A case study on needle selection and fuzzy-based control for suturing force calculation in Minimally Invasive Robotic Surgery

Based on the long-term goal, robots are playing an important role in Minimally Invasive Robotic Surgery (MIRS) where they displace the limits and increase both accuracy and precision which a human cannot achieve. In minimal invasive robotic surgery, the operation is performed by a surgical robot controlled by a human (surgeon) near to the surgical robot through a control system. In the surgical operation room, surgeon and assistant must create an environment for which the robot will get optimal freedom of motion. Inappropriate suturing speed may tear off muscles, leading to internal bleeding and organ damages, which extends the operation time. This paper introduces a method to identify the strength of muscles during suturing task itself and based on muscle strength, a fuzzy-based control system controls the torque applied by the end effector, for better suturing without any muscle damages. Analysis of diagnostic sonographic images is used for finding strength variations of muscles. Sonographic imaging probe is attached to one of the robotic arms which is inserted into the surgical area of the human body through the specified port for getting a clear vision of the entire operating region. Suturing needle selection plays an important role in MIRS. Improper selection of needle leads to multiple regrasps to complete the operation. Muscle strength identification and puncturing force calculation are done using MATLAB.