Semi-Autonomous Surgical Robot Context Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current robotic systems in surgery lack effective human-machine collaboration, particularly in dealing with complex tasks that involve interaction with tissues and suture threads, and do not allow for seamless automation based on real-time context recognition.
Innovation Solution
A semi-automatic, interactive robotic system that includes a user interface, recognition system, and sensor-actuator system, which learns from demonstrations using Hidden Markov Models and temporal curve averaging to recognize task completion and automate specific subtasks, allowing for seamless transitions between manual and automated execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If robotic systems are used for surgical tasks, then complex and dangerous tasks can be automated, but the systems lack effective human-machine collaboration and cannot recognize task completion in real-time
Solution Approach 1:
The patent implements feedback mechanisms through recognition systems that continuously monitor surgical tasks and provide real-time information about task completion status. This enables the robotic system to adapt its automation level based on the current surgical context, allowing seamless transitions between manual and automated execution while maintaining effective human-machine collaboration.
2Productivity
If full automation is implemented, then surgical workload is reduced, but the system cannot handle complex tasks involving tissue and suture thread interactions
Solution Approach 1:
The patent employs dynamic task execution modes that allow the system to switch between fully automated, semi-automated, and fully manual operation based on task complexity. For simple repetitive tasks, full automation maximizes productivity, while for complex tasks involving tissue manipulation, the system dynamically transitions to semi-automated or manual modes to maintain adaptability and handling capability.
3Ease of operation
If manual control is maintained for all tasks, then full operator control is preserved, but surgeon workload remains high and efficiency is reduced
Solution Approach 1:
The patent implements partial automation where only specific subtasks are automated while others remain under manual control. The recognition system identifies suitable candidates for automation and applies automated execution selectively, providing assistance rather than full replacement. This approach maintains operator control over critical decisions while automating routine operations to improve efficiency.
4Productivity
If context recognition is added to enable seamless automation, then task execution efficiency improves, but system complexity increases
Solution Approach 1:
The patent segments the surgical task into distinct phases and subtasks that can be independently recognized and executed. The recognition system is divided into modular components that detect specific surgical events, allowing the complex overall task to be managed through simpler, specialized recognition modules. This segmentation reduces system complexity while maintaining high execution efficiency.
Data Source
AI summary
A semi-automatic, interactive robotic system for performing and/or simulating a multi-step task includes a user interface system, a recognition system adapted to communicate with the user interface system, a control system adapted to communicate with the recognition system, and a sensor-actuator system adapted to communicate with the control system. The recognition system is configured to recognize actions taken by a user while the user operates the user interface system and to selectively instruct the control system to cause the sensor-actuator system to perform, and/or simulate, one of an automatic step, a semi-automatic step or direct step of the multi-step task based on the recognized actions and a task model of the multi-step task.