Robotic Surgery Control Using Virtual Anatomy and Sensor Feedback
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Solution Overview
Problem
Current robotic surgical systems lack the capability to autonomously perform invasive surgeries with precision and adaptability, requiring external intervention and lacking effective simulation and learning modes for diverse patient anatomies.
Innovation Solution
A robotic surgical system with a processing unit that simulates surgical procedures in a virtual 3D environment, learns through machine learning, and adapts parameters for optimal performance, enabling autonomous or assisted surgical operations in various anatomical fields by simulating movements and sensor feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a robotic surgical system operates autonomously without external intervention, then surgical precision and consistency are improved, but the system lacks adaptability to diverse patient anatomies and unexpected surgical conditions
Solution Approach 1:
The system performs virtual surgery simulations and machine learning training before actual surgical operations. The processing unit creates virtual 3D models of patient anatomies and pre-trains the robotic system's control algorithms, allowing the autonomous system to adapt to specific patient anatomies in advance while maintaining surgical precision during the actual procedure
Solution Approach 2:
The system incorporates feedback mechanisms where robot sensor data from actual surgeries is continuously fed back to the processing unit. This data is used to update and refine the machine learning models and virtual simulations, enabling the autonomous system to improve its adaptability to diverse anatomies and unexpected conditions through cumulative learning while maintaining consistent precision
2Adaptability or versatility
If a robotic surgical system incorporates machine learning and virtual simulation capabilities, then adaptability and learning capacity are improved, but device complexity and computational requirements increase
Solution Approach 1:
The processing unit acts as an intermediary between the simple robotic surgical system and the complex requirements of machine learning. It hosts the machine learning unit and virtual surgery simulation environment, separating the computational complexity from the robotic execution system. This allows the robotic system to gain advanced adaptability through the processing unit's computational power without requiring the robotic components themselves to be overly complex
3Reliability
If a robotic surgical system performs extensive virtual surgery simulations for each individual patient, then surgical outcomes are optimized, but time and computational resources are consumed
Solution Approach 1:
The system performs preliminary virtual surgery simulations and machine learning training before actual surgical operations. By pre-training the robotic system on virtual 3D models of the specific patient's anatomy obtained from medical imaging, the system optimizes surgical outcomes for each individual patient while completing the extensive simulation work in advance, thus minimizing time loss during the actual surgery
Solution Approach 2:
The system creates virtual copies (3D models) of patient anatomies from medical imaging data and performs simulations on these copies rather than requiring extensive physical practice. This copying approach allows comprehensive individualized optimization of surgical outcomes while significantly reducing the time and resources needed compared to physical training, as virtual simulations can be executed rapidly and iterated efficiently
Data Source
AI summary
A robotic surgical system for treating a patient includes a surgical robot with a moveable robot member, an actuator for moving the robot member to 6D poses in a surgical field and for driving the robot member to act in the surgical field, a robot sensor for providing robot sensor data, and a control device for controlling the actuator according to a control program and under feedback of the robot sensor data, a processing unit configured to provide the control program to the control device, and to include and utilize a virtual anatomical model, a virtual surgical robot simulating movement and driving of the robot member, a surgical simulator, a sensor simulator, and a machine learning unit to create the control program, the machine learning unit reading the sensor simulator, the virtual surgical robot, and the virtual surgical field and feeding the virtual surgical robot.