Medical System Conversion Table Generation for Robot Arm Control
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Solution Overview
Problem
In medical systems with robot arms, the association between treatment tool units and operation devices is complex due to varying configurations and operator preferences, requiring frequent re-association during tool changes and operator shifts, which can lead to operational inefficiencies and errors.
Innovation Solution
A medical system with a processor that generates and proposes a conversion table associating moving parts of a slave device with operation parts of an operation device based on user, slave, and operation device identification information, utilizing conversion table history and operation history to reduce operational deviations and adapt to changing conditions, such as prolonged inactivity or endoscope positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual association is performed between treatment tool units and operation devices, then the association can be customized to operator preferences, but the time and complexity increase during tool changes and operator shifts
Solution Approach 1:
The system performs preliminary action by automatically generating and storing conversion tables that associate moving parts with operation parts before actual use. When a treatment tool unit is replaced or an operator changes, the pre-stored conversion tables are automatically retrieved and applied, eliminating the need for manual re-association and significantly reducing setup time while preserving customization.
Solution Approach 2:
The system creates copies of conversion tables that encode the association relationships between moving parts and operation parts. These conversion table copies are stored in the storage unit and can be rapidly retrieved and applied without manual intervention, allowing the system to replicate successful associations across different tools and operators instantly.
2Productivity
If automatic association is performed using conversion tables, then the association process is rapid and consistent, but the system cannot adapt to operator preferences or new tool configurations
Solution Approach 1:
The system implements dynamics by allowing the conversion tables to be updated and modified. While the default behavior is automatic retrieval for speed, the system dynamically adapts when new treatment tool units are introduced or when operators provide feedback, enabling the conversion tables to evolve and incorporate new associations while maintaining the efficiency of automatic operation.
Solution Approach 2:
The system incorporates feedback mechanisms that allow operators to provide input about their preferences or issues with automatic associations. This feedback is used to update the conversion tables in the storage unit, ensuring that the system learns from actual usage and continuously improves its automatic association accuracy while maintaining rapid operation speeds.
3Adaptability or versatility
If comprehensive conversion tables are generated for all possible tool and operator combinations, then adaptability is maximized, but the storage requirements and system complexity increase
Solution Approach 1:
The system applies universality by creating conversion tables that can serve multiple purposes and be applied to various treatment tool units and operators. Rather than creating completely separate association data for each specific combination, the conversion tables are designed to be broadly applicable, reducing the total amount of data needed while maintaining high adaptability across different tools and users.
Data Source
AI summary
A medical system includes: a slave having at least one moving part; an operation device having at least one operation part; and a processor that controls operations of the slave based on a conversion table that associates operations of the moving part of the slave with inputs of the operation part of the operation device. The processor is programmed to execute: acquiring user identification information of a user of the slave, slave identification information of the slave, and operation device identification information of the operation device, and generating and proposing the conversion table based on the user identification information, the slave identification information, and the operation device identification information.