Robot Load Monitoring for Human Contact and Collision Detection
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Solution Overview
Problem
Existing robot monitoring systems fail to effectively distinguish between conscious human interactions and unexpected collisions, leading to inadequate safety responses in shared work environments, as they cannot accurately differentiate between various external loads and their causes.
Innovation Solution
A method utilizing a force-torque sensor and a mathematical dynamic model to calculate and differentiate between expected and measured cutting loads, employing signal processing techniques to identify signal characteristics and qualify the cause of external forces, allowing for distinct reactions to human interaction and collisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If camera-based collision detection systems are used to trigger emergency stop whenever a human approaches or touches the robot, then safety response is activated, but the system cannot distinguish between conscious human interactions and unexpected collisions
Solution Approach 1:
The external forces acting on the robot are segmented into different frequency components using signal filtering. Low-frequency components correspond to conscious human interactions, while high-frequency components indicate unexpected collisions. This segmentation enables differentiated safety responses based on the type of contact detected.
Solution Approach 2:
The system changes the parameter of force signal analysis from simple presence detection to frequency-based characterization. By analyzing the frequency characteristics of external forces, the system can distinguish between different types of human-robot interactions and trigger appropriate responses.
2Measurement precision
If force-torque sensors are installed at the robot base to measure cutting loads, then external forces can be detected, but the system cannot differentiate between process-related contact, human contact, and unexpected collision
Solution Approach 1:
The system applies dynamic signal processing techniques to the force sensor data. By analyzing the temporal and frequency characteristics of the force signals, the system can identify the cause of external loads. Conscious human interactions produce distinct dynamic patterns compared to unexpected collisions, enabling accurate cause identification.
Solution Approach 2:
The system continuously monitors the force signals and provides feedback about the type of interaction detected. This feedback mechanism allows the robot controller to adjust its response based on the identified cause, whether it be conscious human interaction requiring collaborative mode or unexpected collision requiring emergency stop.
3Productivity
If the robot operates in shared workspace with humans, then collaboration efficiency is improved, but safety risks from accidental collisions increase
Solution Approach 1:
The system applies different safety response qualities to different types of detected interactions. For low-frequency signals indicating conscious human interaction, the system maintains collaborative operation. For high-frequency signals indicating unexpected collision, the system triggers emergency stop. This localized quality adjustment optimizes both safety and collaboration efficiency.
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
Enables safe and efficient monitoring of robots in human-robot collaboration by distinguishing between conscious human interactions and unexpected collisions, triggering appropriate safety responses such as emergency stops or controlled movements, thereby enhancing safety and operational efficiency.
Implementation Method 1
The cutting loads are recorded at a reference point (13) on the robot using a sensor (20), in particular a force-torque sensor
Data Source
Figure 1
AI summary
The invention relates to a monitoring method for a robot. The actual internal loads are measured with a sensor at a reference point of the robot and are compared with the expected internal loads. The expected internal loads are calculated using the movement of the robot and a dynamic model. It is possible to estimate which external forces act on the robot by comparing the actual and expected internal loads. The signal characteristics of different signal components in the signal of the estimated external forces are used to differentiate between said signal components.