Robotic Arm Object Modeling for Operating Room Collision Avoidance
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Solution Overview
Problem
Existing robotic medical systems face challenges in avoiding collisions between robotic arms and other objects in the operating room, including medical accessories and staff, due to limited collision detection and avoidance capabilities.
Innovation Solution
The system incorporates a platform with one or more robotic arms, a console for user input, a processor, and computer-readable memory that stores models of the robotic arms and objects. It uses these models to control robotic arm movement, detect objects within reach, and update the model to include representations of objects, preventing collisions by restricting arm movement into keep-out zones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If robotic arms are used to control medical tools in laparoscopic procedures, then precision and control of tool manipulation is improved, but the risk of collisions between robotic arms and other objects (including other robotic arms and medical accessories) increases
Solution Approach 1:
The system creates a digital model of the operating room environment including all objects and robotic arms before the procedure begins. This preliminary modeling allows the system to pre-calculate potential collision paths and establish virtual boundaries, enabling collision avoidance before actual movement occurs during the surgical procedure.
Solution Approach 2:
The system continuously monitors the positions of robotic arms and updates the digital model in real-time based on actual object locations. This feedback mechanism allows dynamic adjustment of collision avoidance parameters during the procedure, ensuring that the robotic arms can operate precisely while avoiding newly introduced objects or changed configurations.
2Adaptability or versatility
If the workspace of robotic arms is expanded to accommodate more objects and movement, then operational versatility is improved, but the complexity of collision detection and avoidance increases
Solution Approach 1:
The system creates a simplified digital copy (virtual model) of the complex physical operating room environment. This digital twin includes representations of all objects, robotic arms, and spatial relationships. By working with this simplified digital model rather than directly analyzing the complex physical environment, the system can perform collision detection and path planning more efficiently while maintaining full workspace flexibility.
3Reliability
If real-time model updates are performed to include detected objects, then collision avoidance accuracy is improved, but the processing time and computational load increase
Solution Approach 1:
The system performs selective model updates by only processing and updating portions of the digital model that are relevant to current robotic arm operations. Rather than continuously updating the entire workspace model, the system focuses computational resources on updating regions near active robotic arms or where objects have been detected, reducing processing time while maintaining collision avoidance accuracy where it matters most.
Data Source
AI summary
Systems and methods for collision avoidance using object models are provided. In one aspect, a robotic medical system, includes a platform, one or more robotic arms coupled to the platform, a console configured to receive input commanding motion of the one or more robotic arms, a processor, and at least one computer-readable memory in communication with the processor. The processor is configured to control movement of the one or more robotic arms in a workspace based on the input received by the console, receive an indication of one or more objects are within reach of the one or more robotic arms, and update the model to include a representation of the one or more objects in the workspace.