Depth Sensor Verification for Robot Exclusion Zone Accuracy
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Solution Overview
Problem
Existing systems for monitoring shared workspaces between humans and robots lack precision and reliability in determining the exclusion zone due to inaccuracies in robot position and velocity data, which can lead to unsafe conditions.
Innovation Solution
A system and method that uses sensors to independently verify the robot's position and velocity data by comparing it with data obtained from the robot controller, ensuring the accuracy of the exclusion zone through three-dimensional monitoring and analysis, and taking safety actions if discrepancies are found.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If three-dimensional optoelectronic sensors are used to monitor workspace, then measurement precision and adaptability improve, but device complexity and computational requirements increase
Solution Approach 1:
The patent introduces an intermediary verification system consisting of depth sensors and analysis module that independently verifies robot position data by comparing sensor-derived positions with controller-reported positions. This intermediary layer validates the reliability of position information without requiring complete system redesign, thus improving measurement precision while managing device complexity through modular addition.
2Reliability
If exclusion zone is defined with large safety margin, then reliability of safety protection improves, but productivity decreases due to reduced workspace
Solution Approach 1:
The patent implements dynamic exclusion zones that adjust in real-time based on verified robot position and velocity data. Instead of static large safety margins, the system continuously updates the exclusion zone boundaries to match the actual robot state, maintaining reliable safety protection while maximizing workspace utilization by reducing unnecessary restrictions.
Solution Approach 2:
The verification system provides feedback on the actual reliability of position data, enabling the exclusion zone to be dynamically adjusted. When position data is verified as reliable, the exclusion zone can be tighter; when uncertainty is detected, the zone expands automatically, thus maintaining safety reliability while optimizing productivity through data-driven adjustments.
3Ease of operation
If robot position data is used directly from controller, then ease of operation improves, but measurement precision deteriorates due to data inaccuracies
Solution Approach 1:
The system maintains ease of operation by continuing to use controller position data while adding a feedback verification loop. Depth sensors independently measure robot position, and the analysis module compares this with controller data to verify accuracy. This feedback mechanism preserves the simplicity of using controller data while correcting for inaccuracies through independent verification.
Solution Approach 2:
An intermediary verification layer is introduced between the controller and the exclusion zone calculation system. This intermediary uses depth sensors to independently assess robot position and validates whether controller data is reliable, thus improving measurement precision without eliminating the ease of accessing controller data directly.
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
This approach enhances the reliability of safety protocols by ensuring a precise and dynamic exclusion zone, reducing the risk of human injury by accurately tracking the robot's position and anticipating potential collisions.
Implementation Method 1
depth sensor that captures depth information of the robot
Data Source
AI summary
The disclosure relates to a system and method for verifying robot data that is used by a safety system monitoring a workspace shared by a human and robot. One or more sensors monitoring the workspace are arranged to obtain a three-dimensional view of the workspace. Raw data from each of the sensors is acquired and analyzed to determine the positioning and spatial relationship between the human and robot as both move throughout the workspace. This captured data is compared to the positional data obtained from the robot to assess whether discrepancies exist between the data sets. If the information from the sensors does not sufficiently match the data from the robot, then a signal from the system may be sent to deactivate the robot and prevent potential injury to the human.