Surgical Work Volume Mapping for Dynamic Collision Avoidance

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Solution Overview

Problem

Existing surgical navigation systems face challenges with slow navigation updates and collisions during robotic surgery due to inadequate prediction and adaptation to user movements.

Innovation Solution

A system that predicts user movements using machine learning models and sensors to dynamically update robotic arm navigation paths, creating 3D maps with high and low probability zones to avoid collisions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional surgical navigation systems are used without prediction capabilities, then the system is simpler and requires less computational resources, but navigation updates are slow and collisions may occur during robotic surgery

Engineering Contradiction:

Improvecollision avoidanceVSAvoidnavigation system complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system performs preliminary action by predicting future positions of the robotic arm and surgical tools before collisions can occur. Machine learning models analyze current trajectories and predict upcoming movements, allowing the navigation system to proactively adjust paths and avoid collisions rather than reacting after they occur.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The navigation system implements dynamics by continuously updating robotic arm trajectories in real-time based on predicted movements of surgical tools and staff. The system adapts to changing surgical conditions dynamically, adjusting navigation paths as new information becomes available during the procedure.

Inventive Principle:

Principle #15Dynamics

2Reliability

If real-time prediction and dynamic path updates are implemented, then collision avoidance improves, but computational resources and processing time increase

Engineering Contradiction:

Improvecollision avoidanceVSAvoidcomputational energy consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The system applies partial action by focusing computational resources on predicting and avoiding only the most critical collision risks rather than calculating all possible trajectories. The machine learning models prioritize high-probability collision scenarios, reducing overall computational energy consumption while maintaining effective collision avoidance.

Inventive Principle:

Principle #16Partial or excessive action

3Measurement precision

If machine learning models are used to predict user movements, then navigation accuracy improves, but system complexity and data processing requirements increase

Engineering Contradiction:

Improvemovement prediction accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system implements feedback by continuously monitoring actual movements of surgical tools and staff, comparing them to predicted trajectories, and using this information to refine future predictions. The machine learning models learn from real-time feedback during surgery, improving measurement precision while managing system complexity through adaptive learning.

Inventive Principle:

Principle #23Feedback

Data Source

PatentEP4405868B1Systems for work volume mapping to facilitate dynamic collision avoidance

Publication Date: 2025.10.29 MAZOR ROBOTICS

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AI summary

A system according to at least one embodiment of the present disclosure includes a processor; and a memory coupled with the processor and including data stored thereon that, when processed by the processor, enables the processor to: predict, at a first time, a motion of an object during a surgical procedure and at a second time following the first time; and update, based on the predicted motion of the object, a surgical navigation path of a robotic arm.