Surgical Perception Framework for Robotic Tool Tracking
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Solution Overview
Problem
Current surgical robotic systems lack effective integration of perception, particularly in tracking objects in 3D space, which is crucial for successful control in non-structured surgical environments, leading to inefficiencies in tasks like suturing and hemostasis.
Innovation Solution
A surgical perception framework (SuPer) that uses Bayesian filtering for surgical tool tracking and embedded deformation nodes for tissue tracking, integrating visual perception with endoscopic image data to provide 3D pose information and deformable tissue reconstruction, enabling accurate manipulation and control in closed-loop systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If surgical robotic systems use traditional control algorithms without integrated perception, then device complexity is reduced, but measurement precision and tracking accuracy in 3D space deteriorate
Solution Approach 1:
The patent merges perception modules (visual, tactile, force sensors) with control algorithms into an integrated surgical robotic system. This combination allows the system to simultaneously perform environmental sensing and control tasks, resolving the contradiction by achieving high tracking accuracy through integrated perception while managing complexity through unified system architecture.
Solution Approach 2:
The patent introduces perception modules as intermediary components between the robotic manipulators and the surgical environment. These intermediaries (visual sensors, tactile sensors, force sensors) bridge the gap between physical manipulation and environmental understanding, enabling accurate 3D tracking without requiring complete redesign of the entire control system.
2Measurement precision
If surgical robotic systems integrate comprehensive perception for 3D tracking, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent segments the perception system into distinct functional modules: visual perception for 3D location tracking, tactile perception for contact detection, and force sensing for manipulation control. Each module handles specific sensing tasks independently, allowing high measurement precision through specialized sensors while managing overall system complexity through modular architecture.
Solution Approach 2:
The patent implements perception modules that serve multiple functions simultaneously. For example, visual sensors not only track 3D positions but also provide environmental mapping and tool identification. This multi-functionality achieves comprehensive 3D tracking precision while reducing the number of separate components needed, thereby managing system complexity.
3Productivity
If surgical robotic systems implement automated control tasks, then surgeon fatigue is reduced and procedural consistency improves, but adaptability to non-structured surgical environments deteriorates without integrated perception
Solution Approach 1:
The patent implements closed-loop feedback control where perception modules continuously monitor the surgical environment and provide real-time data to control algorithms. This feedback mechanism enables automated control tasks to adapt to non-structured environments by responding to actual environmental conditions, thereby maintaining both procedural consistency and environmental adaptability.
Solution Approach 2:
The patent employs dynamic control algorithms that adjust their behavior based on real-time perception data. The system transitions between different control modes (manual, semi-autonomous, fully autonomous) depending on environmental structure and task requirements, enabling high procedural consistency in structured tasks while maintaining adaptability when encountering unexpected surgical conditions.
Data Source
AI summary
A method for tracking a surgical robotic tool being viewed by an endoscopic camera, images of the surgical tool are received from the endoscopic camera and surgical tool joint angle measurements are received from the surgical tool. Predetermined features of the surgical tool on the images of the surgical tool are detected to define an observation model to be employed by a Bayesian Filter. A lumped error transform and observable joint angle measurement errors are estimated using the Bayesian Filter. The lumped error transform compensates for errors in a base-to-camera transform and non-observable joint angle measurement errors. Pose information over time of the surgical tool is determined with respect to the endoscopic camera using kinematic information of the robotic tool, the surgical tool joint angle measurements, the lumped error transform and the observable joint angle measurement errors. The pose information is provided to a surgical application.