Confidence-Based Shared Control for Robotic Surgery
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Solution Overview
Problem
Current robotically-assisted surgery (RAS) systems lack an optimal balance between autonomous and manual interaction, limiting their feasibility in various surgical situations and environments, as they are not fully autonomous and require human supervision for safe operation.
Innovation Solution
A confidence-based shared control system that combines manual and autonomous control modes, using a dual-camera system with near-infrared (NIR) and RGBD cameras to identify marker positions, generate 3D trajectories, and control robotic arms for precise tissue manipulation, allowing for real-time adjustment and error reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a completely autonomous RAS system is implemented, then surgical precision and repeatability are improved, but feasibility and safety are reduced due to lack of human supervision
Solution Approach 1:
The system implements dynamic control mode allocation that transitions between autonomous and manual control based on real-time surgical conditions. The allocation function continuously adjusts the degree of automation, allowing the system to operate in fully autonomous mode when conditions are favorable and switch to manual supervision when uncertainty arises, thereby maintaining both precision and safety
Solution Approach 2:
The system employs confidence indicators that provide continuous feedback on the reliability of autonomous control. By monitoring surgical outcomes and comparing them against expected performance, the system generates feedback signals that adjust the allocation function, enabling it to maintain high precision while automatically reducing autonomy when safety concerns are detected
2Stability of the object's composition
If a completely autonomous RAS system is implemented, then repeatability is improved, but feasibility across various surgical situations is reduced
Solution Approach 1:
The adaptive allocation function dynamically adjusts the degree of autonomy based on real-time assessment of surgical conditions, tissue characteristics, and procedural progress. This allows the system to maintain repeatability in standardized procedures while adapting to unique challenges in complex or unexpected surgical situations
Solution Approach 2:
The system changes operational parameters including the level of autonomy, control responsiveness, and decision-making thresholds based on the specific surgical context. By adjusting these parameters in real-time, the system achieves consistent repeatability in routine tasks while maintaining the flexibility to handle diverse surgical scenarios
3Reliability
If manual control is used, then safety and adaptability are maintained, but surgical precision and repeatability are reduced
Solution Approach 1:
The system implements dynamic control mode allocation that transitions between autonomous and manual control based on real-time surgical conditions. The allocation function continuously adjusts the degree of automation, allowing the system to operate in fully autonomous mode when conditions are favorable and switch to manual supervision when uncertainty arises, thereby maintaining both precision and safety
Solution Approach 2:
The shared control system acts as an intermediary between fully manual and fully autonomous control modes. By blending manual operator input with autonomous system recommendations through the allocation function, the system preserves the safety and adaptability of manual control while incorporating the precision and repeatability benefits of automation
4Adaptability or versatility
If manual control is used, then adaptability to various surgical situations is maintained, but surgical precision and error reduction are reduced
Solution Approach 1:
The system implements dynamic control mode allocation that transitions between autonomous and manual control based on real-time surgical conditions. The allocation function continuously adjusts the degree of automation, allowing the system to operate in fully autonomous mode when conditions are favorable and switch to manual supervision when uncertainty arises, thereby maintaining both precision and safety
Solution Approach 2:
The system employs confidence indicators that provide continuous feedback on the reliability of autonomous control. By monitoring surgical outcomes and comparing them against expected performance, the system generates feedback signals that adjust the allocation function, enabling it to maintain high precision while automatically reducing autonomy when safety concerns are detected
Data Source
AI summary
The present disclosure provides a system and method for controlling an articulating member including a tool. The system may include a dual camera system that captures near-infrared (NIR) images and point cloud images of a tissue or other substance that includes NIR markers. The system may generate a three-dimensional (3D) path based on identified positions of the NIR markers, may filter the generated path, and may generate a 3D trajectory for controlling the articulated arm of a robot having a tool to create an incision along the filtered path. In a shared control mode, an operator may generate manually control commands for the robot to guide the tool along such a path, while automated control commands are generated in parallel. One or more allocation functions may be calculated based on calculated manual and automated error models, and shared control signals may be generated based on the allocation functions.