The Russian Foundation for Basic Research (RFBR) has published the regional competitions’ results of 2018. The Laboratory of intelligent robotic systems (LIRS) under the guidance of Professor Evgeni Magid received the support of the Foundation and the Government of the Republic of Tatarstan for the implementation of the scientific project "Development of a control system for robotic laparoscopic instrument for autonomous stitching".
According to statistics, today in Russia there is a large shortage of experienced specialists, including surgeons, which is especially catastrophic in remote rural areas. The successful development of surgical robots will partially solve this problem and will be a significant step towards automating medical services leading to personalized medicine of the future. Therefore, the creation of automated surgical complexes is one of the most important and promising areas of the development of robotics in the field of medicine as part of the transition to high-tech healthcare.
Laparoscopic (minimally invasive) surgery, compared with open surgery, is a more cost-effective and safe alternative that provides rapid recovery after surgery, significantly reduces the frequency of complications and minimizes cosmetic skin defect. As a first step towards automating laparoscopic operations, the project focuses on the suture procedure automation. A certain level of autonomy of the robotic surgical system will help to save the surgeon from tedious repetitive monotonous tasks, using such advantages of the robot as dexterity and precision of movements. To achieve autonomous suturing with the help of a surgical robot, in addition to the complexity of implementing the control system itself in an autonomous or semi-autonomous mode, additional difficulties arise due to the presence of human tissue deformation.
Artur Sagitov, a junior researcher at the LIRS and a key project executor gave us a comment:
"Our project will develop and verify the first prototype of an autonomous surgical manipulator control system to automate suturing procedures under dynamic conditions, taking into account the type of tissue to be sutured. Algorithms will be developed to control the robotic laparoscopic instrument for autonomous tissue suturing, including a model for working with soft (deformable) tissue, models of different types of sutures and planning of manipulator movement. Route planning will be implemented with kinematic constraints of laparoscopic surgery. The integration of stereo camera data and force-torque sensors will enable machine learning to determine the type of tissue to be sutured".