Surgical Robot Motion Execution via Machine Learning
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Solution Overview
Problem
Surgical robotics face challenges due to mechanical imperfections and variable payloads, leading to inaccuracies in motion execution, which complicates surgical procedures and requires novice surgeons to continuously adjust trajectories, making learning and performing surgeries more difficult.
Innovation Solution
A machine learning algorithm is trained to adjust the movement of surgical instruments based on the composition of human tissues, using data from sensors and feedback loops to apply the necessary force for precise cutting and positioning, and a database is used to recognize different tissues and adjust the surgical robot's actions accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional robotic motion control is used, then mechanical precision is limited due to imperfections and variable payloads, but system complexity remains manageable
Solution Approach 1:
The system performs preliminary action by pre-calculating and storing compensation trajectories for various tissue types and payload conditions. The machine learning model is trained in advance on surgical data to learn the relationship between tissue composition and required motion adjustments, so that during actual surgery, the system can directly apply pre-computed corrections without real-time complex calculations.
Solution Approach 2:
The system implements feedback by continuously monitoring actual surgical outcomes and tissue responses, then using this information to refine and update the machine learning model. The feedback loop allows the system to learn from each surgical procedure, improving motion execution precision over time while adapting to variable payloads and tissue characteristics without increasing operational complexity.
2Extent of automation
If automatic motion control is implemented, then payload variations cause mismatch between desired and actual motion, but manual compensation increases surgeon workload
Solution Approach 1:
The system applies parameter changes by dynamically adjusting motion control parameters based on detected tissue composition and payload conditions. The machine learning model automatically modifies trajectory parameters, velocity profiles, and force applications according to the specific surgical context, eliminating the need for manual compensation while maintaining ease of operation.
Solution Approach 2:
The system practices self-service by automatically detecting payload variations and tissue properties, then self-correcting motion execution without surgeon intervention. The machine learning algorithm enables the robotic system to autonomously adapt to changing surgical conditions, making the system both highly automated and easy to operate.
3Reliability
If precise motion execution is achieved through mechanical improvements, then system reliability increases, but device complexity and cost increase
Solution Approach 1:
The system replaces mechanical precision improvements with software-based intelligence. Instead of using more complex mechanical components to achieve precise motion, the patent uses machine learning algorithms to predict and compensate for deviations. This substitution of mechanical complexity with computational intelligence achieves reliable motion execution while avoiding increased device complexity.
Solution Approach 2:
The system achieves reliability through parameter changes by dynamically adjusting motion parameters based on real-time tissue feedback. The machine learning model modifies velocity, acceleration, and positioning parameters according to detected tissue properties, ensuring reliable motion execution without requiring more complex mechanical systems.
Data Source
AI summary
Robotic surgery systems and methods of surgical robot operation are provided. A method of surgical robot operation includes moving a surgical instrument through a tissue using the surgical robot, where the surgical robot attempts to move the surgical instrument to a desired position. The method further includes measuring an actual position of the surgical instrument, and calculating a difference between the actual position and the desired position. The actual position of the surgical instrument is adjusted to the desired position using the surgical robot.