Physically Constrained Kinematic Calibration for High-Precision Robotic Needle Guidance
Physically 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.
The study demonstrates improved kinematic calibration for a 6-DoF robotic needle-guidance system, reducing end-effector RMSE from 3.422 mm to 0.690 mm in simulation and achieving sub-millimeter accuracy (0.865 mm RMSE) in benchtop hardware experiments.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
end-effector RMSE in simulation
0.69
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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
Open source S4end-effector RMSE in coordinate-lattice verification task
0.61
View original evidence
.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.
Open source S5end-effector RMSE before calibration (simulation)
3.422
View original evidence
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
Open source S4number of parameters in geometric identification model
19
View original evidence
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
Open source S3degrees of freedom of robotic manipulator
6
View original evidence
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
Open source S3
- paper: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
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
Open source S4
.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.
Open source S5
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
Open source S3
Physically Constrained Kinematic Calibration for High-Precision Robotic Needle Guidance
Open source S1
Source：arXiv Robotics — research abstracts · arxiv.org