Ultrasound-Guided Wireless Tubular Robotic Anchoring System

Abstract

Untethered miniature robots have significant poten-tial and promise in diverse minimally invasive medical applications inside the human body. For drug delivery and physical contra-ception applications inside tubular structures, it is desirable to have a miniature anchoring robot with self-locking mechanism at a target tubular region. Moreover, the behavior of this robot should be tracked and feedback-controlled by a medical imaging-based system. While such a system is unavailable, we report a reversible untethered anchoring robot design based on remote magnetic actuation. The current robot prototype's dimension is 7.5 mm in diameter, 17.8 mm in length, and made of soft polyurethane elastomer, photopolymer, and two tiny permanent magnets. Its relaxation and anchoring states can be maintained in a stable manner without supplying any control and actuation input. To control the robot's locomotion, we implement a two-dimensional (2D) ultrasound imaging-based tracking and control system, which automatically sweeps locally and updates the robot's position. With such a system, we demonstrate that the robot can be controlled to follow a pre-defined 1D path with the maximal position error of 0.53 ± 0.05 mm inside a tubular phantom, where the reversible anchoring could be achieved under the monitoring of ultrasound imaging.

Keywords: Medical robots and systems; computer vision for medical robotics; mechanism design; soft robotics; ultrasound imaging-based control.

Fig. 1

Design of the proposed soft magnetic anchoring robot module. a. CAD model (i) and photo (ii) of a representative prototype. The robot is composed of two inner magnets and an external four-legged Sarrus linkage. b. Half-section CAD view of the robot with labeled design parameters.

Fig. 2

Actuation method of the robot for reaching to the relaxation or anchoring state. a. Diagram of the actuation method. The anchoring state is actuated by the approaching of actuation magnets with the reverse magnetization direction as the robot’s inner magnets’. The sequence of the state transition is i → ii → iii. The relaxation state is actuated by the one with the same magnetization direction. The sequence of the state transition is iii → iv → i. b. Experimental demonstration photos of two states in a soft plastic tube. In real application scenarios, multiple robots (modules) should be connected in serial to enhance the anchoring state, if necessary.

Fig. 3

Variation of the total inner magnetic force Fmg during the actuation of two robot states. a. Actuation of the anchoring state. The sequence of the state transition is A → B → C → D, where the diagrams of the robot’s configurations are shown aside. $|{\mathit{\text{F}}}_{\text{mag}}^{\text{in}}(\Delta L=5)|>|{\mathit{\text{F}}}_{\text{res}}(\Delta L=5)|$ ensures that the anchoring state can be maintained stably without any control input. b. Actuation of the relaxation state. The transition sequence is D → E → F → A. $|{\mathit{\text{F}}}_{\text{mag}}^{\text{in}}(\Delta L=0)|<|{\mathit{\text{F}}}_{\text{res}}(\Delta L=0)|$ ensures that the relaxation state can be maintained stably (unit: mm).

Fig. 4

Principle of 2D locomotion manipulation and robot position tracking based on local sweeping. a. Isometric view of the locomotion by the manipulation magnet with 4-DoF motion capability. A, B, C refer to the cross section views of three example imaging planes. b. Time sequence demonstration of locomotion. c. Details of the tracking approach. i. Cross section views of A, B, and C. ii. Distribution of pixel value sums along with the sweeping. The sums achieve highest at the locations of two shells.

Fig. 5

Block diagram of the 2D robotic ultrasound imaging-based tracking and control system. ${\sigma }_x\text{and}{\sigma }_y$ indicate the sweeping range around the predicted positions of the robot; $(x_{\text{rm}}^{\text{o}},x_{\text{rm}}^{\text{o}})$ represents the measurement of the robot position from the local sweeping approach; $({\widehat{x}}_{\text{r}}^{\text{o}},{\widehat{y}}_{\text{r}}^{\text{o}})$ represents the estimation of the robot position.

Fig. 6. Photos of the system hardware setup with the coordinate systems. The system includes the imaging part, the robot manipulation part, and the computation part.

Fig. 7

Experimental details of the 2D closed-loop locomotion control system. a-d. Results from a via-point during robot locomotion. a. Camera view and ultrasound imaging of the cross-sections at four steps during the sweeping around a via-point. b. Positions of the imaging plane along the $x_{°}-\text{axis}$ during sweeping. c. Variation of pixel value sums (normalized) during sweeping. d. Relation between the imaging plane positions and the normalized pixel sums from the collected points. Gaussian distributions are used to compute the updated measurement of robot position. e. Measurement and the tracking errors of six via-points along the 1D pre-defined path (points are the means of n = 3 trials; error bars are standard deviations from the means).

Fig. 8

Monitoring of the reversible anchoring from the cross-section view using 2D ultrasound imaging. Legs of the anchoring robot are labeled to indicate the configuration differences between the relaxation state and the anchoring state. a-c. Actuation of the anchoring state. c-e. Actuation of the relaxation state.

Fig. 9

Two states visualized by 3D ultrasound imaging. a-b. Front and side views of the relaxation state inside a tubular phantom. c-d. Two corresponding 3D ultrasound views of the anchoring state.