Robot Teaching via RGB-D Segmentation and EMG Force Guidance
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Solution Overview
Problem
Traditional industrial robot teaching systems face high costs and complexity due to the use of 6D force torque sensors for hand-guiding and force control, which are expensive and reduce joint rigidity.
Innovation Solution
A robot teaching system utilizing image segmentation with a deep learning network and surface electromyography (EMG) sensors to recognize articulated arms and human joints, judge contact conditions, and control robotic movements based on force direction and strength, eliminating the need for expensive sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a 6D force torque sensor is used for hand guiding and force control, then force control capability is improved, but cost and device complexity increase significantly
Solution Approach 1:
The patent replaces the mechanical 6D force torque sensor system with a combination of vision-based detection (RGB-D camera for hand pose and contact detection) and physiological signal detection (EMG sensors for force intention recognition). This substitution eliminates complex mechanical sensors while achieving comparable or superior force control capability through software-based interpretation of human intent.
Solution Approach 2:
The patent introduces EMG sensors as intermediaries that detect electrical signals from muscle contractions before actual physical contact occurs. These sensors serve as early indicators of human force intention, allowing the system to anticipate and respond to force requirements before mechanical contact happens, thereby eliminating the need for direct mechanical force measurement.
2Measurement precision
If a 6D force torque sensor is used for hand guiding, then force detection accuracy is improved, but response speed deteriorates
Solution Approach 1:
The patent uses EMG sensors to detect muscle electrical signals that precede actual muscle contraction and physical contact. By detecting these preliminary physiological signals, the system can prepare for and respond to force requirements faster than waiting for mechanical contact to occur, thereby improving response speed while maintaining accuracy through the combination of EMG and vision data.
Solution Approach 2:
The patent implements a multi-source feedback system that continuously monitors hand pose via RGB-D camera, muscle activation via EMG sensors, and contact status via vision-based detection. This comprehensive feedback loop allows the system to dynamically adjust control parameters based on real-time human intent and contact state, improving both response speed and detection accuracy through coordinated processing of multiple signal streams.
3Manufacturing precision
If a force torque sensor is embedded in the reducer output end to form a flexible joint, then kinematic position control is improved, but joint structure complexity and rigidity deterioration occur
Solution Approach 1:
The patent replaces the mechanical approach of embedding force torque sensors in joints with a software-based control system that uses vision and EMG data to achieve compliant motion. The flexible joint behavior is simulated through control algorithms that interpret human intent and adjust robot motion accordingly, eliminating the need for physical sensors in the joint structure and preserving joint rigidity while achieving position control accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides a cost-effective, simple structure for robot teaching with quick force control and accurate gesture following, reducing operational complexity and costs while maintaining precise control.
Implementation Method 1
the surface electromyography sensor acquires surface electromyography signals and inertial acceleration signals of the robot teacher
Implementation Method 2
A robot teaching system based on image segmentation and surface electromyography, comprising a RGB-D camera
Data Source
AI summary
The present invention relates to a robot teaching system based on image segmentation and surface electromyography and robot teaching method thereof, comprising a RGB-D camera, a surface electromyography sensor, a robot and a computer, wherein the RGB-D camera collects video information of robot teaching scenes and sends to the computer; the surface electromyography sensor acquires surface electromyography signals and inertial acceleration signals of the robot teacher, and sends to the computer; the computer recognizes a articulated arm and a human joint, detects a contact position between the articulated arm and the human joint, and further calculates strength and direction of forces rendered from a human contact position after the human joint contacts the articulated arm, and sends a signal controlling the contacted articulated arm to move along with such a strength and direction of forces and robot teaching is done.