cs.ROFeb 26, 2026

Automated Robotic Needle Puncture for Percutaneous Dilatational Tracheostomy

Authors: Yuan Tang, Bruno V. Adorno, Brendan A. McGrath, Andrew Weightman

Organizations: Manchester Centre for Robotics and AI and the Department of Mechanical and Aerospace Engineering, University of Manchester, Manchester, UK · Manchester Centre for Robotics and AI and the Department of Electrical and Electronic Engineering, University of Manchester, Manchester, UK · Manchester University Foundation Trust Wythenshawe Hospital Intensive Care Unit, Manchester, UK

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Abstract

Percutaneous dilatational tracheostomy (PDT) is frequently performed on patients in intensive care units for prolonged mechanical ventilation. The needle puncture, as the most critical step of PDT, could lead to adverse consequences such as major bleeding and posterior tracheal wall perforation if performed inaccurately. Current practices of PDT puncture are all performed manually with no navigation assistance, which leads to large position and angular errors (5 mm and 30 degree). To improve the accuracy and reduce the difficulty of the PDT procedure, we propose a system that automates the needle insertion using a velocity-controlled robotic manipulator. Guided using pose data from two electromagnetic sensors, one at the needle tip and the other inside the trachea, the robotic system uses an adaptive constrained controller to adapt the uncertain kinematic parameters online and avoid collisions with the patient's body and tissues near the target. Simulations were performed to validate the controller's implementation, and then four hundred PDT punctures were performed on a mannequin to evaluate the position and angular accuracy. The absolute median puncture position error was 1.7 mm (IQR: 1.9 mm) and midline deviation was 4.13 degree (IQR: 4.55 degree), measured by the sensor inside the trachea. The small deviations from the nominal puncture in a simulated experimental setup and formal guarantees of collision-free insertions suggest the feasibility of the robotic PDT puncture.

Figures & tables

Fig. 1 : Bronchoscopic view of an extremely lateral PDT puncture [ 5 ] . Lateral punctures could lead to complications such as tracheostomy tube misplacements and posterior tracheal wall damage.
Fig. 2 : System hardware for automated needle puncture: a robot manipulator, an electromagnetic transmitter, and two sensors (one at the needle tip for navigating the robot, one inserted through the bronchoscope channel and reaching its tip for measuring the desired target puncture position and direction).
Fig. 3 : Simulation setup for robotic PDT needle puncture. The initial estimated needle tip position might be very inaccurate due to high uncertainty in the robot base displacement and needle attachment.
Fig. 4 : Simulation results of the needle puncture. (A) The nominal quintic trajectory ( black curve ) prescribes a smooth approach closely executed in the closed-loop system; (B) at the start of the robot motion, the error is larger than the threshold, but it is quickly reduced to satisfy the constraints. (C) The small violations ( <0.01∘<0.01^{\circ} ) are due to noisy measurements.
Fig. 5 : Simulation results of the adaptation law.
Fig. 6 : (A) Experimental setup of the robotic PDT needle puncture. (B) A mannequin was used for experiments. (C) Example of a needle puncture. (D-E) Bronchoscope images of two needle punctures performed on the silicone skin.
Fig. 7 : Illustration of variables collected during the experiments (seen from anterior to posterior tracheal wall). Variables δmn\delta_{mn} , δn\delta_{n} and εn\varepsilon_{n} determine the quality of puncture. Variables δmm\delta_{mm} , δm\delta_{m} and εm\varepsilon_{m} determine the quality of bronchoscope sensor measurements. Variables δr\delta_{r} and εr\varepsilon_{r} evaluate the VFI constraint violations.
Fig. 8 : Experiment results of the robotic puncture.
 Mannequin position 1Mannequin position 2Mannequin position 3Mannequin position 4All mannequin positions
Number of trials100100100100400
Median (IQR) of needle tip position error εn\varepsilon_{n} [ mm\mathrm{mm} ]2.1 (2.3)1.7 (1.8)1.7 (1.7)1.6 (1.5)1.7 (1.9)
Median (IQR) of final needle error distance εr\varepsilon_{r} [ mm\mathrm{mm} ]1.5 (0.1)1.3 (0.2)1.5 (0.1)1.4 (0.3)1.4 (0.2)
Median (IQR) of needle error angle δn\delta_{n} [ \SIUnitSymbolDegree\mathrm{\SIUnitSymbolDegree} ]21.60 (2.86)22.69 (5.14)21.60 (4.79)23.43 (4.42)21.91 (4.60)
Median (IQR) of final needle error angle δr\delta_{r} [ \SIUnitSymbolDegree\mathrm{\SIUnitSymbolDegree} ]0.20 (0.01)0.20 (0.01)0.20 (0.02)0.20 (0.02)0.20 (0.02)
Median (IQR) of needle midline deviation δmn\delta_{mn} [ \SIUnitSymbolDegree\mathrm{\SIUnitSymbolDegree} ]3.33 (3.74)4.55 (4.66)4.61 (4.71)4.60 (5.31)4.13 (4.55)
TABLE I : Results of robotic PDT needle puncture experiments 1,2
Fig. 9 : Illustration of the bronchoscope sensor measurement. The bronchoscope sensor may not be fully aligned with the nominal trachea direction, which will lead to angular errors in puncture guide line measurements.
 Mannequin position 1Mannequin position 2Mannequin position 3Mannequin position 4All mannequin positions
Mean ±\pm SD of εm\varepsilon_{m} [ mm\mathrm{mm} ]1.9 ±\pm 1.41.9 ±\pm 1.41.6 ±\pm 1.01.7 ±\pm 1.11.8 ±\pm 1.2
Mean ±\pm SD of εn\varepsilon_{n} [ mm\mathrm{mm} ]2.0 ±\pm 1.41.9 ±\pm 1.01.7 ±\pm 1.01.6 ±\pm 1.01.8 ±\pm 1.0
Mean ±\pm SD of εr\varepsilon_{r} [ mm\mathrm{mm} ]1.5 ±\pm 0.21.3 ±\pm 0.31.5 ±\pm 0.21.4 ±\pm 0.31.4 ±\pm 0.2
Median (IQR) of εm\varepsilon_{m} [ mm\mathrm{mm} ]2.0 (2.7)1.8 (1.7)1.5 (1.8)1.6 (1.8)1.7 (2.0)
Mean ±\pm SD of δm\delta_{m} [ \SIUnitSymbolDegree\mathrm{\SIUnitSymbolDegree} ]21.88 ±\pm 2.2022.74 ±\pm 2.9921.53 ±\pm 3.4222.58 ±\pm 3.8922.18 ±\pm 3.21
Mean ±\pm SD of δn\delta_{n} [ \SIUnitSymbolDegree\mathrm{\SIUnitSymbolDegree} ]21.91 ±\pm 2.2122.69 ±\pm 3.0221.60 ±\pm 3.4522.55 ±\pm 3.9022.19 ±\pm 3.23
TABLE II : Results of robotic PDT needle puncture experiments (extended) 1
Fig. 10 : Correlation between δm\delta_{m} , δn\delta_{n} (left) and εm\varepsilon_{m} , εn\varepsilon_{n} (right).
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