IMPROVED ACCURACY AND PRECISION ON NEUROSURGERY USING FUZZY LOGIC CONTROL SURGICAL ROBOTS

Abstract

This study focuses on the development of a robotic system for precision surgical interventions, emphasizing the importance of optimal force control, position accuracy adjustment, and dynamic adaptability to patient conditions during procedures. The system utilizes real-time feedback mechanisms to enhance surgical precision and safety. The robotic system was designed to apply an optimal force of 1.2 N, minimizing tissue damage by adapting to real-time tissue resistance, which varied from 0 to 2.0 N. The system effectively maintained an accuracy tolerance of ±0.1 mm, ensuring precise positioning of the surgical tool. Throughout the testing, the robotic tool operated with a maximum velocity of 1 mm/s, systematically reducing speed as tissue resistance approached a critical threshold of 1.8 N. Temperature stability was maintained around the optimal target of 37 °C, with measured temperatures fluctuating between 35 °C and 39 °C. The system demonstrated the ability to synchronize tool movements with the patient’s heart rate (80 bpm) and respiratory rate (16 breaths/min), adjusting tool speed between 0 and 0.5 mm/s and movement rates from 0 to 0.5 mm/s, respectively. Additionally, the robotic system maintained a minimum safe distance of 1 mm from critical neural structures, crucial for ensuring patient safety during procedures. The adaptive pressure exerted by the tool was adjusted based on real-time tissue elasticity measurements, ranging from 7000 to 10000 Pa, ensuring optimal handling and minimizing damage to surrounding tissues. The simulation results illustrated the robotic arm's effective positioning capabilities during a surgical procedure, reaching a height of approximately 9 units. The final coordinates of the end effector were calculated to be around (5.13, 3.0, 9.36) units, indicating high positioning accuracy near the surgical site. The system achieved an error rate of less than 2% in positioning accuracy throughout the testing phases. Furthermore, a fuzzy inference system (FIS) was established to assess the relationship between precision, force, and surgical stability, underscoring the system's robustness and adaptability in robotic neurosurgery. Overall, the findings demonstrate the potential of this robotic system to enhance the safety and effectiveness of neurosurgical procedures, improving patient outcomes.