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.
Highlights
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
We developed a magnetic-assisted colonoscopy (MAC) system featuring force-based sensing technology and applied the learning real-time A* (LRTA*) searching scheme using a cost-effective colonoscope to facilitate autonomous navigation within a highly realistic colonoscopy training model
This paper presents a fully autonomous navigation scheme in the cost-effective MAC system via a novel tracking technique using load cells and LRTA* for path searching
Summary
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. 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%. We developed a magnetic-assisted colonoscopy (MAC) system featuring force-based sensing technology and applied the learning real-time A* (LRTA*) searching scheme using a cost-effective colonoscope to facilitate autonomous navigation within a highly realistic colonoscopy training model. 1a and 2) to sense attractive forces associated with capsule endoscopes in three-dimensions This scheme enables the real-time tracking of an interior permanent magnet (IPM) in magnetic colonoscope (MC) with a high degree of accuracy by balancing the four force vectors from load cell module without further add-ons to the system. The ability to monitor attractive forces helps to prevent damage to human tissue resulting from excessive force
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