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REVIEW 3 major objections 5 minor 30 references

A Flexible FBG-Based Contact Force Sensor for Robotic Gripping Systems

T0 review · 3 major / 5 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read A compact dual-grating fiber-optic sensor measures contact force on soft grippers with 0.12–0.14 N RMSE and cancels temperature drift.

desk verdict Genuinely new sensor geometry with honest experiments, but the temperature-compensation claim is only proven under uniform temperature. read the letter →

arxiv 2502.03914 v1 pith:DBT2SJWZ submitted 2025-02-06 cs.RO cs.SYeess.SY

Wenjie Lai , Huu Duoc Nguyen , Jiajun Liu , Xingyu Chen , Soo Jay Phee This is my paper · ORCID
classification cs.ROcs.SYeess.SY
keywords fiberBragggratingcontactforcesensingsoftroboticgrippertemperaturecompensationclosed-loopcontrolslippreventiontactile
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 reports a compact fiber-optic force sensor for soft robotic grippers and argues that it provides accurate, temperature-stable contact-force readings. The sensing element is a dual Fiber Bragg Grating (FBG) array: one grating is deformed by pressing a bump-and-uvula TPU casing, and the second, strain-free grating measures temperature alone so its signal can be subtracted. Bench tests give a sensitivity near 1169 pm/N, a force RMSE of 0.12 N against the calibration curve and 0.14 N against a commercial load cell, and a temperature-compensated force RMSE of 0.01 N over an 11 °C change. Integrated with a soft grow-and-twine gripper, the sensor's feedback let a PID controller hold contact force during pick-and-place, preventing the slips that occurred without control. The paper is establishing that this packaging solves the usual trade-off among sensitivity, fiber protection, and thermal drift for gripper force feedback.

What carries the argument

The load-bearing mechanism is the dual-FBG array with asymmetric packaging. A Fiber Bragg Grating is a periodic refractive-index modulation in an optical fiber whose reflected wavelength shifts with strain and temperature; here FBG1 is suspended in a slot beneath the uvula, so pressing the external bump bends the fiber and changes its Bragg wavelength, while FBG2 sits strain-free in a protective tube and responds only to temperature. The compensation identity is $\Delta\lambda_{B,1} = K_{\varepsilon 1}\Delta\varepsilon_1 + K_{T1}\Delta T_1$ with $\Delta T_1 = \Delta T_2$, so the contact force is recovered from the two wavelength shifts through $N = A(\Delta\lambda_{B,1} - r\Delta\lambda_{B,2})^2 + B(\Delta\lambda_{B,1} - r\Delta\lambda_{B,2}) + C$. All reported performance numbers follow from this paired-grating subtraction plus the second-order calibration.

What would settle it

Run the sensor with FBG1 heated by a warm object at the contact bump while FBG2 stays at room temperature, keeping the mechanical load constant; if the compensated force output drifts by more than the reported 0.01 N RMSE or tracks the temperature difference, the equal-temperature assumption is violated and the compensation claim fails.

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Extended reading notes

Core claim

The central discovery is that suspending an optical fiber beneath a 3D-printed TPU bump-and-uvula structure lets a single FBG measure transverse contact force directly, rather than relying on longitudinal stretching of a bonded fiber. A second FBG, glued strain-free inside a protective Hytrel tube, serves as a temperature reference; because the two gratings are 10 mm apart, the paper assumes they see the same temperature change and removes the thermal term using an experimentally measured sensitivity ratio r = KT1/KT2 = 2.356. The relation between applied force and the temperature-corrected wavelength shift is fitted as a second-order polynomial, reflecting the nonlinear bending-and-stretching of a beam fixed at both ends and loaded at the center. The paper claims this sensor is repeatable over 0–4.69 N with 4.83% maximum hysteresis, and that closed-loop PID grip control using its readings kept a 351 g crimper, a 419 g hammer, and a 222 g bottle from slipping during automated transfer, while uncontrolled grasps dropped all three.

Load-bearing premise

The compensation assumes that FBG1 and FBG2 experience identical temperature changes at all times, even though they are 10 mm apart and housed in different structures, and the validation heated both together in a water bath rather than under the asymmetric heating of a real grip.

Editorial extensions

If this is right

  • A 12×12×4 mm FBG sensor can close the force-control loop on a soft gripper without exposed fiber, reducing fragility concerns.
  • Without feedback all three test objects slipped during transfer; with feedback, none slipped, so force feedback from this sensor directly improves grasp retention.
  • Temperature compensation cuts thermal force error from 0.23 N RMSE to 0.01 N RMSE over an 11 °C change, making the sensor usable outside temperature-stable lab conditions.
  • The calibration is repeatable enough (R² = 0.99) for a force feedback signal, with about 0.86 mN resolution given the interrogator's 1 pm resolution.
  • The design's small size and protective tube point toward arrays and multimodal vision-plus-force systems for harvesting, logistics, and prosthetics, as the paper's conclusion sketches.

Reading between the lines

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

  • Extension, not paper claim: because the equal-temperature assumption is untested under the thermal asymmetry of real contact, a field deployment would need either a third grating to measure the gradient or calibration of r against temperature difference.
  • Extension, not paper claim: the sensor's roughly 0.86 mN resolution and fast FBG response imply it could detect incipient slip events before gross slip, but the paper only demonstrates post-slip force recovery, not prediction.
  • Extension, not paper claim: the 4.69 N range and 4.83% hysteresis bound the usefulness for precision assembly; stiffer casing materials or larger chamber heights could extend the range at the cost of sensitivity, a trade-off the paper notes only as future work.
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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 / 5 minor

Summary. The paper presents a compact fiber Bragg grating (FBG) based contact force sensor intended for robotic gripping systems. The sensor comprises a 3D-printed TPU casing with a bump and uvula structure, a dual FBG array, and a protective tube. FBG1 is strain-sensitive and located beneath the uvula, while FBG2 is strain-free and serves as a temperature reference. A second-order polynomial is used to calibrate wavelength shift to force, and a temperature compensation model uses the measured temperature sensitivity ratio between the two FBGs. The authors characterize the sensor through repeatability, hysteresis, calibration, load-cell comparison, and temperature compensation experiments. They then integrate the sensor with a soft grow-and-twine gripper on a UR5e arm and demonstrate that closed-loop PID force control improves object retention during pick-and-place tasks relative to open-loop grasping.

Significance. If the reported performance holds, the sensor offers a useful combination of compactness, high sensitivity (approximately 1169 pm/N), and reasonable accuracy (force RMSE 0.12 N against calibration and 0.14 N against a commercial load cell) for soft gripper force feedback. The experimental protocol is generally sound, with separate calibration and validation runs, explicit hysteresis and repeatability measurements, and a temperature compensation test. The integration demonstration is a clear strength: it provides a concrete application and shows that the sensor enables closed-loop force regulation that prevents object slippage. The main concerns are the incompleteness of the central derivation (missing equations) and the limited validation of the temperature compensation assumption under realistic thermal gradients. These are load-bearing issues for the paper's headline claims, but they are addressable with additional exposition or experiments.

major comments (3)
  1. [Section III, Eqs. (2)-(8)] In the submitted manuscript, the equations in Section III are blank placeholders; the actual formulas for wavelength shift, temperature compensation, and the force calculation are missing. This prevents the reader from verifying the derivation of Eq. (8), which is the core of the sensor's force measurement. The authors must include the full equations with all symbols defined. This is a load-bearing issue because the entire signal-processing chain rests on these expressions.
  2. [Section IV.B, temperature compensation test] The compensation model (Eq. 5) assumes ΔT1 = ΔT2, i.e., that the strain-sensing FBG1 and the strain-free FBG2 experience identical temperature changes. The validation test in Section IV.B submerges the entire sensor in a uniformly heated water bath, so both FBGs are at the same temperature by construction. No test is provided with differential heating between FBG1 in the TPU chamber and FBG2 in the protective tube, nor is the effect of local heating at the contact point examined. Consequently, the claim of effective temperature compensation is only supported for uniform temperature changes; its validity under realistic thermal gradients, where the two gratings could be at different temperatures, remains unverified. The authors should either add an experiment with localized heating or temper the claim and explicitly discuss this limitation.
  3. [Section IV, force characterization] Only a single prototype is tested, and the force sensor is characterized exclusively under normal loading applied to the bump. The slot design is stated to 'minimize interference from small forces along the x- and z-axes,' but no off-axis or shear force data are reported. Since the sensor is mounted on a twining gripper where contact geometry and loading direction vary, the absence of off-axis characterization limits confidence in the force readings during actual grasping. This should be acknowledged as a limitation, and ideally quantified with at least a preliminary off-axis test.
minor comments (5)
  1. [Section IV.B, figure references] The figure numbering is inconsistent: the text repeatedly refers to Fig. 5a, 5b, and 5c for repeatability, hysteresis, and comparison results, but the captions show Fig. 4a-c for those results and Fig. 5a-b for the temperature tests. Similarly, the temperature results are referred to as Fig. 6a and 6b while the captions indicate Fig. 5. Please renumber the figures and cross-references consistently.
  2. [Section IV.B, percentage error statement] The sentence 'This corresponds to a force measurement percentage error of approximately 2.56%' does not specify the denominator (full-scale, reading, or average). Please clarify how the percentage is computed.
  3. [Section IV.B, wording] The phrase 'the force sensor underwent a temperature change of 11 °C during this temperature compensation test' is clear, but later the text says the compensated force 'fluctuated around the saturated value of 0.1 N'; 'saturated' is not an appropriate term here. Consider replacing it with 'the value set by the tape tension' or 'the reference value'.
  4. [Abstract and Section IV.B, precision] The reported sensitivity, 1169.04 pm/N, is given with four significant decimals while the measurement error is on the order of 0.1 N. Please round to a physically meaningful precision, e.g., 1169 pm/N.
  5. [Section V, PID gains] The PID gains are listed as Kp = 20, Ki = 0.05, and Kd = 0 without units. Since the controlled variable is force (in N) and the actuation is motor position or current, the units matter for reproducibility. Please specify the units or state that the gains are dimensionless.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: calibration and temperature-compensation claims are supported by independent experimental protocols rather than by construction.

full rationale

The derivation chain is self-contained. The FBG sensing equations (1)-(2) are standard physics cited to independent literature, and the second-order force model (Eq. 6) is an empirical fit whose coefficients A, B, C are obtained from calibration data against a load cell, not from the target claims. The reported force RMSE of 0.12 N is the calibration fit residual, and the 0.14 N RMSE comes from three additional cyclic tests against the same commercial load cell, so the comparison does not reduce by construction to the fitted parameters. Temperature compensation uses separately measured sensitivities KT1 and KT2 and their ratio r, and the compensation effectiveness (RMSE 0.01 N over 11 °C) is validated in a distinct water-bath protocol; it is an experimental outcome, not an algebraic identity forced by the model. The paper does state the explicit assumption ΔT1 = ΔT2 in Section III (Eq. 5), and the validation only covers uniform temperature in a water bath, which is a genuine limitation for nonuniform thermal conditions in real gripping. However, that is a validity concern, not circularity: the assumption is stated, the compensation test obeys it, and no parameter is defined in terms of the claimed result. Self-citations [17], [18], [20], [21] provide prior hardware and standard formulations; they are not load-bearing for the central accuracy claims and are not invoked to forbid alternatives. Thus the paper's predictions are not equivalent to its inputs by construction.

Assumptions & free parameters 4 free parameters · 6 assumptions · 0 invented entities

The central claim rests on a fitted calibration model, an assumed equality of temperature changes between the two FBGs, and an untested decoupling of off-axis forces. No new physical entities are introduced.

free parameters (4)
  • Calibration coefficients A, B, C = A=144.99, B=527.62, C=-91.42
    Fitted to the force calibration data as a second-order polynomial (Eq. 7). These coefficients define the force-wavelength mapping.
  • Temperature sensitivity ratio r = 2.356
    Derived as KT1/KT2 from temperature calibration (KT1=24.29 pm/°C, KT2=10.31 pm/°C). Used in the temperature compensation formula.
  • PID gains = Kp=20, Ki=0.05, Kd=0
    Experimentally determined to achieve 90% of desired contact force within 5 s in the closed-loop control demonstration.
  • Force setpoints for pick-and-place = 1.6 N (hammer), 0.8 N (shampoo), 0.6 N (crimper)
    Chosen based on force balance considering weight, friction, and number of contact points. These values are not derived from a general model.
assumptions (6)
  • standard math Bragg wavelength equation λB = 2 neff Λ governs the sensing principle.
    Standard FBG physics used to relate wavelength shift to strain and temperature. Invoked in Section III.
  • domain assumption The temperature change experienced by FBG1 and FBG2 is equal, ΔT1 = ΔT2 (Eq. 5).
    Invoked to derive the temperature compensation formula. Only validated in a uniform water bath, not in differential thermal conditions.
  • domain assumption The force-wavelength relationship is a second-order polynomial (Eq. 6), motivated by large-deflection cantilever behavior.
    Empirical choice; coefficients are fitted, and no mechanistically derived model is presented.
  • domain assumption FBG wavelength shift is linear in temperature over 25-45 °C.
    Temperature calibration is fit with a line (R^2=0.99 and 1.00), but there is no discussion of nonlinearity outside this range.
  • domain assumption The applied force from the linear actuator is transmitted entirely through the bump to the uvula, with negligible off-axis contributions.
    The slot is intended to decouple x/z forces, but no cross-sensitivity measurement is reported.
  • domain assumption The commercial load cell provides accurate ground truth.
    Used as reference for calibration and comparison; no uncertainty specified for the load cell.

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Pith. "Pith review of A Flexible FBG-Based Contact Force Sensor for Robotic Gripping Systems." pith.science (2026). https://pith.science/paper/DBT2SJWZ

@misc{pith2026250203914,
  author       = {Pith},
  title        = {Pith review of: A Flexible FBG-Based Contact Force Sensor for Robotic Gripping Systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DBT2SJWZ}},
  note         = {Machine review of arXiv:2502.03914}
}
read the original abstract

Soft robotic grippers demonstrate great potential for gently and safely handling objects; however, their full potential for executing precise and secure grasping has been limited by the lack of integrated sensors, leading to problems such as slippage and excessive force exertion. To address this challenge, we present a small and highly sensitive Fiber Bragg Grating-based force sensor designed for accurate contact force measurement. The flexible force sensor comprises a 3D-printed TPU casing with a small bump and uvula structure, a dual FBG array, and a protective tube. A series of tests have been conducted to evaluate the effectiveness of the proposed force sensor, including force calibration, repeatability test, hysteresis study, force measurement comparison, and temperature calibration and compensation tests. The results demonstrated good repeatability, with a force measurement range of 4.69 N, a high sensitivity of approximately 1169.04 pm/N, a root mean square error (RMSE) of 0.12 N, and a maximum hysteresis of 4.83%. When compared to a commercial load cell, the sensor showed a percentage error of 2.56% and an RMSE of 0.14 N. Besides, the proposed sensor validated its temperature compensation effectiveness, with a force RMSE of 0.01 N over a temperature change of 11 Celsius degree. The sensor was integrated with a soft grow-and-twine gripper to monitor interaction forces between different objects and the robotic gripper. Closed-loop force control was applied during automated pick-and-place tasks and significantly improved gripping stability, as demonstrated in tests. This force sensor can be used across manufacturing, agriculture, healthcare (like prosthetic hands), logistics, and packaging, to provide situation awareness and higher operational efficiency.

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Works this paper leans on

30 extracted references · 28 canonical work pages

  1. [1]

    Nature’s Blueprint in Bioinspired Materials for Robotics,

    Y. Roh et al., “Nature’s Blueprint in Bioinspired Materials for Robotics,” Advanced Functional Materials, vol. 34, no. 35, p. 2306079, 2024, doi: 10.1002/adfm.202306079

    work page doi:10.1002/adfm.202306079 2024

  2. [2]

    Bio-Inspired Soft Grippers Based on Impactive Gripping,

    L. Zhou, L. Ren, Y. Chen, S. Niu, Z. Han, and L. Ren, “Bio-Inspired Soft Grippers Based on Impactive Gripping,” Advanced Science, vol. 8, no. 9, 2021, doi: 10.1002/advs.202002017

    work page doi:10.1002/advs.202002017 2021

  3. [3]

    Slip Detection for Grasp Stabilization With a Multifingered Tactile Robot Hand,

    J. W. James and N. F. Lepora, “Slip Detection for Grasp Stabilization With a Multifingered Tactile Robot Hand,” IEEE Transactions on Robotics, vol. 37, no. 2, pp. 506–519, Apr. 2021, doi: 10.1109/TRO.2020.3031245

    work page arXiv 2021

  4. [4]

    Multipoint Contact Detection for Rigid-Soft Finger Without Tactile Sensor, Using Domain Adaption Combined Network,

    R. Zhen, L. Jiang, and M. Cheng, “Multipoint Contact Detection for Rigid-Soft Finger Without Tactile Sensor, Using Domain Adaption Combined Network,” IEEE Transactions on Industrial Electronics, vol. 71, no. 9, pp. 11083–11092, Sep. 2024, doi: 10.1109/TIE.2023.3342310

    work page arXiv 2024

  5. [5]

    Designing a Contact Fingertip Sensor Made Using a Soft 3D Printing Technique,

    A. Ibarra, B. Darbois-Texier, and F. Melo, “Designing a Contact Fingertip Sensor Made Using a Soft 3D Printing Technique,” Soft Robotics, vol. 9, no. 6, pp. 1210–1219, Dec. 2022, doi: 10.1089/soro.2021.0128

    work page arXiv 2022

  6. [6]

    Ultrasensitive Touch Sensor for Simultaneous Tactile and Slip Sensing,

    Y. Liu, J. Tao, Y. Mo, R. Bao, and C. Pan, “Ultrasensitive Touch Sensor for Simultaneous Tactile and Slip Sensing,” Advanced Materials, vol. 36, no. 21, p. 2313857, 2024, doi: 10.1002/adma.202313857

    work page doi:10.1002/adma.202313857 2024

  7. [7]

    Artificial Cutaneous Sensing of Object Slippage using Soft Robotics with Closed-Loop Feedback Process,

    T. Sekine et al., “Artificial Cutaneous Sensing of Object Slippage using Soft Robotics with Closed-Loop Feedback Process,” Small Science, vol. 1, no. 3, p. 2100002, 2021, doi: 10.1002/smsc.202100002

    work page doi:10.1002/smsc.202100002 2021

  8. [8]

    Anti-Slipping Adaptive Grasping Control with a Novel Optoelectronic Soft Sensor,

    M. S. Han, D. O. Popa, and C. K. Harnett, “Anti-Slipping Adaptive Grasping Control with a Novel Optoelectronic Soft Sensor,” in 2023 IEEE International Conference on Soft Robotics (RoboSoft), Apr. 2023, pp. 1–6. doi: 10.1109/RoboSoft55895.2023.10122010

    work page arXiv 2023

Show all 30 references
  1. [9]

    Soft Fingertips With Tactile Sensing and Active Deformation for Robust Grasping of Delicate Objects,

    L. He, Q. Lu, S.-A. Abad, N. Rojas, and T. Nanayakkara, “Soft Fingertips With Tactile Sensing and Active Deformation for Robust Grasping of Delicate Objects,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 2714–2721, Apr. 2020, doi: 10.1109/LRA.2020.2972851

    2020

  2. [10]

    A Soft Barometric Tactile Sensor to Simultaneously Localize Contact and Estimate Normal Force With Validation to Detect Slip in a Robotic Gripper,

    T. De Clercq, A. Sianov, and G. Crevecoeur, “A Soft Barometric Tactile Sensor to Simultaneously Localize Contact and Estimate Normal Force With Validation to Detect Slip in a Robotic Gripper,” IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 11767–11774, Oct. 2022, doi...

    2022

  3. [11]

    Proprioceptive Soft Pneumatic Gripper for Extreme Environments Using Hybrid Optical Fibers,

    B. Jamil, G. Yoo, Y. Choi, and H. Rodrigue, “Proprioceptive Soft Pneumatic Gripper for Extreme Environments Using Hybrid Optical Fibers,” IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 8694–8701, 2021, doi: 10.1109/LRA.2021.3111038

    2021

  4. [12]

    Multi-Degree-of-Freedom Force Sensor Incorporated into Soft Robotic Gripper for Improved Grasping Stability,

    H. Mun, D. S. Diaz Cortes, J.-H. Youn, and K.-U. Kyung, “Multi-Degree-of-Freedom Force Sensor Incorporated into Soft Robotic Gripper for Improved Grasping Stability,” Soft Robotics, vol. 11, no. 4, pp. 628–638, Aug. 2024, doi: 10.1089/soro.2023.0068

    2024

  5. [13]

    Recent Progress in Advanced Tactile Sensing Technologies for Soft Grippers,

    J. Qu et al., “Recent Progress in Advanced Tactile Sensing Technologies for Soft Grippers,” Advanced Functional Materials, vol. 33, no. 41, p. 2306249, 2023, doi: 10.1002/adfm.202306249

    2023 doi

  6. [14]

    An FBG-based slip recognition and monitoring method for non-destructive grasping of flexible manipulator,

    Q. Hou, Y. Fu, M. Luo, Z. Sun, H. Zhou, and G. Li, “An FBG-based slip recognition and monitoring method for non-destructive grasping of flexible manipulator,” Sensors and Actuators A: Physical, vol. 379, p. 115954, Dec. 2024, doi: 10.1016/j.sna.2024.115954

    2024

  7. [15]

    Bionic Slipping Perception Based on FBG Static-Dynamic Sensing Point,

    C. Lyu et al., “Bionic Slipping Perception Based on FBG Static-Dynamic Sensing Point,” IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1–8, 2023, doi: 10.1109/TIM.2023.3268441

    2023

  8. [16]

    Movement detection in soft robotic gripper using sinusoidally embedded fiber optic sensor,

    M. Yang, Q. Liu, H. S. Naqawe, and M. P. Fok, “Movement detection in soft robotic gripper using sinusoidally embedded fiber optic sensor,” Sensors (Switzerland), vol. 20, no. 5, 2020, doi: 10.3390/s20051312

    2020 doi

  9. [17]

    A Detachable FBG-Based Contact Force Sensor for Capturing Gripper-Vegetable Interactions,

    W. Lai et al., “A Detachable FBG-Based Contact Force Sensor for Capturing Gripper-Vegetable Interactions,” in 2024 IEEE International Conference on Robotics and Automation (ICRA), May 2024, pp. 5673–5679. doi: 10.1109/ICRA57147.2024.10611433

    2024

  10. [18]

    Smart Grow-and-Twine Gripper for Vegetable Harvesting in Vertical Farms,

    J. Liu, W. Lai, B. R. Sim, J. M. Rui Tan, S. Magdassi, and S. J. Phee, “Smart Grow-and-Twine Gripper for Vegetable Harvesting in Vertical Farms,” in 2024 IEEE 7th International Conference on Soft Robotics (RoboSoft), Apr. 2024, pp. 460–466. doi: 10.1109/RoboSoft60065.2024.10521949

    2024

  11. [19]

    3D printed polyurethane exhibits isotropic elastic behavior despite its anisotropic surface,

    M. Pagac, D. Schwarz, J. Petru, and S. Polzer, “3D printed polyurethane exhibits isotropic elastic behavior despite its anisotropic surface,” Rapid Prototyping Journal, vol. 26, no. 8, pp. 1371–1378, Jan. 2020, doi: 10.1108/RPJ-02-2019-0027

    2020 doi

  12. [20]

    Force Sensing With 1 mm Fiber Bragg Gratings for Flexible Endoscopic Surgical Robots,

    W. Lai et al., “Force Sensing With 1 mm Fiber Bragg Gratings for Flexible Endoscopic Surgical Robots,” IEEE/ASME Transactions on Mechatronics, vol. 25, no. 1, pp. 371–382, Feb. 2020, doi: 10.1109/TMECH.2019.2951540

    2020

  13. [21]

    A Three-Axial Force Sensor Based on Fiber Bragg Gratings for Surgical Robots,

    W. Lai, L. Cao, J. Liu, S. Chuan Tjin, and S. J. Phee, “A Three-Axial Force Sensor Based on Fiber Bragg Gratings for Surgical Robots,” IEEE/ASME Transactions on Mechatronics, vol. 27, no. 2, pp. 777–789, Apr. 2022, doi: 10.1109/TMECH.2021.3071437

    2022

  14. [22]

    J. M. Gere and B. J. Goodno, Mechanics of materials, 7. ed., [Nachdr.]. Toronto: Cengage Learning, 2010

    2010

  15. [23]

    Investigation of inelastic behavior of elastomeric composites during loading–unloading cycles,

    M. Wang, D. Shan, Y. Liao, and L. Xia, “Investigation of inelastic behavior of elastomeric composites during loading–unloading cycles,” Polymer Bulletin, vol. 75, no. 2, pp. 561–568, Feb. 2018, doi: 10.1007/s00289-017-2051-x

    2018 doi

  16. [24]

    The XBot2 real-time middleware for robotics,

    A. Laurenzi, D. Antonucci, N. G. Tsagarakis, and L. Muratore, “The XBot2 real-time middleware for robotics,” Robotics and Autonomous Systems, vol. 163, p. 104379, May 2023, doi: 10.1016/j.robot.2023.104379

    2023

  17. [25]

    ROS2 Real-time Performance Optimization and Evaluation,

    Y. Ye, Z. Nie, X. Liu, F. Xie, Z. Li, and P. Li, “ROS2 Real-time Performance Optimization and Evaluation,” Chin. J. Mech. Eng., vol. 36, no. 1, p. 144, Dec. 2023, doi: 10.1186/s10033-023-00976-5

    2023 doi

  18. [26]

    Intelligent robotic gripper with adaptive grasping force,

    S.-J. Huang, W.-H. Chang, and J.-Y. Su, “Intelligent robotic gripper with adaptive grasping force,” Int. J. Control Autom. Syst., vol. 15, no. 5, pp. 2272–2282, Oct. 2017, doi: 10.1007/s12555-016-0249-6

    2017 doi

  19. [27]

    An Object Recognition Grasping Approach Using Proximal Policy Optimization With YOLOv5,

    Q. Zheng, Z. Peng, P. Zhu, Y. Zhao, R. Zhai, and W. Ma, “An Object Recognition Grasping Approach Using Proximal Policy Optimization With YOLOv5,” IEEE Access, vol. 11, pp. 87330–87343, 2023, doi: 10.1109/ACCESS.2023.3305339

    2023

  20. [28]

    Visual–tactile object recognition of a soft gripper based on faster Region-based Convolutional Neural Network and machining learning algorithm,

    C. Jiao, B. Lian, Z. Wang, Y. Song, and T. Sun, “Visual–tactile object recognition of a soft gripper based on faster Region-based Convolutional Neural Network and machining learning algorithm,” International Journal of Advanced Robotic Systems, vol. 17, no. 5, p. 1729881420948...

    2020 doi

  21. [29]

    Control Strategies for Soft Robot Systems,

    J. Wang and A. Chortos, “Control Strategies for Soft Robot Systems,” Advanced Intelligent Systems, vol. 4, no. 5, p. 2100165, 2022, doi: 10.1002/aisy.202100165

    2022 doi

  22. [30]

    Control Methodologies for Robotic Grippers: A Review,

    S. Cortinovis, G. Vitrani, M. Maggiali, and R. A. Romeo, “Control Methodologies for Robotic Grippers: A Review,” Actuators, vol. 12, no. 8, Art. no. 8, Aug. 2023, doi: 10.3390/act12080332

    2023 doi

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Reviewed August 9, 2026 · model on record in the stance chip above.