Locating Contact on Tendon-Driven Continuum Robots From Tendon Measurements via
Overview
This research presents a constrained moving horizon estimator (MHE) that locates contact points and estimates forces on tendon-driven continuum robots using only motor-side tendon measurements. The method encodes non-penetration and complementarity conditions to overcome the inherent null space in the mapping from contact forces to tendon data. In benchmarks involving a two-section robot interacting with a wall, the MHE achieved 0.9 mm localization accuracy and 0.22 N force estimation error, outperforming a tuned extended Kalman filter (65 mm and 0.72 N respectively) while maintaining robustness against unmodeled friction and obstacle position errors.
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Event evidence and corrections
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Reported quantity · contact location error: 0.9 other · Basis not reported
“al, mode-wise robust stability result for it. In a finite- element benchmark of a two-section robot pressing and sliding on a wall, the estimator located contact to 0.9 mm and estimated contact force to 0.22 N, compared with 65 mm and 0.72 N for a tuned extended Kalman filter (EKF) with state projection. The estimator tolerated millimeter- level errors in the obstacle position and unmodeled wall friction, under which”
Exact source · revision 1Source owner not reported
Reported quantity · contact force estimation error: 0.22 other · Basis not reported
“al, mode-wise robust stability result for it. In a finite- element benchmark of a two-section robot pressing and sliding on a wall, the estimator located contact to 0.9 mm and estimated contact force to 0.22 N, compared with 65 mm and 0.72 N for a tuned extended Kalman filter (EKF) with state projection. The estimator tolerated millimeter- level errors in the obstacle position and unmodeled wall friction, under which”
Exact source · revision 1Source owner not reported
Reported quantity · EKF contact location error: 65 other · Basis not reported
“al, mode-wise robust stability result for it. In a finite- element benchmark of a two-section robot pressing and sliding on a wall, the estimator located contact to 0.9 mm and estimated contact force to 0.22 N, compared with 65 mm and 0.72 N for a tuned extended Kalman filter (EKF) with state projection. The estimator tolerated millimeter- level errors in the obstacle position and unmodeled wall friction, under which”
Exact source · revision 1Source owner not reported
Reported quantity · EKF contact force estimation error: 0.72 other · Basis not reported
“al, mode-wise robust stability result for it. In a finite- element benchmark of a two-section robot pressing and sliding on a wall, the estimator located contact to 0.9 mm and estimated contact force to 0.22 N, compared with 65 mm and 0.72 N for a tuned extended Kalman filter (EKF) with state projection. The estimator tolerated millimeter- level errors in the obstacle position and unmodeled wall friction, under which”
Exact source · revision 1Source owner not reported
Reported quantity · trials: 40 other · reported trials
“the projected EKF failed. Under randomized model mismatch over 40 trials, including unmodeled tendon friction, it remained better in force and location in every trial. Without contact, the MHE offered no advantage over the EKF, which confirms that its benefit comes from the contact constraints. This report extends a letter submitted to IEEE Control Systems Letters with complete proofs and additional experiments.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalLocating Contact on Tendon-Driven Continuum Robots From Tendon Measurements Alone via Constrained Moving Horizon Estimation
This research presents a constrained moving horizon estimator (MHE) that locates contact points and estimates forces on tendon-driven continuum robots using only motor-side tendon measurements. The method encodes non-penetration and complementarity conditions to overcome the inherent null space in the mapping from contact forces to tendon data. In benchmarks involving a two-section robot interacting with a wall, the MHE achieved 0.9 mm localization accuracy and 0.22 N force estimation error, outperforming a tuned extended Kalman filter (65 mm and 0.72 N respectively) while maintaining robustness against unmodeled friction and obstacle position errors.
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