Physically Constrained Kinematic Calibration for High-Precision Robotic Needle Guidance
Overview
This paper presents a robust bound-constrained kinematic calibration method to improve positional accuracy for a 6-DoF robotic needle-guidance manipulator. By using physically plausible parameter ranges as active optimization constraints, the approach mitigates parameter coupling and overfitting. In simulation, the method reduced end-effector RMSE from 3.422 mm to 0.690 mm (79.8% improvement). Benchtop experiments achieved sub-millimeter validation accuracy, with 0.865 mm RMSE in trajectory validation and 0.610 mm RMSE in coordinate-lattice verification. These results establish a high-precision foundation for future clinical applications of robotic needle insertion.
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Reported quantity · end-effector RMSE in simulation: 0.69 other · Basis not reported
“n constraints rather than mere numerical safeguards, the framework effectively mitigates parameter coupling and overfitting, ensuring highly stable parameter identification. In simulation, the proposed method reduced end-effector Root Mean Square Error (RMSE) from 3.422 mm to 0.690 mm (a 79.8% improvement). In benchtop hardware experiments, the calibrated model achieved sub-millimeter validation accuracy, including 0”
Exact source · revision 1Source owner not reported
Reported quantity · end-effector RMSE in coordinate-lattice verification task: 0.61 other · Basis not reported
“.865 mm RMSE in experimental trajectory validation and 0.610 mm RMSE in a coordinate-lattice verification task. These results demonstrate that physically constrained kinematic calibration can enhance robotic needle-targeting accuracy, establishing a high-precision foundation for future clinical applications.”
Exact source · revision 1Source owner not reported
Reported quantity · end-effector RMSE before calibration (simulation): 3.422 other · Basis not reported
“n constraints rather than mere numerical safeguards, the framework effectively mitigates parameter coupling and overfitting, ensuring highly stable parameter identification. In simulation, the proposed method reduced end-effector Root Mean Square Error (RMSE) from 3.422 mm to 0.690 mm (a 79.8% improvement). In benchtop hardware experiments, the calibrated model achieved sub-millimeter validation accuracy, including 0”
Exact source · revision 1Source owner not reported
Reported quantity · number of parameters in geometric identification model: 19 other · Basis not reported
“matic models do not fully represent the physical manipulator. This paper presents a robust bound-constrained kinematic calibration method designed to improve positional accuracy for a 6-Degree-of-Freedom (6-DoF) robotic needle-guidance manipulator. The approach uses physically plausible parameter ranges to regularize a compact 19-parameter geometric identification model. By treating these bounds as active optimizatio”
Exact source · revision 1Source owner not reported
Reported quantity · degrees of freedom of robotic manipulator: 6 other · Basis not reported
“matic models do not fully represent the physical manipulator. This paper presents a robust bound-constrained kinematic calibration method designed to improve positional accuracy for a 6-Degree-of-Freedom (6-DoF) robotic needle-guidance manipulator. The approach uses physically plausible parameter ranges to regularize a compact 19-parameter geometric identification model. By treating these bounds as active optimizatio”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalPhysically Constrained Kinematic Calibration for High-Precision Robotic Needle Guidance
This paper presents a robust bound-constrained kinematic calibration method to improve positional accuracy for a 6-DoF robotic needle-guidance manipulator. By using physically plausible parameter ranges as active optimization constraints, the approach mitigates parameter coupling and overfitting. In simulation, the method reduced end-effector RMSE from 3.422 mm to 0.690 mm (79.8% improvement). Benchtop experiments achieved sub-millimeter validation accuracy, with 0.865 mm RMSE in trajectory validation and 0.610 mm RMSE in coordinate-lattice verification. These results establish a high-precision foundation for future clinical applications of robotic needle insertion.
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