Surgical Robot Tracking Error Detection with Dual Filter Paths

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Solution Overview

Problem

Existing surgical robotic systems are unable to detect errors or loss of accuracy in real time, leading to potential damage to the system or surgical site due to delayed detection of positioning errors.

Innovation Solution

A robotic surgical system that utilizes unfiltered kinematic and navigation data to determine the relationship between components, applying different filter lengths to detect errors or loss of accuracy by comparing filtered and unfiltered relationships, enabling real-time error detection.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If low-pass filtering is applied to tracking data to reduce noise and improve signal-to-noise ratio, then the robot achieves smoother movement and improved performance, but error detection is delayed due to filtering processing time

Engineering Contradiction:
Improverobot performanceVSAvoiderror detection delay
Core Design Contradiction:
ReliabilityVSLoss of time

Solution Approach 1:

The patent segments the tracking data processing into two distinct pathways: a first low-pass filter with longer filter length for robot control to ensure smooth movement, and a second low-pass filter with shorter filter length for error detection to provide timely feedback. This segmentation allows both objectives to be achieved simultaneously without compromising either robot performance or error detection speed.

Inventive Principle:
Principle #1Segmentation

2Speed

If high-speed tracking data is used directly by the robot, then real-time feedback is achieved, but noise and control system stability issues arise

Engineering Contradiction:
Improvetracking speedVSAvoidcontrol system stability
Core Design Contradiction:
SpeedVSStability of the object's composition

Solution Approach 1:

The patent applies different filter characteristics to different processing pathways based on their specific requirements. The first filter uses a longer filter length optimized for control stability, while the second filter uses a shorter filter length optimized for timely error detection. This local quality differentiation allows each pathway to operate at optimal performance levels without compromising overall system stability.

Inventive Principle:
Principle #3Local quality

3Measurement precision

If the outer positioning loop bandwidth is increased to match tracking system measurement speed, then real-time positioning accuracy is improved, but control system stability is compromised

Engineering Contradiction:
Improvepositioning accuracyVSAvoidcontrol system stability
Core Design Contradiction:
Measurement precisionVSStability of the object's composition

Solution Approach 1:

The patent dynamically adjusts the effective bandwidth for different purposes by using two different filter lengths. The first filter provides a lower bandwidth appropriate for stable robot control, while the second filter provides a higher bandwidth for timely error detection. This dynamic approach allows the system to achieve high measurement precision for error detection without compromising the stability required for control operations.

Inventive Principle:
Principle #15Dynamics

Data Source

PatentEP3554774B1Techniques for detecting errors or loss of accuracy in a surgical robotic system
Publication Date: 2025.07.16 MAKO SURGICAL CORP
  • EP3554774B1 patent drawingFigure 1
  • EP3554774B1 patent drawingFigure 2
  • EP3554774B1 patent drawingFigure 3

AI summary

Systems and methods for operating a robotic surgical system are provided. The system includes a surgical tool, a manipulator comprising a base supporting links for controlling the tool, a navigation system comprising a tracker coupled to the tool and a localizer to monitor a state of the tracker. A controller acquires raw kinematic measurement data about a state of the tool relative to the base from the manipulator, known relationship data about the state of the tracker relative to the tool, and raw navigation data about the state of the tracker relative to the localizer from the navigation system. The controller combines this data to determine a raw relationship between the base and the localizer. The raw relationship is filtered for controlling the manipulator. The raw relationship or a less filtered version of the raw relationship is utilized to determine whether an error has occurred in the system.