Gesture-Based Robotic Control via Markerless Vision
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Solution Overview
Problem
Current motion capture and gesture recognition technologies are cumbersome, expensive, and inefficient, particularly in robotics, as they require markers or sensors that interfere with natural movement and struggle to capture real-time dynamic gestures without pre-defined poses, failing to accurately model the complex motion and fine motor skills of the human hand.
Innovation Solution
A gesture-based human-robot interface that tracks the motion and contact of the human hand to generate corresponding robotic commands, using a 3D solid model with adaptable joints to replicate free-form gestures, allowing for intuitive control of robotic arms without the need for markers or sensors, and enabling dexterous manipulation of workpieces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If markers or sensors are used for motion capture, then gesture recognition accuracy is improved, but natural movement is interfered with and device complexity increases
Solution Approach 1:
The patent removes markers and sensors from the system entirely, extracting only the essential function of motion detection. Instead of attaching external devices to the hand, the system uses computer vision algorithms to detect and track hand gestures directly through video input, eliminating the interference caused by physical markers or sensors while maintaining gesture recognition capability
Solution Approach 2:
The patent replaces the mechanical system of markers and sensors with an optical-computational system. Computer vision algorithms process video frames to detect hand position, orientation, and gesture dynamics, substituting physical measurement devices with digital image processing that does not interfere with natural hand movement
2Reliability
If pre-defined poses are used for gesture recognition, then recognition reliability is improved, but ability to capture real-time dynamic gestures is reduced
Solution Approach 1:
The patent implements a dynamic gesture recognition system that processes hand gestures in real-time video streams without requiring pre-defined pose libraries. The system continuously tracks hand position, orientation, and motion vectors across video frames, adapting to any gesture the user performs naturally without being constrained to predetermined poses, thereby capturing real-time dynamic gestures with high versatility
Solution Approach 2:
The system uses continuous feedback from video frame analysis to recognize and interpret gestures as they occur. By processing sequential video frames and tracking hand motion dynamics, the system provides real-time gesture recognition feedback that adapts to the user's natural movements, maintaining reliability through continuous monitoring rather than relying on pre-defined pose matching
3Measurement precision
If complex motion capture systems are used, then gesture tracking precision is improved, but system cost and complexity increase
Solution Approach 1:
The patent creates a virtual copy of the hand's motion and gesture dynamics through computer vision. By analyzing video frames and generating digital representations of hand position, orientation, and movement vectors, the system captures gesture information with sufficient precision for robotic control without requiring complex physical motion capture equipment, thereby reducing system complexity and cost
Solution Approach 2:
The patent employs a universal computer vision-based gesture recognition system that can identify and track multiple types of hand gestures simultaneously. The same video processing pipeline handles various gesture types (pointing, grasping, manipulating) without requiring specialized sensors or markers for each gesture type, reducing overall system complexity while maintaining tracking precision across diverse gesture scenarios
4Stability of the object's composition
If traditional robotic control interfaces are used, then control stability is improved, but ease of operation for intuitive control is reduced
Solution Approach 1:
The system enables the operator to control the robotic arm through natural hand gestures that the system automatically recognizes and translates into control commands. The gesture recognition system serves itself by continuously analyzing video input and generating appropriate robotic control signals without requiring complex interface interactions, making the control process as intuitive as natural hand movement while maintaining stability through consistent gesture-to-command mapping
Data Source
AI summary
The technology disclosed relates to motion capture and gesture recognition. In particular, it calculates the exerted force implied by a human hand motion and applies the equivalent through a robotic arm to a target object. In one implementation, this is achieved by tracking the motion and contact of the human hand and generating corresponding robotic commands that replicate the motion and contact of the human hand on a workpiece through a robotic tool.