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REVIEW 3 major objections 6 minor 34 references

DRIVE Through the Unpredictability:From a Protocol Investigating Slip to a Metric Estimating Command Uncertainty

T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Six-second random commands, held with a two-second steady-state tail, map a skid-steer robot's slip across its command space, and a kinetic-energy ratio turns that map into one command-unpredictability score.

desk verdict A genuinely useful multi-terrain dataset and protocol validation, with an unpredictability metric whose ice validation is undermined by the paper's own steady-state caveat. read the letter →

arxiv 2506.16593 v1 pith:MPI23TJH submitted 2025-06-19 cs.RO cs.LGcs.SYeess.SY

Nicolas Samson , William Larrivée-Hardy , William Dubois , Élie Roy-Brouard , Edith Brotherton , Dominic Baril , Julien Lépine , François Pomerleau This is my paper · ORCID
classification cs.ROcs.LGcs.SYeess.SY
keywords DRIVEprotocolsystemidentificationskid-steermobilerobotslipunpredictabilitymetrickineticenergyriskassessmentterrainfactors
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that a standardized random-sampling procedure, DRIVE, can characterize how skid-steer robots slip on different terrains well enough to serve as a system-identification standard. Using nearly five hours of driving from two robots across six terrains, it holds each randomly sampled wheel-speed command for six seconds and treats the last two seconds as steady-state slip, producing maps of longitudinal, lateral, and angular slip over the commanded velocity space. The second claim is that a kinetic-energy ratio, $\rho$, condenses that slip into one number between zero and one, which can serve as an unpredictability metric and, combined with kinetic energy, as a continuous risk assessment tool. If these claims are right, off-road deployments become comparable by a single scalar, and motion models can be corrected with empirically measured slip instead of terrain-specific hand-tuning.

What carries the argument

The load-bearing pieces are: the DRIVE protocol, which samples commands uniformly at random over the true input space in wheel speeds and splits each six-second interval into a transient window plus two steady-state windows; the ideal differential-drive (IDD) motion model used as the no-slip reference for defining slip $g = u - x$; a Gaussian kernel smoother that converts sparse sampled commands into a uniform grid of slip values over the command space; and the unpredictability metric built from an inertia-matrix model of the robot as a uniform rectangular prism, with commanded kinetic energy $K_u$, measured kinetic energy $K_x$, and alignment weights $\alpha,\beta$ that penalize direction mismatch. The arctangent construction in $\rho$ is what keeps the metric bounded in $[0,1]$ and symmetric with respect to the ratio $K_u/K_x$.

What would settle it

Run the same DRIVE sequence on ice but compute $\rho$ from the last two seconds of each six-second step and from an extended tail lasting several additional seconds after the step; if the two estimates drift apart consistently as the window lengthens, the two-second steady-state window is capturing residual transients rather than pure slip, and the protocol's core timing assumption is violated.

Watch

Extended reading notes

Core claim

The paper's central claim is that the DRIVE protocol's random sample of wheel-speed commands, held for six seconds, reaches a quasi-steady state whose last two seconds carry the terrain-robot slip signature, and that this signature can be summarized by one scalar, $\rho$. The protocol builds transfer functions from commanded longitudinal and angular velocities to steady-state slip in three dimensions, showing that lateral slip peaks when both high longitudinal and angular commands are sent and that angular slip depends almost exclusively on commanded angular speed. The unpredictability metric is defined as $$\rho(u_{\dot p},x_{\dot p}) = \frac{4}{\pi}|\operatorname{arctan2}(K_u,K_x) - \pi/4|,$$ where $K_u$ is commanded kinetic energy and $K_x$ is measured kinetic energy after alignment penalties, so the score rises when terrain absorbs commanded energy or when the vehicle cannot reproduce its commanded motion. Field results place ice clearly apart from grass, gravel, and asphalt, with sand in between, and the paper demonstrates a continuous risk matrix with $\rho$ as likelihood and kinetic energy as severity.

Load-bearing premise

The whole characterization rests on the assumption that two seconds after each new command the robot has settled, so the final two seconds of each six-second step measure terrain-robot slip and nothing else; the paper's own ice data shows vehicle dynamics still dominating, which is the one terrain where the metric is supposed to matter.

Editorial extensions

If this is right

  • Because the protocol samples the full commanded velocity space, the resulting slip maps can be added to a motion model as empirical corrections, reducing the need to model wheel-terrain mechanics from first principles.
  • The single scalar $\rho$ allows a deployment to be compared across robots and terrains: in these experiments ice stands apart from grass, gravel, and asphalt, which cluster together, while sand is intermediate.
  • Plotting $\rho$ against commanded kinetic energy yields a continuous risk matrix in which a heavy fast robot on ice lands in the high-risk corner and a light slow robot on asphalt in the low-risk corner.
  • A minimum of about 150 sampled commands is sufficient to estimate slip transfer functions on hard terrain, giving a practical stopping rule for dataset collection.
  • The protocol's ability to quantify reachable velocities and lateral slip gives engineers a direct measurement of terrain-robot limits rather than relying on manufacturer specifications or human terrain labels.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because $\rho$ takes the absolute value around $\pi/4$, it treats under-production and over-production of kinetic energy symmetrically, so the same score can hide two different failure modes: terrain absorbing commanded energy versus vehicle dynamics producing extra motion.
  • The metric inherits uncertainty from the localization pipeline used to compute body-frame linear velocities, so comparing $\rho$ across deployments assumes comparable state-estimation quality.
  • Uniform random sampling in speed produces a triangular distribution of speed steps, which systematically under-samples large accelerations; an active-sampling variant could deliberately target the high-acceleration regions that matter for dynamic motion models.
  • If the near-identical slip distributions across grass, gravel, sand, and asphalt hold for other robots, then human terrain labels are a poor proxy for motion difficulty, and a scalar like $\rho$ could replace terrain categories in deployment risk registers.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper extends the DRIVE data-gathering protocol for off-road skid-steer UGVs and validates it with 4.9 hours and 14.7 km of driving by a 470 kg Warthog and a 75 kg Husky on six terrains. It analyzes the reachable velocity space, builds steady-state slip transfer functions, proposes a kinetic-energy-based unpredictability metric rho, and illustrates a risk-assessment visualization. The paper also reports lessons learned on mechanical wear, deployment efficiency, and protocol limitations.

Significance. If the central claims hold, the paper makes a useful contribution by offering a standardized, open-source protocol for collecting slip data and a single scalar for comparing command uncertainty across terrains and platforms. The strengths are the substantial multi-platform dataset, the detailed experimental documentation, the open release of the protocol package, and the explicit discussion of limitations. The main risks are that the steady-state assumption is not verified on ice, where the paper itself reports long transients, and that the validation of the unpredictability metric against the slip distributions is partly internal to the metric's construction. These issues are load-bearing for the paper's two central claims and require attention before publication.

major comments (3)
  1. [III.D, V.B, V.C.2] The two-second steady-state window is not demonstrated to be a steady state on ice. Section III.D defines the training interval as one transient window followed by two steady windows totaling six seconds, and Section V.C.2 states that rho is computed from the mean of the last two seconds of each sample. However, Section V.B reports that on ice the Warthog cannot reach a longitudinal speed higher than 2.5 m/s within the six seconds the command is maintained, and Section II.B states that low friction leads to longer transient behavior and a high impact of vehicle inertia. Consequently, the ice entries in the slip distributions of Figure 8, the ice transfer functions of Figure 9, and the ice unpredictability values in Figures 10-12 may mix transient acceleration with steady-state slip. Since ice is the terrain where the unpredictability metric is most salient, this is an internal inconsistency in the central claim, not merely a disagreement with external expectations. Please add an explicit convergence check per step (for example, a threshold on the slope of measured speed during the last two seconds), report the fraction of steps that fail this check on each terrain, and either rerun or exclude non-converged steps, or lengthen the command duration on low-friction surfaces.
  2. [V.C.1 and III.F, Eq. (10)] The agreement between the unpredictability metric and the slip distributions is partly circular. The metric rho is defined in Eq. (10) as a nonlinear transform of the ratio of commanded kinetic energy Ku to measured kinetic energy Kx, and Kx is computed from the measured velocities via Eqs. (8) and (9). The slip vector Bg in Eq. (4) is the difference between commanded and measured velocities, so rho is a function of the same commanded-versus-measured error that defines the slip. Therefore, observing that terrain ordering by rho matches terrain ordering by slip (Section V.C.1) is an internal consistency check, not an independent validation that rho estimates command uncertainty. Please validate rho against an external quantity, such as path-tracking error, repeated-trial dispersion, or operator-intervention or near-miss counts, and report correlation coefficients and confidence intervals. Without such independent evidence, the claim that rho quantifies command uncertainty is not established beyond being a slip-derived index.
  3. [V.C.3] The risk-assessment claim is descriptive rather than validated. In Figure 12, rho is used as a proxy for risk likelihood and kinetic energy as a proxy for risk severity, and the regions labeled (A) through (D) are illustrative. The manuscript does not provide evidence that a point in the (rho, K) plane predicts the probability or consequence of an adverse event, nor does it report any quantitative relationship between rho and measured outcomes such as tracking failures, immobilizations, or manual interventions. To support the claim that the metric can serve as a risk-assessment tool, the paper should either connect rho and kinetic energy to a measurable incident or error rate, or explicitly reframe the contribution as a qualitative visualization aid and soften the corresponding claims in the abstract and conclusion.
minor comments (6)
  1. [Abstract] The phrase 'collection data' should be 'collection of data', and the sentence beginning 'In this work, we propose...' contains a grammatical article mismatch ('an uncrewed ground vehicles') that should be corrected.
  2. [III.F, after Eq. (8)] The text states 'The total kinetic energy K in R<=0', but kinetic energy is nonnegative; this should read R>=0 or 'nonnegative reals'.
  3. [III.D and Algorithm 1] The variable hcalib is described as 'Calibration step duration', but it is used as the six-second command duration in the protocol. Renaming it to 'step duration' or 'command duration' would avoid confusion with a calibration procedure.
  4. [V.A] The coverage claim is supported mainly by visual inspection of Figure 7, where the continuous spaces are described as 'visual approximations'. A quantitative coverage measure, such as the area ratio between the measured space and the command space with uncertainty estimates, would make the coverage validation more rigorous.
  5. [V.C.1] The text says no terrain is significantly different based on the unpredictability metric and a 95% confidence interval, yet the previous sentence highlights ice as standing out with a median 1.6 times the others. Reporting the actual confidence intervals and effect sizes would remove the apparent contradiction.
  6. [VI.B] The statement that 150 commands are the minimal sample size is presented as an experimental finding, but the criterion used to determine this number is not described. Please state the procedure, for example a convergence criterion on the transfer function or a target number of samples within one kernel standard deviation, so that other users can reproduce the recommendation.

Circularity Check

1 steps flagged · score 4.0 of 10

ρ is a rescaling of the slip vector, so its agreement with slip distributions in Figure 10 is by construction; the DRIVE protocol validation itself is independent and non-circular.

  1. self definitional [Section III.F, Eq. (10); Section V.C.1, Figure 10]
    "ρ(u ˙p,x ˙p) = 4/π |arctan2(Ku,Kx)−π/4| ... Instead of dealing with slip in multiple dimensions, Figure 10 rates all the terrain tested with the Warthog using a single scalar. Therefore, the unpredictability metric based on the kinetic energy is coherent with our prior analysis of slip, with ice standing out from grass, gravel, and asphalt for both the unpredictability metric and the slip distributions."

    Eq. (10) defines ρ solely from the commanded kinetic energy Ku and the measured kinetic energy Kx, both computed from the commanded and realized velocities via Eqs. (7)-(9). The slip vector g is defined in Eqs. (3)-(4) as the difference between the same commanded and realized velocities, so ρ is a deterministic function of the same underlying slip measurements. The 'coherence' reported in V.C.1 is therefore built into the definition: any scalarization of the slip vector will rank terrains in a related order. The paper offers no independent benchmark, such as predicting a held-out command outcome or using a different sensor modality, to show that ρ estimates command uncertainty beyond restating the slip data from which it is computed.

full rationale

The DRIVE protocol validation is not circular: the coverage analysis (Section V.A), the transfer-function fits (Section V.B), and the risk-scatter plots (Section V.C.3) are built on real recorded data and do not assume the conclusions they demonstrate. The paper's self-citations to Baril et al. [8] and Samson et al. [29] provide context and prior protocol details but are not the load-bearing justification for the new empirical results. The ice steady-state concern is a correctness and validity issue about transient contamination, not a circularity of the derivation. The one genuinely circular element is the evaluation of ρ against slip distributions: because ρ is constructed from the same commanded-versus-measured velocity pairs that define slip, the agreement in Figure 10 is a self-consistency check rather than an independent confirmation. This warrants a moderate score of 4: partial circularity in the metric's evaluation, while the protocol contribution remains independent.

Assumptions & free parameters 2 free parameters · 6 assumptions · 1 invented entities

The ledger contains one hand-selected smoothing variance with inconsistent reported values, one experimental minimum-sample guideline, five domain assumptions about terrain, steady state, state estimation, and risk interpretation, and one invented metric with no independent evidence. The protocol coverage claims rest on empirical measurements, while the metric and risk interpretation rest on domain assumptions without external validation.

free parameters (2)
  • Gaussian kernel covariance Sigma = 0.64 I (Section V.B); 0.8 I / 0.25 I (Section VI.B)
    Hand-selected smoothing variance chosen so that at least four sampled commands fall within one standard deviation of each grid point. The transfer functions and unpredictability maps depend on its value, and the two sections report different values.
  • Minimum number of DRIVE commands = 150 commands
    Experimental choice for estimating transfer functions, declared after 'many tests' in Section VI.B. Affects the resolution and reliability of the slip maps without a formal convergence criterion.
assumptions (6)
  • domain assumption Ideal differential-drive (IDD) motion model maps wheel speeds to body-frame velocities with no slip (Eq. 2).
    Used as the baseline for command-space limits and for computing commanded kinetic energy in the unpredictability metric. The model is known to be wrong under slip, but the paper deliberately uses it as the reference frame for defining slip.
  • domain assumption The state vector x is directly accessible with reasonable noise.
    Section III.A states 'we will assume that we can access directly the state vector x and its noise is reasonable'. All slip and kinetic-energy computations rely on estimated linear velocities from a lidar ICP pipeline, with no explicit error quantification.
  • domain assumption The test terrain is reasonably flat and uniform, and gravity can be neglected.
    Section III.A assumptions 1 and 2 reduce the state from 18 to 9 dimensions and restrict the protocol to near-flat, homogeneous areas. The paper acknowledges that real terrain is not perfectly controlled.
  • domain assumption Steady state is reached within the last two seconds of each six-second command.
    Section III.D defines the training windows: one two-second transient window and two two-second steady-state windows. This assumption is load-bearing for all slip maps and metric values, and is contradicted by the paper's own observation of long transients on ice.
  • domain assumption Lidar ICP localization provides accurate linear body-frame velocities.
    Section IV states that linear velocities are extracted from an ICP-based localization pipeline. Section VI.B notes that linear velocities are hard to estimate reliably because of high centripetal acceleration during aggressive maneuvers, so the slip and metric inherit this uncertainty.
  • domain assumption The unpredictability metric proxies risk likelihood and kinetic energy proxies risk severity.
    Section V.C.3 builds the risk matrix on this interpretation. No independent evidence connects rho to actual failure or collision likelihood; the scenarios in Figure 12 are illustrative.
invented entities (1)
  • Unpredictability metric rho
    purpose: Single scalar on [0,1] quantifying the discrepancy between commanded and measured kinetic energy, proposed as a risk-likelihood proxy.
    No external benchmark or falsifiable prediction is provided. Its validation in Section V.C.1 compares it with slip distributions from the same data it is computed from, so the supporting evidence is internal.

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Cite this review

Pith. "Pith review of DRIVE Through the Unpredictability:From a Protocol Investigating Slip to a Metric Estimating Command Uncertainty." pith.science (2026). https://pith.science/paper/MPI23TJH

@misc{pith2026250616593,
  author       = {Pith},
  title        = {Pith review of: DRIVE Through the Unpredictability:From a Protocol Investigating Slip to a Metric Estimating Command Uncertainty},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MPI23TJH}},
  note         = {Machine review of arXiv:2506.16593}
}
read the original abstract

Off-road autonomous navigation is a challenging task as it is mainly dependent on the accuracy of the motion model. Motion model performances are limited by their ability to predict the interaction between the terrain and the UGV, which an onboard sensor can not directly measure. In this work, we propose using the DRIVE protocol to standardize the collection of data for system identification and characterization of the slip state space. We validated this protocol by acquiring a dataset with two platforms (from 75 kg to 470 kg) on six terrains (i.e., asphalt, grass, gravel, ice, mud, sand) for a total of 4.9 hours and 14.7 km. Using this data, we evaluate the DRIVE protocol's ability to explore the velocity command space and identify the reachable velocities for terrain-robot interactions. We investigated the transfer function between the command velocity space and the resulting steady-state slip for an SSMR. An unpredictability metric is proposed to estimate command uncertainty and help assess risk likelihood and severity in deployment. Finally, we share our lessons learned on running system identification on large UGV to help the community.

Figures

Figures reproduced from arXiv: 2506.16593 by the authors.

Figure 1
Figure 1. FIGURE 1 [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. FIGURE 2 [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. FIGURE 3 [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: FIGURE 4 [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: FIGURE 5 [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: FIGURE 6 [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 8
Figure 8. Figure 8: FIGURE 8 [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]
Figure 9
Figure 9. Figure 9: where axes correspond to commanded speeds (angular and longitudinal), and the color intensity expresses the slip. On the first row, we see the longitudinal slip gtx˙ for the whole command space U. The transfer function shows that longitudinal slip highly depends on bot…
Figure 10
Figure 10. Figure 10: FIGURE 10 [PITH_FULL_IMAGE:figures/full_fig_p013_10.png]
Figure 12
Figure 12. Figure 12: FIGURE 12 [PITH_FULL_IMAGE:figures/full_fig_p014_12.png]
Figure 13
Figure 13. Figure 13: FIGURE 13 [PITH_FULL_IMAGE:figures/full_fig_p016_13.png]
Figure 14
Figure 14. Figure 14: FIGURE 14 [PITH_FULL_IMAGE:figures/full_fig_p018_14.png]
Figure 15
Figure 15. Figure 15: FIGURE 15 [PITH_FULL_IMAGE:figures/full_fig_p018_15.png]

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Reference graph

Works this paper leans on

34 extracted references · 33 canonical work pages

  1. [1]

    Safe and Smooth: Certified Continuous-Time Range-Only Localization,

    F. D ¨umbgen, C. Holmes, and T. D. Barfoot, “Safe and Smooth: Certified Continuous-Time Range-Only Localization,” IEEE Robotics and Automation Letters (RA-L), vol. 8, no. 2, pp. 1117–1124, Feb. 2023

    work page 2023

  2. [2]

    Traversability-based Trajectory Planning with Quasi-Dynamic Vehicle Model in Loose Soil,

    R. Takemura and G. Ishigami, “Traversability-based Trajectory Planning with Quasi-Dynamic Vehicle Model in Loose Soil,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021, pp. 8411–8417

    work page 2021

  3. [3]

    Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning,

    L. Brunke, M. Greeff, A. W. Hall, et al. , “Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning,” Annual Review of Control, Robotics, and Autonomous Systems , vol. 5, no. 1, pp. 411–444, 2022

    work page 2022

  4. [4]

    A Survey of Off-Road Mobile Robots: Slippage Estimation, Robot Control, and Sensing Technology,

    M. D. Teji, T. Zou, and D. S. Zeleke, “A Survey of Off-Road Mobile Robots: Slippage Estimation, Robot Control, and Sensing Technology,” Journal of Intelligent & Robotic Systems , vol. 109, no. 2, p. 38, Oct. 2023

    work page 2023

  5. [5]

    High-Fidelity Yet Fast Dynamic Models of Wheeled Mobile Robots,

    N. Seegmiller and A. Kelly, “High-Fidelity Yet Fast Dynamic Models of Wheeled Mobile Robots,” IEEE Transactions on Robotics (T-RO) , vol. 32, no. 3, pp. 614–625, Jun. 2016

    work page 2016

  6. [6]

    Ma- chine learning in planetary rovers: A survey of learn- ing versus classical estimation methods in terrame- chanics for in situ exploration,

    A. J. R. Lopez-Arreguin and S. Montenegro, “Ma- chine learning in planetary rovers: A survey of learn- ing versus classical estimation methods in terrame- chanics for in situ exploration,” Journal of Terrame- chanics, vol. 97, pp. 1–17, 2021

    work page 2021

  7. [7]

    Information-Theoretic Model Predictive Control: Theory and Applications to Au- tonomous Driving,

    G. Williams, P. Drews, B. Goldfain, J. M. Rehg, and E. A. Theodorou, “Information-Theoretic Model Predictive Control: Theory and Applications to Au- tonomous Driving,” IEEE Transactions on Robotics (T-RO), vol. 34, no. 6, pp. 1603–1622, Dec. 2018

    work page 2018

  8. [8]

    DRIVE: Data-driven Robot Input Vector Exploration,

    D. Baril, S.-P. Desch ˆenes, L. Coupal, et al., “DRIVE: Data-driven Robot Input Vector Exploration,” in Pro- ceedings of the IEEE International Conference on Robotics and Automation (ICRA) , Yokohama, Japan, 2024

    work page 2024

Show all 34 references
  1. [9]

    Evalua- tion of Skid-Steering Kinematic Models for Subarctic Environments,

    D. Baril, V . Grondin, S.-P. Deschenes, et al., “Evalua- tion of Skid-Steering Kinematic Models for Subarctic Environments,” in 2020 17th Conference on Computer and Robot Vision (CRV) , IEEE, May 2020, pp. 198– 205

    2020

  2. [10]

    Analysis and control of high sideslip manoeuvres,

    C. V oser, R. Y . Hindiyeh, and J. C. Gerdes, “Analysis and control of high sideslip manoeuvres,” Vehicle System Dynamics , vol. 48, no. sup1, pp. 317–336, Dec. 2010

    2010

  3. [11]

    Analysis and Experimental Kinematics of a Skid-Steering Wheeled Robot Based on a Laser Scan- ner Sensor,

    T. Wang, Y . Wu, J. Liang, C. Han, J. Chen, and Q. Zhao, “Analysis and Experimental Kinematics of a Skid-Steering Wheeled Robot Based on a Laser Scan- ner Sensor,” Sensors, vol. 15, no. 5, pp. 9681–9702, Apr. 2015

    2015

  4. [12]

    TartanDrive: A Large-Scale Dataset for Learning Off-Road Dy- namics Models,

    S. Triest, M. Sivaprakasam, S. J. Wang, W. Wang, A. M. Johnson, and S. Scherer, “TartanDrive: A Large-Scale Dataset for Learning Off-Road Dy- namics Models,” in IEEE International Conference on Robotics and Automation (ICRA) , May 2022, pp. 2546–2552

    2022

  5. [13]

    High-Fidelity Dy- namic Modeling and Simulation of Planetary Rovers Using Single-Input-Multi-Output Joints with Terrain Property Mapping,

    H. Yang, L. Ding, H. Gao, et al., “High-Fidelity Dy- namic Modeling and Simulation of Planetary Rovers Using Single-Input-Multi-Output Joints with Terrain Property Mapping,” IEEE Transactions on Robotics (T-RO), vol. 38, no. 5, pp. 3238–3258, Oct. 2022

    2022

  6. [14]

    Kilometer-scale autonomous navigation in subarc- tic forests: challenges and lessons learned,

    D. Baril, S.-P. Desch ˆenes, O. Gamache, et al. , “Kilometer-scale autonomous navigation in subarc- tic forests: challenges and lessons learned,” Field Robotics, vol. 2, no. 1, pp. 1628–1660, Mar. 2022

    2022

  7. [15]

    A Friction-Based Kinematic Model for Skid-Steer Wheeled Mobile Robots,

    S. Rabiee and J. Biswas, “A Friction-Based Kinematic Model for Skid-Steer Wheeled Mobile Robots,” in IEEE International Conference on Robotics and Au- tomation (ICRA), May 2019, pp. 8563–8569

    2019

  8. [16]

    Learning-Based Model Predictive Control for Autonomous Racing,

    J. Kabzan, L. Hewing, A. Liniger, and M. N. Zeilinger, “Learning-Based Model Predictive Control for Autonomous Racing,” IEEE Robotics and Automa- tion Letters (RA-L), vol. 4, no. 4, pp. 3363–3370, Oct. 2019

    2019

  9. [17]

    Autonomous Drifting with 3 Minutes of Data VOLUME , 19 Author et al.: via Learned Tire Models,

    F. Djeumou, J. Y . Goh, U. Topcu, and A. Balachan- dran, “Autonomous Drifting with 3 Minutes of Data VOLUME , 19 Author et al.: via Learned Tire Models,” in IEEE International Con- ference on Robotics and Automation (ICRA) , IEEE, May 2023, pp. 968–974

    2023

  10. [18]

    Model- and Acceleration-based Pursuit Controller for High- Performance Autonomous Racing,

    J. Becker, N. Imholz, L. Schwarzenbach, E. Ghignone, N. Baumann, and M. Magno, “Model- and Acceleration-based Pursuit Controller for High- Performance Autonomous Racing,” in IEEE Inter- national Conference on Robotics and Automation (ICRA), May 2023, pp. 5276–5283

    2023

  11. [19]

    Experimental kinematics for wheeled skid-steer mobile robots,

    A. Mandow, J. L. Martinez, J. Morales, J. L. Blanco, A. Garcia-Cerezo, and J. Gonzalez, “Experimental kinematics for wheeled skid-steer mobile robots,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2007, pp. 1222–1227

    2007

  12. [20]

    Model- based Prediction of Skid-steer Robot Kinematics Us- ing Online Estimation of Track Instantaneous Centers of Rotation,

    J. Pentzer, S. Brennan, and K. Reichard, “Model- based Prediction of Skid-steer Robot Kinematics Us- ing Online Estimation of Track Instantaneous Centers of Rotation,” Journal of Field Robotics, vol. 31, no. 3, pp. 455–476, May 2014

    2014

  13. [21]

    Slip Modeling and Estimation for a Planetary Explo- ration Rover: Experimental Results from Mt. Etna,

    K. Bussmann, L. Meyer, F. Steidle, and A. Wedler, “Slip Modeling and Estimation for a Planetary Explo- ration Rover: Experimental Results from Mt. Etna,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2018, pp. 2449–2456

    2018

  14. [22]

    Learning-based Nonlinear Model Predictive Control to Improve Vision-based Mobile Robot Path Tracking,

    C. J. Ostafew, A. P. Schoellig, T. D. Barfoot, and J. Collier, “Learning-based Nonlinear Model Predictive Control to Improve Vision-based Mobile Robot Path Tracking,” Journal of Field Robotics , vol. 33, no. 1, pp. 133–152, Jan. 2016

    2016

  15. [23]

    Analysis of Tread ICRs for Wheeled Skid-Steer Vehicles on Inclined Terrain,

    J. L. Martinez, J. Morales, J. M. Garcia, and A. Garcia-Cerezo, “Analysis of Tread ICRs for Wheeled Skid-Steer Vehicles on Inclined Terrain,” IEEE Ac- cess, vol. 11, pp. 547–555, 2023

    2023

  16. [24]

    Multimodal dynamics modeling for off-road autonomous vehicles,

    J.-F. Tremblay, T. Manderson, A. Noca, G. Dudek, and D. Meger, “Multimodal dynamics modeling for off-road autonomous vehicles,” in IEEE International Conference on Robotics and Automation (ICRA), May 2021, pp. 1796–1802

    2021

  17. [25]

    General Dynamic Model for Skid-Steer Mo- bile Manipulators with Wheel-Ground Interactions,

    S. Aguilera-Marinovic, M. Torres-Torriti, and F. Auat- Cheein, “General Dynamic Model for Skid-Steer Mo- bile Manipulators with Wheel-Ground Interactions,” IEEE/ASME Transactions on Mechatronics , vol. 22, pp. 433–444, 1 Feb. 2017

    2017

  18. [26]

    Effects of Turning Radius on Skid-Steered Wheeled Robot Power Consumption on Loose Soil,

    J.-S. Fiset, M. Effati, and K. Skonieczny, “Effects of Turning Radius on Skid-Steered Wheeled Robot Power Consumption on Loose Soil,” in Field and Ser- vice Robotics, Springer Singapore, 2021, pp. 115–129

    2021

  19. [27]

    GeRoNa: Generic Robot Navigation,

    G. Huski ´c, S. Buck, and A. Zell, “GeRoNa: Generic Robot Navigation,” Journal of Intelligent & Robotic Systems, vol. 95, no. 2, pp. 419–442, Aug. 2019

    2019

  20. [28]

    T. Han, S. Talia, R. Panicker, P. Shah, N. Jawale, and B. Boots, Dynamics Models in the Aggressive Off- Road Driving Regime , Yokoham, Japan, 2024

    2024

  21. [29]

    Samson, D

    N. Samson, D. Baril, J. L ´epine, and F. Pomerleau, Comparing Motion Distortion Between Vehicle Field Deployments, Yokoham, Japan, 2024

    2024

  22. [30]

    Model Predictive Control for Aggressive Driving Over Uneven Terrain,

    T. Han, A. Liu, A. Li, A. Spitzer, G. Shi, and B. Boots, “Model Predictive Control for Aggressive Driving Over Uneven Terrain,” ArXiv preprint, accepted for the 2024 International Conference on Robotics and Automation (ICRA), 2024

    2024

  23. [31]

    Comparing ICP variants on real-world data sets: Open-source library and experimental protocol,

    F. Pomerleau, F. Colas, R. Siegwart, and S. Mag- nenat, “Comparing ICP variants on real-world data sets: Open-source library and experimental protocol,” Autonomous Robots , vol. 34, no. 3, pp. 133–148, 2013

    2013

  24. [32]

    Inference by eye: Reading the over- lap of independent confidence intervals,

    G. Cumming, “Inference by eye: Reading the over- lap of independent confidence intervals,” Statistics in Medicine, vol. 28, pp. 205–220, 2 Jan. 2009

    2009

  25. [33]

    What’s Wrong with Risk Matrices?

    L. A. Cox Jr, “What’s Wrong with Risk Matrices?” Risk Analysis, vol. 28, no. 2, pp. 497–512, 2008

    2008

  26. [34]

    Saturation-Aware An- gular Velocity Estimation: Extending the Robustness of SLAM to Aggressive Motions,

    S.-P. Desch ˆenes, D. Baril, M. Boxan, J. Laconte, P. Gigu`ere, and F. Pomerleau, “Saturation-Aware An- gular Velocity Estimation: Extending the Robustness of SLAM to Aggressive Motions,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Yo...

    2024

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.