laboratory testbed [28]. For docking with a tumbling target, a sampling-based model predictive controller is demonstrated in simulation with idealized rigid bodies [29]. None of these has flown.

SystemControl rateDeadlineProcessor
Mars 2020 EDL GNC64 Hz rate groupabout 7 minutes of flight, no ground loopRAD750, shared with the rest of the flight software [1]
Convex powered descent guidanceone solve per guidance updatenot flown on Mars2 to 3 s on a 2.16 GHz Intel workstation [4]
Lunar hazard detection and avoidanceone pass3 s data collection window [5]not published
Curiosity traction control8 Hzcontinuous during drivesrover flight computer [12]
CRESCENT rover planner1 Hz global, 5 Hz localper planning cyclesimulation and analog yard [22]

No onboard solver for convexified powered descent has a published flight timing on a flight-class processor; the 2 to 3 s figure is a workstation prototype and the learning-based speedups name no hardware [4][20]. Lidar hazard requirements for landing rest on synthetic terrain and Monte Carlo, with no measured divert fuel cost [5]. The entry controller results are one flight reported by its developers [2], and the Curiosity traction control benefit cannot be separated from terrain differences [12]. Ingenuity’s low-frequency stability derivatives are poorly determined, and its airspeed is inferred, so the lateral model is only partly validated in flight [7]. Orbiter reconfiguration, manipulator, proximity and rover planning methods listed above are demonstrated in analysis, simulation or laboratory only [18][22][28][29][30][31].

References

  1. Brugarolas, P. (2021). Mars 2020 Perseverance Entry Descent and Landing Guidance Navigation and Control . AAS/AIAA Astrodynamics Specialist Conference, AAS 21-600. Source
    BibTeX
    @inproceedings{brugarolas2021mars,
      title = {Mars 2020 Perseverance Entry Descent and Landing Guidance Navigation and Control},
      author = {Brugarolas, Paul},
      booktitle = {AAS/AIAA Astrodynamics Specialist Conference},
      number = {AAS 21-600},
      year = {2021},
      url = {https://dataverse.jpl.nasa.gov/dataset.xhtml?persistentId=hdl:2014/55247}
    }
  2. Brugarolas, P. and Way, D. W. (2021). Mars 2020 Perseverance Entry Controller Design and Flight Reconstruction . AAS/AIAA Astrodynamics Specialist Conference, AAS 21-601. Source
    BibTeX
    @inproceedings{brugarolas2021entry,
      title = {Mars 2020 Perseverance Entry Controller Design and Flight Reconstruction},
      author = {Brugarolas, Paul and Way, David W.},
      booktitle = {AAS/AIAA Astrodynamics Specialist Conference},
      number = {AAS 21-601},
      year = {2021},
      url = {https://dataverse.jpl.nasa.gov/dataset.xhtml?persistentId=hdl:2014/55195}
    }
  3. San Martin, A. M., Lee, S. W. and Wong, E. C. (2013). The Development of the MSL Guidance, Navigation, and Control System for Entry, Descent, and Landing . Annual AAS Guidance and Control Conference. Source
    BibTeX
    @inproceedings{sanmartin2013development,
      title = {The Development of the MSL Guidance, Navigation, and Control System for Entry, Descent, and Landing},
      author = {San Martin, A. Miguel and Lee, Steven W. and Wong, Edward C.},
      booktitle = {Annual AAS Guidance and Control Conference},
      year = {2013},
      url = {https://dataverse.jpl.nasa.gov/dataset.xhtml?persistentId=hdl:2014/44894}
    }
  4. Açıkmeşe, B., Blackmore, L., Scharf, D. P. and Wolf, A. (2008). Enhancements on the Convex Programming Based Powered Descent Guidance Algorithm for Mars Landing . AIAA/AAS Astrodynamics Specialist Conference. Source
    BibTeX
    @inproceedings{acikmese2008enhancements,
      title = {Enhancements on the Convex Programming Based Powered Descent Guidance Algorithm for Mars Landing},
      author = {Açıkmeşe, Behçet and Blackmore, Lars and Scharf, Daniel P. and Wolf, Aron},
      booktitle = {AIAA/AAS Astrodynamics Specialist Conference},
      year = {2008},
      doi = {10.2514/6.2008-6426},
      abstract = {In this paper, we present enhancements on the powered descent guidance algorithm developed for Mars pinpoint landing. The guidance algorithm solves the powered descent minimum fuel trajectory optimization problem via a direct numerical method. Our main contribution is to formulate the trajectory optimization problem, which has nonconvex control constraints, as a finite dimensional convex optimization problem, specifically as a finite dimensional second order cone programming (SOCP) problem. SOCP is a subclass of convex programming, and there are efficient SOCP solvers with deterministic convergence properties. Hence, the resulting guidance algorithm can potentially be implemented onboard a spacecraft for real-time applications. Particularly, this paper discusses the algorithmic improvements obtained by: (i) Using an efficient approach to choose the optimal time-of-flight; (ii) Using a computationally inexpensive way to detect the feasibility/ infeasibility of the problem due to the thrust-to-weight constraint; (iii) Incorporating the rotation rate of the planet into the problem formulation; (iv) Developing additional constraints on the position and velocity to guarantee no-subsurface flight between the time samples of the temporal discretization; (v) Developing a fuel-limited targeting algorithm; (vi) Initial result on developing an onboard table lookup method to obtain almost fuel optimal solutions in real-time.}
    }
  5. Johnson, A. E., Huertas, A., Werner, R. A. and Montgomery, J. F. (2008). Analysis of On-Board Hazard Detection and Avoidance for Safe Lunar Landing . IEEE Aerospace Conference. Source
    BibTeX
    @inproceedings{johnson2008analysis,
      title = {Analysis of On-Board Hazard Detection and Avoidance for Safe Lunar Landing},
      author = {Johnson, Andrew E. and Huertas, Andres and Werner, Robert A. and Montgomery, James F.},
      booktitle = {IEEE Aerospace Conference},
      pages = {1-9},
      address = {Big Sky, Montana},
      year = {2008},
      doi = {10.1109/aero.2008.4526301},
      abstract = {Landing hazard detection and avoidance technology is being pursued within NASA to improve landing safety and increase access to sites of interest on the lunar surface. The performance of a hazard detection and avoidance system depends on properties of the terrain, sensor performance, algorithm design, vehicle characteristics and the overall all guidance navigation and control architecture. This paper analyzes the size of the region that must be imaged, sensor performance parameters and the impact of trajectory angle on hazard detection performance. The analysis shows that vehicle hazard tolerance is the driving parameter for hazard detection system design.}
    }
  6. Grip, H. F., Johnson, W., Malpica, C., Scharf, D. P., Mandić, M., Young, L., Allan, B., Mettler, B. and San Martin, M. (2017). Flight Dynamics of a Mars Helicopter . European Rotorcraft Forum. Source
    BibTeX
    @inproceedings{grip2017flight,
      title = {Flight Dynamics of a Mars Helicopter},
      author = {Grip, Håvard Fjær and Johnson, Wayne and Malpica, Carlos and Scharf, Daniel P. and Mandić, Milan and Young, Larry and Allan, Brian and Mettler, Bérénice and San Martin, Miguel},
      booktitle = {European Rotorcraft Forum},
      year = {2017},
      url = {https://dataverse.jpl.nasa.gov/dataset.xhtml?persistentId=hdl:2014/47585}
    }
  7. Aagren, T. S., Ruan, A. W., Malpica, C., Withrow-Maser, S. and Meyn, L. (2025). In-flight System Identification of the Ingenuity Mars Helicopter . AIAA SCITECH Forum, 20240014856. Source
    BibTeX
    @inproceedings{aagren2025flight,
      title = {In-flight System Identification of the Ingenuity Mars Helicopter},
      author = {Aagren, Tove S. and Ruan, Allen W. and Malpica, Carlos and Withrow-Maser, Shannah and Meyn, Larry},
      booktitle = {AIAA SCITECH Forum},
      number = {20240014856},
      institution = {NASA},
      year = {2025},
      doi = {10.2514/6.2025-0007},
      abstract = {The 68th and 69th flights of NASA’s Ingenuity Mars Helicopter marked the first dedicated system identification flights of an aerial vehicle on another planet. Chirp signals were injected into the swashplate cyclic controls for both legs of the two out-and-back flights. Frequency responses were computed from the flight data, using both the Direct Method (DM) and the Joint-Input-Output (JIO) approach, for the identification of stability and control derivatives in forward flight conditions. The resulting identified state-space models were compared against existing flight dynamics simulation models, showing excellent correlation in the higher frequency range. External disturbances were seen to introduce a bias in the identified lower frequency responses, which was partially mitigated using the JIO method. These findings will inform future modeling and flight testing efforts of Mars rotorcraft.}
    }
  8. Afman, J. P., Feron, E. and Walker, M. (2019). A full scale atmospheric flight experimental research environment for the Mars helicopter . arXiv preprint. Source
    BibTeX
    @article{afman2019full,
      title = {A full scale atmospheric flight experimental research environment for the Mars helicopter},
      author = {Afman, J. Pablo and Feron, Eric and Walker, Mitchell},
      journal = {arXiv preprint},
      year = {2019},
      url = {https://arxiv.org/abs/1910.09290}
    }
  9. Wang, Z. (2023). A Survey on Convex Optimization for Guidance and Control of Vehicular Systems . arXiv. Source
    BibTeX
    @article{wang2023survey,
      title = {A Survey on Convex Optimization for Guidance and Control of Vehicular Systems},
      author = {Wang, Zhenbo},
      journal = {arXiv},
      year = {2023},
      doi = {10.48550/arxiv.2311.05115},
      abstract = {Guidance and control (G&C) technologies play a central role in the development and operation of vehicular systems. The emergence of computational guidance and control (CG&C) and highly efficient numerical algorithms has opened up the great potential for solving complex constrained G&C problems onboard, enabling higher level of autonomous vehicle operations. In particular, convex-optimization-based G&C has matured significantly over the years and many advances continue to be made, allowing the generation of optimal G&C solutions in real-time for many vehicular systems in aerospace, automotive, and other domains. In this paper, we review recent major advances in convex optimization and convexification techniques for G&C of vehicular systems, focusing primarily on three important application fields: 1) Space vehicles for powered descent guidance, small body landing, rendezvous and proximity operations, orbital transfer, spacecraft reorientation, space robotics and manipulation, spacecraft formation flying, and station keeping; 2) Air vehicles including hypersonic/entry vehicles, missiles and projectiles, launch/ascent vehicles, and low-speed air vehicles; and 3) Motion control and powertrain control of ground vehicles. Throughout the paper, we draw figures that illustrate the basic mission concepts and scenarios and present tables that summarize similarities and distinctions among the key problems, ideas, and approaches. Where available, we provide comparative analyses and reveal correlations between different applications. Finally, we identify open challenges and issues, discuss potential opportunities, and make suggestions for future research directions.}
    }
  10. Ueda, S., Ito, T., Yokota, K., Sakai, S., Ishida, T., Miyazawa, Y., Kushiki, K., Fukuda, S. and Sawai, S. (2025). On-orbit operation results of the powered descent guidance algorithm for pinpoint lunar landing . Herald of the Bauman Moscow State Technical University. Series Instrument Engineering. Source
    BibTeX
    @article{ueda2025orbit,
      title = {On-orbit operation results of the powered descent guidance algorithm for pinpoint lunar landing},
      author = {Ueda, Satoshi and Ito, Takahiro and Yokota, Kentaro and Sakai, Shinichiro and Ishida, Takayuki and Miyazawa, Yu and Kushiki, Kenichi and Fukuda, Seisuke and Sawai, Shujiro},
      journal = {Herald of the Bauman Moscow State Technical University. Series Instrument Engineering},
      volume = {236},
      pages = {47-61},
      publisher = {Elsevier BV},
      year = {2025},
      doi = {10.1016/j.actaastro.2025.06.039},
      abstract = {This paper discusses the on-orbit operation results of the powered descent guidance algorithm that played a key role in the navigation, guidance, and control of the Smart Lander for Investigating Moon (SLIM) mission, which made a pinpoint lunar landing in January 2024. The SLIM mission successfully demonstrated pinpoint lunar landing, confirmed within 100 meters. The technology required for a pinpoint lunar landing consists of a variety of elements: a vision-based navigation system that accurately detects the position of the spacecraft relative to the lunar surface, a propulsion system that operates stably, a landing descent reference trajectory that is in harmony with the system design, mature spacecraft navigation, guidance, and control technology, and precise guidance algorithms to guide the spacecraft to its target location. The SLIM landing descent sequence consists of a powered descent phase to reduce the horizontal velocity and a vertical descent phase to reduce the remaining vertical velocity after the powered descent. Dedicated guidance algorithms applied to both phases use advanced mathematical methods for high-precision landing and backup options. This paper focuses on the powered descent guidance algorithm. The powered descent phase consists of three boost parts and two coasting parts. The coasting sections adjust the attitude of the spacecraft and the direction of the body-fixed navigation camera. Image acquisition for vision-based navigation and guidance calculation for the next boost section are performed before each boost section. In the boost section, the spacecraft's translational control law follows the trajectory generated by the guidance algorithm. The powered descent guidance algorithm uses an explicit guidance method that sequentially calculates the optimal trajectory and necessary controls during flight. Highly accurate guidance algorithms are required to adapt the control acceleration to the thrust acceleration of the spacecraft. To achieve this, unique mechanisms are introduced: the trajectory is generated based on polynomials with dimensionless time and the boost start time is corrected based on navigation and machine learning. This combination achieved a guidance accuracy of 0.2%, contributing to the pinpoint lunar landing. This paper presents details of these mechanisms and their evaluation based on telemetry from the landing.}
    }
  11. Casoliva, J., Singh, G., Brugarolas, P. and Way, D. W. (2021). Reconstructed Flight Performance of the Powered Descent Guidance and Control System for the Mars 2020 Perseverance Mission . AAS/AIAA Astrodynamics Specialist Conference. Source
    BibTeX
    @inproceedings{casoliva2021reconstructed,
      title = {Reconstructed Flight Performance of the Powered Descent Guidance and Control System for the Mars 2020 Perseverance Mission},
      author = {Casoliva, Jordi and Singh, Gurkirpal and Brugarolas, Paul and Way, David W.},
      booktitle = {AAS/AIAA Astrodynamics Specialist Conference},
      publisher = {JPL Open Repository},
      year = {2021},
      url = {https://dataverse.jpl.nasa.gov/dataset.xhtml?persistentId=hdl:2014/55198}
    }
  12. Toupet, O., Biesiadecki, J., Rankin, A., Steffy, A., Meirion-Griffith, G., Levine, D., Schadegg, M. and Maimone, M. (2020). Traction Control on the Curiosity Mars Rover: Algorithm and Flight Results . Journal of Field Robotics. Source
    BibTeX
    @article{toupet2020traction,
      title = {Traction Control on the Curiosity Mars Rover: Algorithm and Flight Results},
      author = {Toupet, Olivier and Biesiadecki, Jeffrey and Rankin, Arturo and Steffy, Amanda and Meirion-Griffith, Gareth and Levine, Dan and Schadegg, Maximilian and Maimone, Mark},
      journal = {Journal of Field Robotics},
      year = {2020},
      doi = {10.48577/jpl.hkzuqs},
      abstract = {No abstract available.}
    }
  13. Toupet, O., Biesiadecki, J., Rankin, A., Steffy, A., Meirion-Griffith, G., Levine, D., Schadegg, M. and Maimone, M. (2018). Traction Control Design and Integration Onboard the Mars Science Laboratory Curiosity Rover . IEEE Aerospace Conference. Source
    BibTeX
    @inproceedings{toupet2018traction,
      title = {Traction Control Design and Integration Onboard the {Mars Science Laboratory} {Curiosity} Rover},
      author = {Toupet, Olivier and Biesiadecki, Jeffrey and Rankin, Arturo and Steffy, Amanda and Meirion-Griffith, Gareth and Levine, Dan and Schadegg, Maximilian and Maimone, Mark},
      booktitle = {IEEE Aerospace Conference},
      pages = {1--20},
      address = {Big Sky, Montana},
      year = {2018},
      doi = {10.1109/aero.2018.8396761},
      abstract = {The Mars Science Laboratory (MSL) Curiosity rover experienced increasing wheel damage beginning in October 2013. While the wheels were designed to operate with considerable damage, the rate at which damage was occurring was unexpected and raised concerns regarding wheel lifetime. The Jet Propulsion Laboratory (JPL) has now developed and deployed new software on Curiosity that reduces the forces acting on the wheels. Our new Traction Control algorithm adapts each wheel's speed to fit the terrain it drives over. It does not rely on any a priori knowledge of the terrain, and instead leverages the rover's measured attitude rates and suspension angles, together with a rigid-body kinematics model, to estimate the real-time wheel-terrain contact angles and ideal, no-slip wheel angular rates. In addition, free-floating “wheelies” are detected and autonomously corrected. In this paper, we describe the algorithm, its ground testing campaign and associated challenges, and finally its validation and performance in flight. Ground test data demonstrates reductions in the forces acting on the wheels and validates the wheelie-damping capability. Secondary benefits in some terrains include a reduction in heading deviations while climbing rocks, with a reduction in slip in certain sandy terrains. Preliminary validation from flight data confirms these findings.}
    }
  14. Tzanetos, T., Aung, M., Balaram, J. (., Grip, H. F., Karras, J. T., Canham, T. K., Kubiak, G., Anderson, J., Merewether, G., Starch, M., Pauken, M., Cappucci, S., Chase, M., Golombek, M., Toupet, O., Smart, M. C., Dawson, S., Blandon Ramirez, E., Lam, J., Stern, R., Chahat, N., Ravich, J., Hogg, R., Pipenberg, B., Keennon, M. and Williford, K. H. (2022). Ingenuity Mars Helicopter: From Technology Demonstration to Extraterrestrial Scout . IEEE Aerospace Conference. Source
    BibTeX
    @inproceedings{tzanetos2022ingenuity,
      title = {Ingenuity Mars Helicopter: From Technology Demonstration to Extraterrestrial Scout},
      author = {Tzanetos, Theodore and Aung, MiMi and Balaram, J. (Bob) and Grip, Håvard Fjær and Karras, Jaakko T. and Canham, Timothy K. and Kubiak, Gerik and Anderson, Joshua and Merewether, Gene and Starch, Michael and Pauken, Mike and Cappucci, Stefano and Chase, Matthew and Golombek, Matthew and Toupet, Olivier and Smart, Marshall C. and Dawson, Stephen and Blandon Ramirez, Erick and Lam, Johnny and Stern, Ryan and Chahat, Nacer and Ravich, Joshua and Hogg, Robert and Pipenberg, Benjamin and Keennon, Matthew and Williford, Kenneth H.},
      booktitle = {IEEE Aerospace Conference},
      pages = {01-19},
      publisher = {IEEE},
      year = {2022},
      doi = {10.1109/aero53065.2022.9843428},
      abstract = {On April 19, 2021, NASA's Ingenuity Mars Helicopter successfully executed humanity's historic first flight on Mars. In the flights that followed, Ingenuity continued to explore the boundaries of what was aerodynamically, energetically, and operationally possible, and took on increasingly daring missions in the process. Over time, Ingenuity's mission has evolved from a “Technology Demonstration” of the first rotorcraft to fly on Mars to an “Operations Demonstration” of scientific aerial exploration scenarios on Mars. Ingenuity's activities to date have yielded a rich first-of-its-kind data set and extensive operational experience. This paper describes the Ingenuity Mars Helicopter's operation and technical performance. It details the approach and considerations involved in remotely operating a rotorcraft on Mars from Earth. It presents the performance of the vehicle in the extremely thin Martian atmosphere compared to predicted design values, based on analysis and testing on Earth. The agreement between predicted and observed performance has been excellent. This paper also discusses scientific impacts that Ingenuity Mars Helicopter has been able to contribute to the Mars 2020 Perseverance mission during Ingenuity's Operations Demonstration phase. The performance of the vehicle, the operational experiences and lessons learned, anomalies encountered and their resolutions presented in this paper critically inform the formulation, design, and development of the next generation of advanced aerial rotorcraft platforms for Mars and other extra-terrestrial bodies.11Copyright 2021 Jet Propulsion Laboratory, California Institute of Technology. Government sponsorship acknowledged.22The research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004).}
    }
  15. Grip, H. (2023). Evolving the Ingenuity Aerial GN&C System for Mars Sample Recovery . Root. doi.org/10.48577/jpl.ojoe9g
    BibTeX
    @misc{grip2023evolving,
      title = {Evolving the Ingenuity Aerial GN&C System for Mars Sample Recovery},
      author = {Grip, Havard},
      journal = {Root},
      year = {2023},
      doi = {10.48577/jpl.ojoe9g},
      abstract = {No abstract available.}
    }
  16. Anderson, J. L., Brown, T. L., Cacan, M., Kubiak, G., Jasour, A. and Rothenberger, N. Z. (2024). Lessons From Ingenuity's Climb Up Jezero Crater Delta . IEEE Aerospace Conference. Source
    BibTeX
    @inproceedings{anderson2024lessons,
      title = {Lessons From Ingenuity's Climb Up Jezero Crater Delta},
      author = {Anderson, Joshua L. and Brown, Travis L. and Cacan, Martin and Kubiak, Gerik and Jasour, Ashkan and Rothenberger, Noah Z.},
      booktitle = {IEEE Aerospace Conference},
      pages = {1--15},
      publisher = {IEEE},
      year = {2024},
      doi = {10.1109/aero58975.2024.10521181},
      abstract = {As Perseverance began exploring the delta of Jezero crater, the Ingenuity team took on the most ambitious flights of the mission to date. Designed for the relatively flat terrain of the crater floor, the Ingenuity team now needed to climb up and fly through the rocky channels of the Jezero delta. Before the flights in the delta could even be attempted, the team needed to upgrade Ingenuity’s flight software to identify landing hazards and utilize digital elevation maps in the navigation filter. After upgrading the flight software, the team needed to work through a series of checkout flights to validate this new functionality prior to climbing the delta. The Jezero delta features narrow channels, limiting Ingenuity and Perseverance’s available traverse paths. This required Ingenuity and Perseverance to follow similar routes, necessitating new processes for coordinating Ingenuity and Perseverance’s parallel flights and drives. In previous traverses, Ingenuity was able to take separate, shorter routes to the destination, but this climb required Ingenuity to keep pace with Perseverance’s record setting drives. This rapid traverse pushed the Ingenuity team to optimize the flight planning process to shorten the nominal two week flight cadence to under a week between flights. The narrow passages of the delta also limited Ingenuity’s telecom propagation, providing only small windows of strong communication with Perseverance. All these above constraints required the Perseverance and Ingenuity teams to carefully orchestrate both vehicles’ journey up the delta, keeping both vehicles in lockstep while minimizing the chance that one vehicle would hold up the other’s progress. The paper will cover flight 34 and beyond, Ingenuity’s delta climb, as well as the lessons learned from both the challenging flights in the delta terrain and the required multi-vehicle coordination of the climb.12}
    }
  17. Anderson, J. L., Karras, J. T., Cacan, M., Kubiak, G., Pyrzak, G., Dor, H. and Pipenberg, B. (2023). Ingenuity, One Year of Flying on Mars . IEEE Aerospace Conference. Source
    BibTeX
    @inproceedings{anderson2023ingenuity,
      title = {Ingenuity, One Year of Flying on Mars},
      author = {Anderson, Joshua L. and Karras, Jaakko T. and Cacan, Martin and Kubiak, Gerik and Pyrzak, Guy and Dor, Harel and Pipenberg, Benjamin},
      booktitle = {IEEE Aerospace Conference},
      publisher = {JPL Open Repository},
      year = {2023},
      doi = {10.1109/aero55745.2023.10115837}
    }
  18. Macala, G. A., Lee, A. Y. and Wang, E. K. (2012). Feasibility study of two candidate reaction wheel/thruster hybrid control architecture designs for the Cassini spacecraft . AIAA Guidance, Navigation, and Control Conference. Source
    BibTeX
    @inproceedings{macala2012feasibility,
      title = {Feasibility study of two candidate reaction wheel/thruster hybrid control architecture designs for the Cassini spacecraft},
      author = {Macala, Glenn A. and Lee, Allan Y. and Wang, Eric K.},
      booktitle = {AIAA Guidance, Navigation, and Control Conference},
      publisher = {American Institute of Aeronautics and Astronautics},
      year = {2012},
      doi = {10.2514/6.2012-4541}
    }
  19. Serricchio, F., Serricchio, F., San Martin, A. M., Brugarolas, P. and Casoliva, J. (2021). The Mars 2020 Perseverance Navigation Filter . AAS/AIAA Astrodynamics Specialist Conference. Source
    BibTeX
    @inproceedings{serricchio2021mars,
      title = {The Mars 2020 Perseverance Navigation Filter},
      author = {Serricchio, Frederick and Serricchio, Frederick and San Martin, A. Miguel and Brugarolas, Paul and Casoliva, Jordi},
      booktitle = {AAS/AIAA Astrodynamics Specialist Conference},
      publisher = {JPL Open Repository},
      year = {2021},
      url = {https://dataverse.jpl.nasa.gov/dataset.xhtml?persistentId=hdl:2014/55199}
    }
  20. Briden, J., Gurga, T., Johnson, B., Cauligi, A. and Linares, R. (2025). Tight Constraint Prediction of Six-Degree-of-Freedom Transformer-based Powered Descent Guidance . AIAA SPACE Conference. Source
    BibTeX
    @inproceedings{briden2025tight,
      title = {Tight Constraint Prediction of Six-Degree-of-Freedom Transformer-based Powered Descent Guidance},
      author = {Briden, Julia and Gurga, Trey and Johnson, Breanna and Cauligi, Abhishek and Linares, Richard},
      booktitle = {AIAA SPACE Conference},
      publisher = {JPL Open Repository},
      year = {2025},
      doi = {10.48577/jpl.ahyi2p},
      abstract = {This work introduces Transformer-based Successive Convexification (T-SCvx), an extension of Transformer-based Powered Descent Guidance (T-PDG), generalizable for efficient sixdegree-of-freedom (DoF) fuel-optimal powered descent trajectory generation. Our approach significantly enhances the sample efficiency and solution quality for nonconvex-powered descent guidance by employing a rotation invariant transformation of the sampled dataset. T-PDG was previously applied to the 3-DoF minimum fuel powered descent guidance problem, improving solution times by up to an order of magnitude compared to lossless convexification (LCvx). By learning to predict the set of tight or active constraints at the optimal control problem’s solution, Transformer-based Successive Convexification (T-SCvx) creates the minimal reduced-size problem initialized with only the tight constraints, then uses the solution of this reduced problem to warm-start the direct optimization solver. 6-DoF powered descent guidance is known to be challenging to solve quickly and reliably due to the nonlinear and non-convex nature of the problem, the discretization scheme heavily influencing solution validity, and reference trajectory initialization determining algorithm convergence or divergence. Our contributions in this work address these challenges by extending T-PDG to learn the set of tight constraints for the successive convexification (SCvx) formulation of the 6-DoF powered descent guidance problem. In addition to reducing the problem size, feasible and locally optimal reference trajectories are also learned to facilitate convergence from the initial guess. T-SCvx enables onboard computation of real-time guidance trajectories, demonstrated by a 6-DoF Mars powered landing application problem.}
    }
  21. Carney, T. M. (1961). An Automatic Terminal Guidance System for Rendezvous with a Satellite . NASA Langley Research Center, NASA TN D-923 (L-1522). Source
    BibTeX
    @techreport{carney1961automatic,
      title = {An Automatic Terminal Guidance System for Rendezvous with a Satellite},
      author = {Carney, Terrance M.},
      number = {NASA TN D-923 (L-1522)},
      institution = {NASA Langley Research Center},
      year = {1961},
      url = {https://ntrs.nasa.gov/citations/19980228129},
      abstract = {This study includes a consideration of the design philosophy for an automatic terminal guidance system, a derivation of guidance equations required, and an outline of the general type of instrumentation necessary to provide the essential information. A control system for a sample vehicle is analyzed. A representative case, rendezvous with a satellite in circular orbit at 400 nautical miles, was examined. Terminal-stage nominal burning times of 200 and 400 seconds were used. For the 200-second case, initial errors in circumferential displacement of +/- 25,000 feet, in radial displacement of 7,000 to -9,000 feet, and in lateral displacement of +/- 20,000 feet were within the capabilities of the system. Velocity errors of 300 to -400 ft/sec in the circumferential direction, 180 to -200 ft/sec in the radial direction, and velocity offsets of at least 20 (+/- 800 ft/sec) in the lateral direction could also be handled. The 400-second case was capable of correcting larger errors, but limits were not determined. The dependence of required characteristic velocity on initial errors was determined and it was found that increases over the nominal terminal-stage characteristic velocity of the order of 15 percent covered most of the previously mentioned in-plane errors. The requirements were more severe for cases with lateral velocity offsets. A simplified set of guidance equations was tested and produced only slight variations in performance. Overall velocity requirements and mass ratios were determined for terminal-stage burning times of 100, 200, 300, and 400 seconds and for a range of transfer angles by using exact calculations for the terminal stage and an impulsive launching velocity. These results indicated that the shortest burning time consistent with the launch guidance errors expected gave the best mass ratio.}
    }
  22. Cauligi, A., Albee, K., de la Croix, J.-P. and Brockers, R. (2025). CRESCENT: Collision-Free Highly-Constrained Trajectory Optimization for Driving on the Moon . IEEE Transactions on Field Robotics. Source
    BibTeX
    @article{cauligi2025crescent,
      title = {CRESCENT: Collision-Free Highly-Constrained Trajectory Optimization for Driving on the Moon},
      author = {Cauligi, Abhishek and Albee, Keenan and de la Croix, Jean-Pierre and Brockers, Roland},
      journal = {IEEE Transactions on Field Robotics},
      publisher = {JPL Open Repository},
      year = {2025},
      doi = {10.48577/jpl.m3hu8h},
      abstract = {Rovers have been a mainstay of planetary exploration missions, significantly expanding ourknowledge in planetary science. However, past rover missions have involved significant human supervisionto oversee rover operations, a state-of-practice that scales poorly for the next generation of missions. Inthis work, we present the development of CRESCENT, a motion planning algorithm developed for theupcoming multi-agent CADRE Lunar rover mission. CRESCENT was designed to safely drive a miniaturerover platform in a highly cluttered unmapped Lunar environment, executing complex motion directivesfrom CADRE’s team-level autonomy while meeting far stricter dynamical and temporal constraints thanexisting on-board planetary rover planning algorithms are capable of satisfying. Our hierarchical approachformulates an efficient numerical trajectory optimization-based motion planning algorithm that makesuse of nonlinear optimization to solve the planning problem in real-time. We demonstrate the efficiencyof our proposed approach through extensive simulations and hardware testing in a representative Lunarenvironment. Following CADRE’s upcoming deployment on the Lunar surface, CRESCENT will be thefirst nonlinear optimization-based trajectory optimization approach used on another celestial body.}
    }
  23. Wolf, A. A., Acikmese, B., Cheng, Y., Casolica, J., Carson, J. M. and Ivanov, M. C. (2011). Toward improved landing precision on Mars. . Aerospace Conference. Source
    BibTeX
    @inproceedings{wolf2011improved,
      title = {Toward improved landing precision on Mars.},
      author = {Wolf, Aron A. and Acikmese, Behcet and Cheng, Yang and Casolica, Jordi and Carson, John M. and Ivanov, Mark C.},
      booktitle = {Aerospace Conference},
      pages = {1-8},
      publisher = {IEEE},
      year = {2011},
      doi = {10.1109/aero.2011.5747243},
      abstract = {Mars landers to date have flown ballistic entry trajectories with no trajectory control after the final maneuver before entry.12Improvements in landing accuracies (from ~150 km from the target for Mars Pathfinder to ~30-40 km for MER and Phoenix) have been driven by approach navigation improvements. MSL will fly the first guided-entry trajectory to Mars, further improving accuracy to ~10-12 km from the target. For future missions, landing within ~100m is desired to assure landing safety close to a target of high scientific interest in irregular terrain, or to land near a previously landed asset. Improvements in approach navigation alone are not sufficient to achieve this requirement. If approach navigation error and IMU error are eliminated, the dominant error source is wind drift on the parachute, with map-tie error also significant. Correcting these errors requires terrain-relative navigation (TRN), which can be accomplished with passive imaging supplemented by radar for terrain sensing (with onboard navigation capable of processing measurements from IMU, imaging, and radar). Additionally, near-optimal-ΔV powered descent guidance is needed to minimize the amount of propellant required to reach the target. The capability to land within 100m can be applied in different landing modes depending on how much fuel is carried.}
    }
  24. Mars 2020 Entry, D. and Landing Team. (2023). Mars 2020 Entry, Descent & Landing As-Flown Reconstruction Report . Jet Propulsion Laboratory, California Institute of Technology, JPL Publication 22-1. Source
    BibTeX
    @techreport{mars2023mars,
      title = {Mars 2020 Entry, Descent & Landing As-Flown Reconstruction Report},
      author = {{Mars 2020 Entry, Descent} and {Landing Team}},
      number = {JPL Publication 22-1},
      institution = {Jet Propulsion Laboratory, California Institute of Technology},
      year = {2023},
      doi = {10.48577/jpl.16mnyf},
      abstract = {On February 18, 2021, amidst a global pandemic sweeping the nation and the world, the Perseverance rover safely completed its autonomous landing in Jezero Crater. The largest, heaviest, and most complex planetary rover ever built depended on its seven-minute entry, descent, and landing (EDL) sequence to touch down safely at the most dangerous landing site ever attempted on Mars. Building upon the success of the Mars Science Laboratory (MSL) mission, the Mars 2020 EDL system preserved as much of the MSL EDL architecture as possible while simultaneously fixing issues identified both pre- and post-MSL’s landing, adding mission enabling new EDL capabilities, and accommodating new mission-enhancing payloads. Reconstruction results to date have shown that the Mars 2020 EDL system performed largely as expected. Issues identified with MSL’s landing were successfully addressed during the Mars 2020 landing. The mission’s two new landing capabilities, range trigger and terrain relative navigation, proved critical in identifying and reaching a safe landing target within the hazardous Jezero Crater landing site, a site deemed too hazardous for previous landing systems. Finally, new EDL instrumentation and a new EDL camera suite gave the team greater insight into the landing performance and brought the world along for the ride. This As-Flown Reconstruction Report details the design, development challenges, pre-flight expectations, and flight performance of almost all aspects of the Mars 2020 EDL system. While this report captures the best understanding of the system as of the time of publication, no one knows better than this team that additional time and scrutiny will yield further insights; the team’s scrutiny of the MSL EDL system throughout the development of Mars 2020 led to several fixes and improvements to the EDL design. As a result, this report is a snapshot in time of the team’s best understanding of the performance of the system; expect that understanding to grow and evolve with future efforts. The Mars 2020 EDL team faced numerous challenges, some expected, some not, over the project’s lifecycle. Their engineering prowess, creativity, dedication, and can-do attitude allowed the tight-knit team to face each of these challenges and overcome them. This report documents their story. With Mars 2020’s successful landing, the edge of the Mars landing envelope has been pushed out once again, enabling the first controlled aircraft flights on another planet by the Ingenuity helicopter, the search for evidence of past life by the Perseverance rover, and the beginning of the long-envision Mars sample return campaign.}
    }
  25. Braun, R., Manning, R. M., Braun, R. and Manning, R. M. (2026). Mars Exploration Entry, Descent and Landing Challenges . IEEE Aerospace Conference. Source
    BibTeX
    @inproceedings{braunndmars,
      title = {Mars Exploration Entry, Descent and Landing Challenges},
      author = {Braun, R.D. and Manning, Robert M. and Braun, R.D. and Manning, Robert M.},
      booktitle = {IEEE Aerospace Conference},
      pages = {1-18},
      publisher = {IEEE},
      year = {2026},
      doi = {10.1109/aero.2006.1655790},
      abstract = {The United States has successfully landed five robotic systems on the surface of Mars. These systems all had landed mass below 0.6 metric tons (t), had landed footprints on the order of hundreds of km and landed at sites below -1.4 km MOLA elevation due the need to perform entry, descent and landing operations in an environment with sufficient atmospheric density. At present, robotic exploration systems engineers are struggling with the challenges of increasing landed mass capability to 0.8 t while improving landed accuracy to tens of km and landing at a site as high as +2 km MOLA elevation for the Mars Science Laboratory project. Meanwhile, current plans for human exploration of Mars call for the landing of 40-80 t surface elements at scientifically interesting locations within close proximity (tens of m) of pre-positioned robotic assets. This paper summarizes past successful entry, descent and landing systems and approaches being developed by the robotic Mars exploration program to increased landed performance (mass, accuracy and surface elevation). In addition, the entry, descent and landing sequence for a human exploration system will be reviewed, highlighting the technology and systems advances required.}
    }
  26. Paton, M. D., Savijärvi, H., Harri, A.-M., Leino, J., Bertrand, T., Viúdez-Moreiras, D., Lorenz, R. D. and Newman, C. (2024). Inferred wind speed and direction during the descent and landing of Perseverance on Mars . Icarus. Source
    BibTeX
    @article{paton2024inferred,
      title = {Inferred wind speed and direction during the descent and landing of Perseverance on Mars},
      author = {Paton, Mark D. and Savijärvi, Hannu and Harri, Ari-Matti and Leino, J. and Bertrand, Tanguy and Viúdez-Moreiras, Daniel and Lorenz, Ralph D. and Newman, C.},
      journal = {Icarus},
      volume = {415},
      pages = {116045},
      publisher = {Elsevier BV},
      year = {2024},
      doi = {10.1016/j.icarus.2024.116045}
    }
  27. Jackson, B., Fenton, L., Brown, T., Munguira, A., Martinez, G., Newman, C., Viúdez-Moreiras, D., Golombek, M., Lorenz, R., Paton, M. D. and Conway, D. (2024). Profiling Near-Surface Winds on Mars Using Attitude Data from Mars 2020 Ingenuity . The Planetary Science Journal. Source
    BibTeX
    @article{jackson2024profiling,
      title = {Profiling Near-Surface Winds on Mars Using Attitude Data from Mars 2020 Ingenuity},
      author = {Jackson, Brian and Fenton, Lori and Brown, Travis and Munguira, Asier and Martinez, German and Newman, Claire and Viúdez-Moreiras, Daniel and Golombek, Matthew and Lorenz, Ralph and Paton, Mark D. and Conway, Dylan},
      journal = {The Planetary Science Journal},
      volume = {6},
      pages = {21},
      year = {2024},
      doi = {10.3847/psj/ad8b41},
      abstract = {Abstract We used attitude data from the Mars Ingenuity helicopter with a simple steady-state model to estimate wind speeds and directions at altitudes between 3 and 24 m, the first time winds at such altitudes have been probed on Mars. We compared our estimates to wind data from the meteorology package MEDA on board the Mars 2020 Perseverance rover and to predictions from meteorological models. Wind directions inferred from Ingenuity data agreed with the directions measured by MEDA, when the latter were available, but deviated from model-predicted directions by as much as 180° in some cases. The inferred wind speeds are often much higher than expected. For example, meteorological predictions suggest that Ingenuity should not have seen wind speeds above about 15 m s −1 during its 59th flight, but we inferred speeds reaching nearly 25 m s −1 . For flights during which we have MEDA data to compare to, inferred wind speeds imply friction velocities >1 m s −1 and roughness lengths >10 cm, which seem implausibly large. These results suggest that Ingenuity was probing winds sensitive to aerodynamic conditions hundreds of meters upwind instead of the conditions very near Mars 2020, but they may also reflect a need for updated boundary layer wind models. An improved model for Ingenuity’s aerodynamic response that includes the effects of transient winds may also modify our results. In any case, the work here provides a foundation for exploration of planetary boundary layers using drones and suggests important future avenues for research and development.}
    }
  28. Mavroidis, C., Rowe, P. and Dubowsky, S. (2026). Inferred end-point control of long reach manipulators . SAE Technical Paper Series. Source
    BibTeX
    @inproceedings{mavroidisndinferred,
      title = {Inferred end-point control of long reach manipulators},
      author = {Mavroidis, Constantinos and Rowe, P. and Dubowsky, Steven},
      booktitle = {SAE Technical Paper Series},
      volume = {2},
      pages = {71-76},
      publisher = {IEEE Comput. Soc. Press},
      year = {2026},
      doi = {10.1109/iros.1995.526141},
      abstract = {The problem of the end-point control of long reach manipulator systems, consisting of a dexterous manipulator carried by deployable structures, is addressed. Such systems can be important where a manipulator must perform a task in a difficult to reach location. Power line maintenance systems and the Space Station Freedom's external maintenance robot are examples. Their supporting deployable structures can exhibit substantial vibrations, making the system very difficult to control. Here, a sensor based control algorithm, called inferred end-point control is proposed. Simulation and experimental results show that it yields stable and accurate manipulator end-effector positioning control despite of vibrations of the system's supporting structure using easily obtained strain measurements.}
    }
  29. Li, D., Dong, R.-Q., Guo, Y., Ran, G. and Li, D. (2020). Sampling-based 3-D Line-of-Sight PWA Model Predictive Control for Autonomous Rendezvous and Docking with a Tumbling Target. Source
    BibTeX
    @article{li2020sampling,
      title = {Sampling-based 3-D Line-of-Sight PWA Model Predictive Control for Autonomous Rendezvous and Docking with a Tumbling Target},
      author = {Li, Dongting and Dong, Rui-Qi and Guo, Yanning and Ran, Guangtao and Li, Dongyu},
      year = {2020},
      url = {https://arxiv.org/abs/2007.15390v3}
    }
  30. Wilde, M., Kwok Choon, S., Grompone, A. and Romano, M. (2018). Equations of Motion of Free-Floating Spacecraft-Manipulator Systems: An Engineer's Tutorial . Icarus. Source
    BibTeX
    @article{wilde2018equations,
      title = {Equations of Motion of Free-Floating Spacecraft-Manipulator Systems: An Engineer's Tutorial},
      author = {Wilde, Markus and Kwok Choon, Stephen and Grompone, Alessio and Romano, Marcello},
      journal = {Icarus},
      volume = {5},
      pages = {41--41},
      publisher = {Frontiers Media SA},
      year = {2018},
      doi = {10.3389/frobt.2018.00041},
      abstract = {The paper provides a step-by-step tutorial on the Generalized Jacobian Matrix (GJM) approach for modeling and simulation of spacecraft-manipulator systems. The General Jacobian Matrix approach describes the motion of the end-effector of an underactuated manipulator system solely by the manipulator joint rotations, with the attitude and position of the base-spacecraft resulting from the manipulator motion. The coupling of the manipulator motion with the base-spacecraft are thus expressed in a generalized inertia matrix and a GJM. The focus of the paper lies on the complete analytic derivation of the generalized equations of motion of a free-floating spacecraft-manipulator system. This includes symbolic analytic expressions for all inertia property matrices of the system, including their time derivatives and joint-angle derivatives, as well as an expression for the generalized Jacobian of a generic point on any link of the spacecraft-manipulator system. The kinematics structure of the spacecraft-manipulator system is described both in terms of direction-cosine matrices and unit quaternions. An additional important contribution of this paper is to propose a new and more detailed definition for the modes of maneuvering of a spacecraft-manipulator. In particular, the two commonly used categories free-flying and free-floating are expanded by the introduction of five categories, namely floating, rotation-floating, rotation-flying, translation-flying, and flying. A fully-symbolic and a partially-symbolic option for the implementation of a numerical simulation model based on the proposed analytic approach are introduced and exemplary simulation results for a planar four-link spacecraft-manipulator system and a spatial six-link spacecraft manipulator system are presented.}
    }
  31. Hu, T., Guo, W., Liu, R., Xu, X., Qian, R., Chen, J., Yuan, S. and Xie, L. (2025). Energy-Constrained Navigation for Planetary Rovers under Hybrid RTG-Solar Power. arxiv.org/abs/2509.15062v1
    BibTeX
    @misc{hu2025energy,
      title = {Energy-Constrained Navigation for Planetary Rovers under Hybrid RTG-Solar Power},
      author = {Hu, Tianxin and Guo, Weixiang and Liu, Ruimeng and Xu, Xinhang and Qian, Rui and Chen, Jinyu and Yuan, Shenghai and Xie, Lihua},
      year = {2025},
      url = {https://arxiv.org/abs/2509.15062v1}
    }
  32. Ishigami, G., Miwa, A., Nagatani, K. and Yoshida, K. (2007). Terramechanics-based model for steering maneuver of planetary exploration rovers on loose soil . Journal of Field Robotics, 3. Source
    BibTeX
    @article{ishigami2007terramechanics,
      title = {Terramechanics-based model for steering maneuver of planetary exploration rovers on loose soil},
      author = {Ishigami, Genya and Miwa, Akiko and Nagatani, Keiji and Yoshida, Kazuya},
      journal = {Journal of Field Robotics},
      volume = {24},
      number = {3},
      pages = {233-250},
      publisher = {Wiley},
      year = {2007},
      doi = {10.1002/rob.20187},
      abstract = {Abstract This paper presents analytical models to investigate the steering maneuvers of planetary exploration rovers on loose soil. The models are based on wheel‐soil interaction mechanics, or terramechanics, with which the traction and disturbance forces of a wheel are evaluated for various slip conditions. These traction forces are decomposed into the longitudinal and lateral directions of the wheel. The latter component, termed the side force has a major influence in characterizing the steering maneuvers of the rover. In this paper, the wheel‐soil mechanics models are developed with particular attention to the side force and the validity of the model is confirmed by using a single‐wheel test bed. The motion profile of the entire rover is numerically evaluated by incorporating the wheel‐soil models into an articulated multibody model that describes the motion dynamics of the vehicle's body and chassis. Steering maneuvers are investigated under different steering angles by using a four‐wheel rover test bed on simulated lunar soil regolith simulant. The experimental results are compared with the simulation results using the corresponding model parameters. The proposed wheel‐and‐vehicle model demonstrates better accuracy in predicting steering maneuvers as compared to the conventional kinematics‐based model. © 2006 Wiley Periodicals, Inc.}
    }
  33. Cheng, Y. and Ansar, A. (2003). A landmark based pinpoint landing simulator . IEEE Aerospace Conference. Source
    BibTeX
    @inproceedings{cheng2003landmark,
      title = {A landmark based pinpoint landing simulator},
      author = {Cheng, Y. and Ansar, A.},
      booktitle = {IEEE Aerospace Conference},
      publisher = {JPL Open Repository},
      year = {2003},
      url = {https://dataverse.jpl.nasa.gov/dataset.xhtml?persistentId=hdl:2014/7011}
    }
  34. Kruizinga, G. (2021). Navigation of the Mars 2020 Perseverance Rover, From Earth to Jezero Crater. dataverse.jpl.nasa.gov/dataset.xhtml
    BibTeX
    @misc{kruizinga2021navigation,
      title = {Navigation of the Mars 2020 Perseverance Rover, From Earth to Jezero Crater},
      author = {Kruizinga, Gerhard},
      year = {2021},
      url = {https://dataverse.jpl.nasa.gov/dataset.xhtml?persistentId=hdl:2014/54688}
    }