Autonomous navigation of a magnetic colonoscope using force sensing and a heuristic search algorithm

Hao-En Huang, Sheng-Yang Yen, Chia-Feng Chu, Fat-Moon Suk, Gi-Shih Lien, Chih-Wen Liu, Hao-En Huang, Sheng-Yang Yen, Chia-Feng Chu, Fat-Moon Suk, Gi-Shih Lien, Chih-Wen Liu

Abstract

This paper presents an autonomous navigation system for cost-effective magnetic-assisted colonoscopy, employing force-based sensors, an actuator, a proportional-integrator controller and a real-time heuristic searching method. The force sensing system uses load cells installed between the robotic arm and external permanent magnets to derive attractive force data as the basis for real-time surgical safety monitoring and tracking information to navigate the disposable magnetic colonoscope. The average tracking accuracy on magnetic field navigator (MFN) platform in x-axis and y-axis are 1.14 ± 0.59 mm and 1.61 ± 0.45 mm, respectively, presented in mean error ± standard deviation. The average detectable radius of the tracking system is 15 cm. Three simulations of path planning algorithms are presented and the learning real-time A* (LRTA*) algorithm with our proposed directional heuristic evaluation design has the best performance. It takes 75 steps to complete the traveling in unknown synthetic colon map. By integrating the force-based sensing technology and LRTA* path planning algorithm, the average time required to complete autonomous navigation of a highly realistic colonoscopy training model on the MFN platform is 15 min 38 s and the intubation rate is 83.33%. All autonomous navigation experiments are completed without intervention by the operator.

Conflict of interest statement

The authors declare no competing interests.

© 2021. The Author(s).

Figures

Figure 1
Figure 1
The architecture of magnetic-assisted colonoscopy (MAC) system. (a) Magnetic field navigator (MFN). (b) Disposable magnetic colonoscope (MC) and its receiver. (c) Tension/compression load cells and their signal amplifier.
Figure 2
Figure 2
Schematic illustration showing the architecture of proposed force sensing system, featuring load cells installed between the robot arm and the container holding a large permanent magnet.
Figure 3
Figure 3
Illustration of the trace, moving speed and force variation in localization function. (a) The tracking path illustrates a localization trace after the tracking process is stopped. The starting point of EPM was placed at (x = 347, y = 315), and the IPM was placed at (x = 464, y = 187) with a vertical height difference of 7.5 cm. (b) The variation in motor speed and force vectors are presented.
Figure 4
Figure 4
The trajectory simulation in synthetic colon with (a) online BFS (b) online DFS (c) LRTA* method. The taken steps for online BFS, online DFS and LRTA* are 768, 200 and 75, respectively. The non-passable states and the passable states are represented in black and white, respectively. When the algorithm starts to explore, the state in each iteration and the trajectory between states are represented in orange and green, respectively. If the non-passable state is visited, the trajectory is shown in purple.
Figure 5
Figure 5
The tracking result of autonomous navigation. (a) Illustration of the autonomous navigation trajectory. (b) Attractive force in each state corresponds to the tracking result in the graph (a). (c) Time required to complete autonomous navigation experiment 25 times. The average navigation duration was 15 min 38 s, corresponding to an average moving speed of 96 mm/min.
Figure 6
Figure 6
Heat maps showing forces in various locations around an IPM. (a) Without magnetic objects; (b) IPM directly beneath the EPM; (c) IPM is in front of the EPM. Load cell A is stretched while load cell C is squeezed; (d) IPM is located in the bottom right region of the EPM. Load cells A and D are stretched while load cells B and C are squeezed.
Figure 7
Figure 7
Results of detectable range, force distribution and attractive force when the MC is placed in the center of the MFN platform in four different vertical distance from EPM to MC: (a) 10 cm; (b) 7.5 cm; (c) 5 cm; (d) 2.5 cm.
Figure 8
Figure 8
The orchestration of visiting direction sequence in heuristic evaluation design while the passable direction (action) from the previous iteration is (a) θ′=0∘ and (b) θ′=45∘.
Figure 9
Figure 9
The flowchart of the autonomous navigation in the MAC system.
Figure 10
Figure 10
Experimental setup. A flexible colonoscope training model was placed on the MFN platform and covered with a clear acrylic board. While the EPM is guiding the MC, the illustration of EPM in sweeping mode is also presented.

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