SaddleSense: Saddle-Shaped Modules for 3D Shape Sensing with Single-Axis Off-the-Shelf Bend Sensors
Overview
This research introduces SaddleSense, a 3D-printed TPU saddle-shaped lattice that integrates single-axis off-the-shelf bend sensors to capture multi-axis deformation in continuum robots. The system uses a hybrid calibration combining learned models with physics-based fallbacks to map sensor readings to piecewise constant curvature parameters. A six-module prototype on a 430 mm body achieves a tip position RMSE of 13.2 mm (3.1%) on held-out samples similar to the training set and 15.8 mm (3.7%) on separate trials, demonstrating effective posture feedback without tying segment length to sensor length.
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Event evidence and corrections
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Reported quantity · tip position RMSE: 29.9 other · Basis not reported · Differing source assertions
“bration maps the sensor readings to piecewise constant curvature (PCC) parameters using a learned model paired with a physics-based fallback for out-of-distribution inputs. We demonstrate a six-module prototype with 24 off-the-shelf bend sensors on a 430~mm continuum body. Using only each module's four local readings, the physics-based model alone achieves a tip position root mean square error (RMSE) of 29.9~mm (7.0\”
Exact source · revision 1Source owner not reported
Reported quantity · tip position RMSE: 13.2 other · Basis not reported · Differing source assertions
“% of body length). The data-driven model reduces the tip RMSE to 13.2~mm (3.1\%) on held-out samples of motions similar to the training set and 15.8~mm (3.7\%) on separately recorded trials. Reducing each module from four sensors to one nearly triples the tip error.”
Exact source · revision 1Source owner not reported
Reported quantity · tip position RMSE: 15.8 other · Basis not reported · Differing source assertions
“% of body length). The data-driven model reduces the tip RMSE to 13.2~mm (3.1\%) on held-out samples of motions similar to the training set and 15.8~mm (3.7\%) on separately recorded trials. Reducing each module from four sensors to one nearly triples the tip error.”
Exact source · revision 1Source owner not reported
Reported quantity · body length: 430 other · Basis not reported
“bration maps the sensor readings to piecewise constant curvature (PCC) parameters using a learned model paired with a physics-based fallback for out-of-distribution inputs. We demonstrate a six-module prototype with 24 off-the-shelf bend sensors on a 430~mm continuum body. Using only each module's four local readings, the physics-based model alone achieves a tip position root mean square error (RMSE) of 29.9~mm (7.0\”
Exact source · revision 1Source owner not reported
Reported quantity · modules: 6 other · Basis not reported
“bration maps the sensor readings to piecewise constant curvature (PCC) parameters using a learned model paired with a physics-based fallback for out-of-distribution inputs. We demonstrate a six-module prototype with 24 off-the-shelf bend sensors on a 430~mm continuum body. Using only each module's four local readings, the physics-based model alone achieves a tip position root mean square error (RMSE) of 29.9~mm (7.0\”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalSaddleSense: Saddle-Shaped Modules for 3D Shape Sensing with Single-Axis Off-the-Shelf Bend Sensors
This research introduces SaddleSense, a 3D-printed TPU saddle-shaped lattice that integrates single-axis off-the-shelf bend sensors to capture multi-axis deformation in continuum robots. The system uses a hybrid calibration combining learned models with physics-based fallbacks to map sensor readings to piecewise constant curvature parameters. A six-module prototype on a 430 mm body achieves a tip position RMSE of 13.2 mm (3.1%) on held-out samples similar to the training set and 15.8 mm (3.7%) on separate trials, demonstrating effective posture feedback without tying segment length to sensor length.
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