Dynamic Collision Modeling for Mobile Medical Device Movement
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Solution Overview
Problem
Conventional static collision models in medical devices with motorized functions are inadequate for adapting to changing environments, leading to inefficient movements or collisions, and rely on manual adjustments that are prone to errors and require skilled operators.
Innovation Solution
A method for training a dynamic collision model using machine learning, combining deployment type and environmental maps during the planning and installation phase, enabling adaptive collision avoidance and efficient movement of mobile medical devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static collision model is used in medical devices with motorized functions, then the device structure is simple and easy to implement, but the device cannot adapt to changing environmental conditions, leading to collisions or inefficient movements
Solution Approach 1:
The patent applies the dynamics principle by transitioning from a static collision model to a dynamic collision model that continuously adapts to changing environmental conditions. The dynamic model updates collision parameters in real-time based on sensor feedback and environmental changes, enabling the medical device to respond to varying spatial conditions, patient positions, and additional equipment without requiring complex manual reconfiguration.
Solution Approach 2:
The patent implements self-service by enabling the collision model to automatically adjust and optimize itself without requiring manual intervention from service technicians. The system uses machine learning algorithms to learn from environmental data and automatically update collision parameters, eliminating the need for skilled operators to manually measure and configure obstacles while maintaining high adaptability.
2Reliability
If manual adjustment of collision models is performed during installation, then the model can be adapted to specific room situations, but the process is error-prone and requires skilled technicians
Solution Approach 1:
The system performs self-service by automatically capturing environmental maps and training collision models without requiring manual measurement or configuration by technicians. The medical device uses its sensors to autonomously detect obstacles, measure spatial dimensions, and generate accurate collision models, eliminating human error and the need for skilled operators while improving both reliability and ease of operation.
Solution Approach 2:
The patent replaces the manual mechanical measurement and configuration process with an automated sensor-based system. Instead of technicians physically measuring obstacles and entering data, the system uses cameras, LIDAR, or other sensors to automatically capture environmental data and process it into accurate collision models, significantly improving accuracy while simplifying the installation process.
3Adaptability or versatility
If preset collision models are switched based on configuration, then the device can react to different circumstances, but multiple models increase system complexity
Solution Approach 1:
The patent applies universality by creating a single dynamic collision model that can handle multiple different circumstances and environments. Instead of maintaining separate preset models for different scenarios, the dynamic model adapts its parameters based on the current environmental conditions, patient position, and device configuration, providing universal adaptability while reducing system complexity.
Solution Approach 2:
The system uses dynamics to transition from static preset models to a dynamic model that continuously adjusts its behavior. The dynamic collision model modifies its parameters in real-time based on sensor feedback and environmental changes, enabling the device to respond to different circumstances without requiring multiple predefined models, thus reducing complexity while maintaining versatility.
4Reliability
If safety features like speed limiters and emergency stops are implemented, then collision risk is reduced, but device productivity and movement efficiency decrease
Solution Approach 1:
The patent applies dynamics by replacing static safety limits with dynamic safety parameters that adjust in real-time. The dynamic collision model continuously calculates safe movement parameters based on the current environmental context, allowing the device to move efficiently when conditions permit while maintaining high collision avoidance reliability. This eliminates the need for conservative speed limiters and emergency stops, improving productivity without compromising safety.
Data Source
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AI summary
The invention relates to a technique for training a dynamic collision model for a mobile medical device. A computer-implemented method (100) comprises acquiring (S102) an indication of a medical device application (506) of the device. An environmental map (502) of an application location associated with the application type (506) of the device is acquired (S104). A dynamic collision model (516) for the device is trained (S106) using machine learning (510). The training (S106) is based on the combination of the acquired (S102) indication of the application type (506) and the acquired (S104) environmental map (502) with the application location. The training (S106) is performed during a planning and/or installation phase of the device.