Robotic Surgery Trajectory Control for Deformable Tissue
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Solution Overview
Problem
Current robotic surgery systems are limited by the inability of surgical robots to perform fully or partially autonomous procedures due to inadequate design, optimization, and execution of safe and effective movements, both pre-operatively and in real time.
Innovation Solution
A robotic surgery system that includes an imaging system for obtaining real-time images of a patient's soft tissue organ, a controller configured to receive a user-input treatment trajectory and determine a score related to the organ's function based on the trajectory, and a machine learning system trained with historical surgical data to modify the treatment trajectory and predict post-operative organ function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a surgical robot is used for teleoperative procedures, then surgical precision can be improved, but the robot's ability to make autonomous decisions is limited due to human control imperfections
Solution Approach 1:
The system employs feedback mechanisms where the AI model continuously analyzes surgical data, organ deformation, and trajectory execution, then adjusts control signals in real-time to improve surgical precision while enabling autonomous decision-making capabilities
Solution Approach 2:
The surgical robot incorporates self-service capabilities through an integrated AI model that autonomously processes surgical data, predicts organ deformation, optimizes trajectories, and adjusts control parameters without requiring constant human intervention, thereby enabling autonomous operation while maintaining precision
2Productivity
If the surgical robot executes pre-planned trajectories, then surgical procedure can be performed, but the trajectories cannot be optimized in real-time due to organ deformation
Solution Approach 1:
The system transitions from static pre-planned trajectories to dynamic real-time trajectory optimization by continuously monitoring organ deformation through imaging systems and adjusting trajectories accordingly, maintaining accuracy while enabling procedure execution
Solution Approach 2:
The system performs preliminary trajectory planning before surgery, then continuously optimizes these trajectories in real-time based on actual organ deformation and surgical conditions, combining advance preparation with adaptive refinement to maintain precision throughout the procedure
3Ease of operation
If human surgeons teleoperate the surgical robot, then surgical procedures can be performed, but human error limits the full utilization of robot capabilities
Solution Approach 1:
The system introduces an AI model as an intermediary between the human surgeon and the surgical robot, where the AI processes surgical data, predicts outcomes, and provides optimized control recommendations, thereby reducing human error while maintaining ease of operation through intuitive interfaces
Solution Approach 2:
The surgical robot incorporates self-service capabilities through autonomous AI analysis of surgical conditions, automatic trajectory optimization, and self-adjustment of control parameters, reducing dependence on human operators and improving outcome consistency while maintaining operational ease through collaborative human-AI control
Data Source
AI summary
Teleoperative, partially automated, and fully automated robotic surgery systems and methods are described herein. These systems and methods relate to at least improvement of robotic movements, three dimensional tracking and pose correction for robots interacting with deformable objections, controlling and optimizing the redundant axis of a seven degree of freedom robotic arm, virtual robotic surgery and simulation, and task coordination and optimization for multi-robot surgery.