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arxiv: 2602.07209 · v2 · submitted 2026-02-06 · 💻 cs.RO

Continuum Robot Localization using Distributed Time-of-Flight Sensors

Spencer Teetaert , Giammarco Caroleo , Marco Pontin , Sven Lilge , Jessica Burgner-Kahrs , Timothy D. Barfoot , Perla Maiolino This is my paper

Pith reviewed 2026-05-16 06:25 UTC · model grok-4.3

classification 💻 cs.RO
keywords continuum robotlocalizationtime-of-flight sensorsensor fusionshape priorsoft robotics
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The pith

Distributed low-resolution time-of-flight sensors along a continuum robot enable localization to 2.5 cm position and 7.2° rotation error by fusing data with a shape prior.

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

This paper demonstrates a localization method for continuum robots that places several small, low-resolution ToF sensors along the robot body instead of relying on large high-resolution sensors like lidar. The technique combines the sensor readings with a prior model of the robot's possible shapes to produce usable estimates even when many individual sensors return degenerate or uninformative data. Experiments on a 53 cm robot show average errors of 2.5 cm in position and 7.2° in rotation across simulated and real environments. The result matters because it makes navigation in unknown spaces feasible for soft, deformable robots that cannot carry bulky sensing hardware.

Core claim

Fusing measurements from distributed low-resolution ToF sensors with a robot shape prior produces accurate localization of continuum robots despite frequent degenerate readings from individual sensors. The authors report an average error of 2.5 cm in position and 7.2° in rotation on a 53 cm robot and show the performance holds across multiple environments in both simulation and hardware while remaining robust to moderate errors in the prior map.

What carries the argument

Fusion of distributed low-resolution ToF distance measurements with a continuum-robot shape prior that constrains possible configurations.

If this is right

  • Localization becomes practical for continuum robots in unstructured spaces without high-resolution sensors.
  • The same fusion approach works across different environments and robot lengths tested.
  • Moderate inaccuracies in the shape prior can be tolerated without catastrophic failure.
  • Both simulation and real-world tests produce consistent error levels.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The method could be extended to joint localization and mapping if the shape prior is allowed to update online.
  • Integration with closed-loop control would let the robot use the estimates to steer itself toward targets.
  • Replacing some ToF sensors with other cheap modalities might reduce hardware cost further while preserving accuracy.

Load-bearing premise

The shape prior stays accurate enough that the fusion step can still produce good estimates when many individual ToF readings are poor or missing.

What would settle it

Running the same experiments with a deliberately inaccurate shape prior and observing position errors consistently above 5 cm would falsify the central claim.

Figures

Figures reproduced from arXiv: 2602.07209 by Giammarco Caroleo, Jessica Burgner-Kahrs, Marco Pontin, Perla Maiolino, Spencer Teetaert, Sven Lilge, Timothy D. Barfoot.

Figure 1
Figure 1. Figure 1: A continuum robot equipped with distributed sensors is shown [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Simulated jet engine environment with CR in the MuJoCo physics [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Experimental setup including the extensible continuum robot, the [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Side-by-side comparison of the simulated environment (left) and the [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Simulated scene S8 (left), prior map S0 (middle), and the recon [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Side-by-side comparison of the real environment (left) and the [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Real scenes (left), prior maps (middle), and the reconstructed scenes [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 9
Figure 9. Figure 9: Real scene (top) with detected anomalies shown over prior map [PITH_FULL_IMAGE:figures/full_fig_p009_9.png] view at source ↗
read the original abstract

Localization and mapping of an environment are crucial tasks for any robot operating in unstructured environments. Time-of-flight (ToF) sensors (e.g.,~lidar) have proven useful in mobile robotics, where high-resolution sensors can be used for simultaneous localization and mapping. In soft and continuum robotics, however, these high-resolution sensors are too large for practical use. This, combined with the deformable nature of such robots, has resulted in continuum robot (CR) localization and mapping in unstructured environments being a largely untouched area. In this work, we present a localization technique for CRs that relies on small, low-resolution ToF sensors distributed along the length of the robot. By fusing measurement information with a robot shape prior, we show that accurate localization is possible despite each sensor experiencing frequent degenerate scenarios. We achieve an average localization error of 2.5cm in position and 7.2{\deg} in rotation across all experimental conditions with a 53cm long robot. We demonstrate that the results are repeated across multiple environments, in both simulation and real-world experiments, and study robustness in the estimation to deviations in the prior map.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper proposes a localization technique for continuum robots (CRs) that fuses measurements from distributed low-resolution Time-of-Flight (ToF) sensors along the robot body with an explicit shape prior. It claims that this enables accurate localization despite frequent degenerate measurements from individual sensors, reporting an average error of 2.5 cm in position and 7.2° in rotation for a 53 cm robot. The results are stated to hold across simulation and real-world experiments in multiple environments, with a robustness study to prior-map deviations.

Significance. If the empirical claims hold under closer scrutiny, the work addresses a notable gap in CR navigation for unstructured environments by avoiding bulky high-resolution sensors such as lidar. The repeated quantitative results across sim-to-real settings and the focus on robustness to prior deviations represent a practical contribution that could support applications in medical robotics or inspection. The explicit use of a shape prior as an external input is a clear design choice that avoids circularity in the derivation.

major comments (3)
  1. [Abstract / Experiments] Abstract and Experiments section: The headline average error (2.5 cm / 7.2°) is reported across “all experimental conditions” without specifying the number of trials, variance, per-condition breakdowns, or data-exclusion rules; this makes it impossible to verify whether the result is driven by a few favorable runs or truly representative.
  2. [Method] Method section: The sensor-fusion algorithm is described at a high level but does not specify how the shape prior is encoded, how degenerate ToF measurements are detected or down-weighted, or the values (or selection procedure) for any free parameters such as fusion weights or noise covariances; without these details the reproducibility of the 2.5 cm / 7.2° figure cannot be assessed.
  3. [Experiments / Results] Experiments / Results: No quantitative characterization is supplied of (a) the distribution of per-sensor degeneracy rates, (b) the actual magnitudes of prior-map deviations tested, or (c) the divergence threshold of the filter; these omissions directly undermine the central robustness claim that the method works “despite each sensor experiencing frequent degenerate scenarios.”
minor comments (2)
  1. [Figures] Figure captions and axis labels should explicitly state units and the number of runs averaged; several plots appear to lack error bars or confidence intervals.
  2. [Notation] The notation for the shape prior and the ToF measurement model should be introduced with a single consistent symbol table to avoid ambiguity when reading the fusion equations.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the insightful and constructive comments on our manuscript. We have addressed each of the major comments point-by-point below. We agree with the need for additional details to enhance reproducibility and verifiability, and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract / Experiments] Abstract and Experiments section: The headline average error (2.5 cm / 7.2°) is reported across “all experimental conditions” without specifying the number of trials, variance, per-condition breakdowns, or data-exclusion rules; this makes it impossible to verify whether the result is driven by a few favorable runs or truly representative.

    Authors: We agree that more detailed statistical information is necessary to fully substantiate the reported average errors. In the revised manuscript, we will specify the number of trials performed, include measures of variance (such as standard deviation), provide per-condition error breakdowns, and clarify any data exclusion rules applied. This will demonstrate that the 2.5 cm position and 7.2° orientation errors are representative across the experiments. revision: yes

  2. Referee: [Method] Method section: The sensor-fusion algorithm is described at a high level but does not specify how the shape prior is encoded, how degenerate ToF measurements are detected or down-weighted, or the values (or selection procedure) for any free parameters such as fusion weights or noise covariances; without these details the reproducibility of the 2.5 cm / 7.2° figure cannot be assessed.

    Authors: We acknowledge the lack of sufficient implementation details in the current method description. The revised version will include explicit descriptions of: the encoding of the shape prior (e.g., as a B-spline or polynomial representation), the detection and down-weighting mechanism for degenerate ToF measurements (based on signal strength or range validity checks), and the specific values or selection methods for free parameters including fusion weights and noise covariances. These additions will facilitate reproducibility of the localization accuracy. revision: yes

  3. Referee: [Experiments / Results] Experiments / Results: No quantitative characterization is supplied of (a) the distribution of per-sensor degeneracy rates, (b) the actual magnitudes of prior-map deviations tested, or (c) the divergence threshold of the filter; these omissions directly undermine the central robustness claim that the method works “despite each sensor experiencing frequent degenerate scenarios.”

    Authors: We agree that quantitative characterizations are essential to support the robustness claims. In the updated manuscript, we will provide: (a) the distribution of per-sensor degeneracy rates (e.g., via histograms or average percentages), (b) the specific magnitudes of the prior-map deviations tested in the robustness study (e.g., in terms of position and orientation offsets), and (c) the divergence threshold of the filter along with its impact on the estimation. This will strengthen the evidence that the method handles frequent degeneracies effectively. revision: yes

Circularity Check

0 steps flagged

No significant circularity in the empirical localization pipeline

full rationale

The paper presents a sensor-fusion localization method for continuum robots that combines distributed low-resolution ToF readings with an external shape prior. The headline result (2.5 cm / 7.2°) is obtained directly from simulation and hardware experiments across multiple environments; no derivation chain, fitted parameter, or self-citation is invoked to produce the reported accuracy by construction. The shape prior is treated as an independent input whose deviations are studied experimentally rather than assumed or derived from the method itself.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The approach depends on a robot shape prior as an input assumption and on the fusion algorithm's ability to handle degenerate sensor data; no new entities are postulated.

free parameters (1)
  • sensor fusion weights or noise covariances
    Parameters in the estimation algorithm that combine measurements with the shape prior are expected to be tuned or fitted.
axioms (1)
  • domain assumption A sufficiently accurate shape prior for the continuum robot is available
    The method explicitly fuses measurements with a robot shape prior to overcome individual sensor degeneracy.

pith-pipeline@v0.9.0 · 5522 in / 1227 out tokens · 48187 ms · 2026-05-16T06:25:15.996462+00:00 · methodology

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Reference graph

Works this paper leans on

47 extracted references · 47 canonical work pages

  1. [1]

    Orekhov, Garrison L

    Colette Abah, Andrew L. Orekhov, Garrison L. H. John- ston, and Nabil Simaan. A multi-modal sensor array for human–robot interaction and confined spaces exploration using continuum robots.IEEE Sensors Journal, 22(4): 3585–3594, 2022. doi: 10.1109/JSEN.2021.3140002

    work page doi:10.1109/jsen.2021.3140002 2022

  2. [2]

    Blade inspection toolkit

    GE Aerospace. Blade inspection toolkit. URL https://www.geaerospace.com/commercial/services/ engine-maintenance-technologies/blade-inspection-tool

    work page

  3. [3]

    Cambridge University Press, 2017

    T D Barfoot.State Estimation for Robotics. Cambridge University Press, 2017. ISBN 9781107159396. doi: 10. 1017/9781316671528

    work page 2017

  4. [4]

    Borgstadt, Michael R

    Justin A. Borgstadt, Michael R. Zinn, and Nicola J. Ferrier. Multi-modal localization algorithm for catheter interventions. In2015 IEEE International Conference on Robotics and Automation (ICRA), pages 5350–5357,

    work page

  5. [5]

    doi: 10.1109/ICRA.2015.7139946

    work page doi:10.1109/icra.2015.7139946 2015

  6. [6]

    Caleb Rucker, and Howie Choset

    Jessica Burgner-Kahrs, D. Caleb Rucker, and Howie Choset. Continuum robots for medical applications: A survey.IEEE Transactions on Robotics, 31(6):1261– 1280, 2015. doi: 10.1109/TRO.2015.2489500

    work page doi:10.1109/tro.2015.2489500 2015

  7. [7]

    Schoellig, and Timothy D

    Keenan Burnett, Angela P. Schoellig, and Timothy D. Barfoot. Continuous-time radar-inertial and lidar-inertial odometry using a gaussian process motion prior.IEEE Transactions on Robotics, 41:1059–1076, 2025. doi: 10. 1109/TRO.2024.3521856

    work page arXiv 2025

  8. [8]

    Soft robot localization using distributed minia- turized time-of-flight sensors

    Giammarco Caroleo, Alessandro Albini, and Perla Maiolino. Soft robot localization using distributed minia- turized time-of-flight sensors. In2025 IEEE 8th Interna- tional Conference on Soft Robotics (RoboSoft), pages 1–

    work page

  9. [9]

    In2025 IEEE 8th International Conference on Soft Robotics (RoboSoft)

    doi: 10.1109/RoboSoft63089.2025.11020858. ISSN: 2769-4534

    work page doi:10.1109/robosoft63089.2025.11020858 2025

  10. [10]

    Barfoot, and Perla Maiolino

    Giammarco Caroleo, Alessandro Albini, Daniele De Martini, Timothy D. Barfoot, and Perla Maiolino. Tiny lidars for manipulator self-awareness: Sensor character- ization and initial localization experiments, 2025. URL https://arxiv.org/abs/2503.03449

    work page arXiv 2025

  11. [11]

    3d printable jet engine

    CATIA V5FTW. “3d printable jet engine” [3d model]. https://www.printables.com/model/ 572421-3d-printable-jet-engine, 2025. Adapted by the authors. Original work licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)

    work page 2025

  12. [12]

    Development of a continuum robot with inflatable stiffness-adjustable elements for in-situ repair of aeroengines.Robotics and Computer-Integrated Manufacturing, 95:103018, 2025

    Jung-Che Chang, Hengtai Dai, Xi Wang, Dragos Axinte, and Xin Dong. Development of a continuum robot with inflatable stiffness-adjustable elements for in-situ repair of aeroengines.Robotics and Computer-Integrated Manufacturing, 95:103018, 2025. ISSN 0736-5845. doi: https://doi.org/10.1016/j.rcim.2025.103018

    work page doi:10.1016/j.rcim.2025.103018 2025

  13. [13]

    Demantk ´e, C

    J. Demantk ´e, C. Mallet, N. David, and B. Vallet. Di- mensionality based scale selection in 3d lidar point clouds.The International Archives of the Photogramme- try, Remote Sensing and Spatial Information Sciences, XXXVIII-5/W12:97–102, 2011

    work page 2011

  14. [14]

    X. Dong, D. Axinte, D. Palmer, S. Cobos, M. Raffles, A. Rabani, and J. Kell. Development of a slender con- tinuum robotic system for on-wing inspection/repair of gas turbine engines.Robotics and Computer-Integrated Manufacturing, 44:218–229, 2017. ISSN 0736-5845. doi: https://doi.org/10.1016/j.rcim.2016.09.004

    work page doi:10.1016/j.rcim.2016.09.004 2017

  15. [15]

    Dupont, Nabil Simaan, Howie Choset, and Caleb Rucker

    Pierre E. Dupont, Nabil Simaan, Howie Choset, and Caleb Rucker. Continuum robots for medical interven- tions.Proceedings of the IEEE, 110(7):847–870, 2022. doi: 10.1109/JPROC.2022.3141338

    work page doi:10.1109/jproc.2022.3141338 2022

  16. [16]

    Ferguson, D

    James M. Ferguson, D. Caleb Rucker, and Robert J. Webster. Unified shape and external load state estimation for continuum robots.IEEE Transactions on Robotics, 40:1813–1827, 2024. doi: 10.1109/TRO.2024.3360950

    work page doi:10.1109/tro.2024.3360950 2024

  17. [17]

    Franz, Tam ´as Haidegger, Wolfgang Birkfell- ner, Kevin Cleary, Terry M

    Alfred M. Franz, Tam ´as Haidegger, Wolfgang Birkfell- ner, Kevin Cleary, Terry M. Peters, and Lena Maier- Hein. Electromagnetic tracking in medicine—a review of technology, validation, and applications.IEEE Transac- tions on Medical Imaging, 33(8):1702–1725, 2014. doi: 10.1109/TMI.2014.2321777

    work page doi:10.1109/tmi.2014.2321777 2014

  18. [18]

    Kudryavtsev, Patrick Rougeot, Pierre Renaud, Kanty Rabenorosoa, and Brahim Tamadazte

    Cedric Girerd, Andrey V . Kudryavtsev, Patrick Rougeot, Pierre Renaud, Kanty Rabenorosoa, and Brahim Tamadazte. Slam-based follow-the-leader deployment of concentric tube robots.IEEE Robotics and Automation Letters, 5(2):548–555, 2020. doi: 10.1109/LRA.2019. 2963821

    work page doi:10.1109/lra.2019 2020

  19. [19]

    Holland and Roy E

    Paul W. Holland and Roy E. Welsch. Robust regression using iteratively reweighted least-squares.Communica- tions in Statistics - Theory and Methods, 6(9):813–827,

    work page

  20. [20]

    doi: 10.1080/03610927708827533

    work page doi:10.1080/03610927708827533

  21. [21]

    Kengo Iwao, Hikaru Arita, and Kenji Tahara. State estimation and environment recognition for articulated structures via proximity sensors distributed over the whole body.IEEE Robotics and Automation Letters, 10 (3):3030–3037, 2025. doi: 10.1109/LRA.2025.3539117

    work page doi:10.1109/lra.2025.3539117 2025

  22. [22]

    Park, and Pierre E

    Beobkyoon Kim, Junhyoung Ha, Frank C. Park, and Pierre E. Dupont. Optimizing curvature sensor placement for fast, accurate shape sensing of continuum robots. In2014 IEEE International Conference on Robotics and Automation (ICRA), pages 5374–5379, 2014. doi: 10.1109/ICRA.2014.6907649

    work page doi:10.1109/icra.2014.6907649 2014

  23. [23]

    An ultrasound-based localiza- tion algorithm for catheter ablation guidance in the left atrium.The International Journal of Robotics Research, 29(6):643–665, 2010

    Aditya Brij Koolwal, Federico Barbagli, Christopher Carlson, and David Liang. An ultrasound-based localiza- tion algorithm for catheter ablation guidance in the left atrium.The International Journal of Robotics Research, 29(6):643–665, 2010. doi: 10.1177/0278364909105332

    work page doi:10.1177/0278364909105332 2010

  24. [24]

    Barfoot, and Jessica Burgner- Kahrs

    Sven Lilge, Timothy D. Barfoot, and Jessica Burgner- Kahrs. Continuum robot state estimation using gaussian process regression on se(3).The International Journal of Robotics Research, 41(13-14):1099–1120, 2022. doi: 10.1177/02783649221128843

    work page doi:10.1177/02783649221128843 2022

  25. [25]

    where to put your sensors and how to use them

    Arthur W. Mahoney, Trevor L. Bruns, Philip J. Swaney, and Robert J. Webster. On the inseparable nature of sensor selection, sensor placement, and state estimation for continuum robots or “where to put your sensors and how to use them”. In2016 IEEE International Conference on Robotics and Automation (ICRA), pages 4472–4478. doi: 10.1109/ICRA.2016.7487646

    work page doi:10.1109/icra.2016.7487646 2016

  26. [26]

    How to model Tendon-Driven continuum robots and benchmark modelling performance.Front Robot AI, 7:630245, February 2021

    Priyanka Rao, Quentin Peyron, Sven Lilge, and Jessica Burgner-Kahrs. How to model Tendon-Driven continuum robots and benchmark modelling performance.Front Robot AI, 7:630245, February 2021

    work page 2021

  27. [27]

    Goldman, Andrea Bajo, Kon- stantinos Iliopoulos, Nabil Simaan, and Peter K

    Austin Reiter, Roger E. Goldman, Andrea Bajo, Kon- stantinos Iliopoulos, Nabil Simaan, and Peter K. Allen. A learning algorithm for visual pose estimation of contin- uum robots. In2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 2390–2396,

    work page

  28. [28]

    doi: 10.1109/IROS.2011.6094947

    work page doi:10.1109/iros.2011.6094947 2011

  29. [29]

    Rone and Pinhas Ben-Tzvi

    William S. Rone and Pinhas Ben-Tzvi. Multi-segment continuum robot shape estimation using passive cable displacement. In2013 IEEE International Symposium on Robotic and Sensors Environments (ROSE), pages 37–42,

    work page

  30. [30]

    doi: 10.1109/ROSE.2013.6698415

    work page doi:10.1109/rose.2013.6698415 2013

  31. [31]

    Walker, Christos Bergeles, Kai Xu, and Dragos A

    Matteo Russo, Seyed Mohammad Hadi Sadati, Xin Dong, Abdelkhalick Mohammad, Ian D. Walker, Christos Bergeles, Kai Xu, and Dragos A. Axinte. Continuum robots: An overview.Advanced Intelligent Systems, 5(5):2200367, 2023. doi: https://doi.org/10.1002/aisy. 202200367

    work page doi:10.1002/aisy 2023

  32. [32]

    Moss: Monocular shape sensing for continuum robots

    Chengnan Shentu, Enxu Li, Chaojun Chen, Puspita T Dewi, David B Lindell, and Jessica Burgner-Kahrs. Moss: Monocular shape sensing for continuum robots. IEEE Robotics and Automation Letters, 9(2):1524–1531, 2023

    work page 2023

  33. [33]

    Shape sensing techniques for continuum robots in minimally invasive surgery: A survey.IEEE Transactions on Biomedical Engineering, 64(8):1665– 1678, 2017

    Chaoyang Shi, Xiongbiao Luo, Peng Qi, Tianliang Li, Shuang Song, Zoran Najdovski, Toshio Fukuda, and Hongliang Ren. Shape sensing techniques for continuum robots in minimally invasive surgery: A survey.IEEE Transactions on Biomedical Engineering, 64(8):1665– 1678, 2017. doi: 10.1109/TBME.2016.2622361

    work page doi:10.1109/tbme.2016.2622361 2017

  34. [34]

    Sensing of continuum robots: A review.Sensors, 24(4), 2024

    Peter Jan Sincak, Erik Prada, ˇLubica Mikov ´a, Roman Mykhailyshyn, Martin Varga, Tomas Merva, and Ivan Virgala. Sensing of continuum robots: A review.Sensors, 24(4), 2024. ISSN 1424-8220. doi: 10.3390/s24041311

    work page doi:10.3390/s24041311 2024

  35. [35]

    Multi-robot task planning under individual and collaborative temporal logic specifications

    Christian Sorensen, Phillip Hyatt, Matthew Ricks, Seth Nielsen, and Marc D. Killpack. Soft robot configuration estimation and control using simultaneous localization and mapping. In2021 IEEE/RSJ International Confer- ence on Intelligent Robots and Systems (IROS), pages 616–623, 2021. doi: 10.1109/IROS51168.2021.9635896

    work page doi:10.1109/iros51168.2021.9635896 2021

  36. [36]

    Cr-scan raptor 3d scanner,

    Creality Store. Cr-scan raptor 3d scanner,

    work page

  37. [37]

    URL https://store.creality.com/uk/products/ cr-scan-raptor-3d-scanner

    work page

  38. [38]

    Spencer Teetaert, Sven Lilge, Jessica Burgner-Kahrs, and Timothy D. Barfoot. A stochastic framework for continuous-time state estimation of continuum robots

    work page

  39. [39]

    URL https://arxiv.org/abs/2510.01381

    work page arXiv

  40. [40]

    Spencer Teetaert, Sven Lilge, Jessica Burgner-Kahrs, and Timothy D. Barfoot. A sliding-window filter for online continuous-time continuum robot state estimation. 2025. URL https://arxiv.org/abs/2510.26623

    work page arXiv 2025

  41. [41]

    Mujoco: A physics en- gine for model-based control, in: 2012 IEEE/RSJ International Con- ference on Intelligent Robots and Systems, IEEE

    Emanuel Todorov, Tom Erez, and Yuval Tassa. Mu- joco: A physics engine for model-based control. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 5026–5033. IEEE, 2012. doi: 10.1109/IROS.2012.6386109

    work page doi:10.1109/iros.2012.6386109 2012

  42. [42]

    T. S. Vaquero, G. Daddi, R. Thakker, M. Paton, A. Jasour, M. P. Strub, R. M. Swan, R. Royce, M. Gildner, P. Tosi, M. Veismann, P. Gavrilov, E. Marteau, J. Bowkett, D. Loret de Mola Lemus, Y . Nakka, B. Hockman, A. Orekhov, T. D. Hasseler, C. Leake, B. Nuernberger, P. Proenc ¸a, W. Reid, W. Talbot, N. Georgiev, T. Paileva- nian, A. Archanian, E. Ambrose, J...

    work page doi:10.1126/scirobotics.adh8332 2024

  43. [43]

    Learning-based visual-strain fusion for eye-in-hand continuum robot pose estimation and control.IEEE Transactions on Robotics, 39(3):2448– 2467, 2023

    Xiaomei Wang, Jing Dai, Hon-Sing Tong, Kui Wang, Ge Fang, Xiaochen Xie, Yun-Hui Liu, Kwok Wai Samuel Au, and Ka-Wai Kwok. Learning-based visual-strain fusion for eye-in-hand continuum robot pose estimation and control.IEEE Transactions on Robotics, 39(3):2448– 2467, 2023. doi: 10.1109/TRO.2023.3240556

    work page doi:10.1109/tro.2023.3240556 2023

  44. [44]

    Yaming Wang, Feng Ju, Yahui Yun, Jiafeng Yao, Yaoyao Wang, Hao Guo, and Bai Chen. An inspection continuum robot with tactile sensor based on electrical impedance tomography for exploration and navigation in unknown environment.The Industrial Robot, 47(1):121–130, 2020

    work page 2020

  45. [45]

    Approximate convex decomposition for 3d meshes with collision-aware concavity and tree search.ACM Trans- actions on Graphics (TOG), 41(4):1–18, 2022

    Xinyue Wei, Minghua Liu, Zhan Ling, and Hao Su. Approximate convex decomposition for 3d meshes with collision-aware concavity and tree search.ACM Trans- actions on Graphics (TOG), 41(4):1–18, 2022

    work page 2022

  46. [46]

    Development of a continuum robot enhanced with distributed sensors for search and rescue.ROBOMECH J., 9(1), December 2022

    Yu Yamauchi, Yuichi Ambe, Hikaru Nagano, Masashi Konyo, Yoshiaki Bando, Eisuke Ito, Solvi Arnold, Kim- itoshi Yamazaki, Katsutoshi Itoyama, Takayuki Okatani, Hiroshi G Okuno, and Satoshi Tadokoro. Development of a continuum robot enhanced with distributed sensors for search and rescue.ROBOMECH J., 9(1), December 2022

    work page 2022

  47. [47]

    A survey on design, actuation, modeling, and control of continuum robot.Cyborg and Bionic Systems, 2022:9754697, 2022

    Jingyu Zhang, Qin Fang, Pingyu Xiang, Danying Sun, Yanan Xue, Rui Jin, Ke Qiu, Rong Xiong, Yue Wang, and Haojian Lu. A survey on design, actuation, modeling, and control of continuum robot.Cyborg and Bionic Systems, 2022:9754697, 2022. doi: 10.34133/2022/9754697

    work page doi:10.34133/2022/9754697 2022