3D TOF Collision Avoidance With Floor-Based Self-Diagnostics
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Solution Overview
Problem
Existing 2D laser scanner-based collision avoidance systems have limited field of view and safety integrity level (SIL) ratings insufficient for critical safety applications, necessitating improved reliability and accuracy in obstacle detection for autonomous vehicles.
Innovation Solution
Implementing a 3D time-of-flight (TOF) camera with self-diagnostic capabilities to ensure accurate distance measurements and adjust for inclination, enhancing the safety rating of collision avoidance systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a 2D laser scanner is used for collision avoidance, then the device complexity is reduced, but the measurement precision and safety integrity level are insufficient for critical applications
Solution Approach 1:
The patent transitions from 2D laser scanning to 3D time-of-flight imaging, adding a temporal dimension to distance measurement. The TOF camera captures depth information for all pixels simultaneously, providing three-dimensional spatial data that improves measurement precision and enables more accurate obstacle detection while maintaining manageable system complexity through integrated sensor design
2Volume of moving object
If the field of view is expanded to improve obstacle detection coverage, then the volume for obstacle detection increases, but the measurement precision may be compromised
Solution Approach 1:
The patent divides the detection space into multiple regions corresponding to different pixel fields of view. Each pixel or pixel group has its own distance measurement and protective field, allowing the system to monitor a large overall volume while maintaining precise measurements through segmented, pixel-level distance calculations and independent protective field definitions
3Reliability
If safety integrity level is increased to meet SIL 3 requirements, then the reliability improves, but the device complexity and diagnostic requirements increase
Solution Approach 1:
The patent implements comprehensive feedback mechanisms including real-time distance measurement verification, protective field monitoring, and diagnostic sequences that continuously check system integrity. The level component provides feedback on inclination changes, and the system generates control outputs based on measured distances compared to protective field boundaries, ensuring SIL 3 reliability through multiple feedback loops
Solution Approach 2:
The system performs self-diagnosis by monitoring its own components and measurements. The diagnostic sequences automatically verify system integrity, check distance measurement accuracy, and detect faults without external intervention. The system can identify and report diagnostic faults autonomously, reducing the need for external diagnostic complexity while maintaining high reliability
4Measurement precision
If the protective field is adjusted to compensate for inclination changes, then the measurement precision is maintained, but the device complexity increases
Solution Approach 1:
The patent introduces a level component as an intermediary sensor that measures inclination changes. This intermediary device provides inclination data that is used to adjust the protective field definition, acting as a mediator between the physical inclination of the system and the virtual protective field boundaries, maintaining measurement precision through intermediate compensation measurements
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 3D TOF camera system provides a larger volume for obstacle detection, improves reliability, and ensures safety-rated collision avoidance by verifying measurement accuracy, meeting SIL 3 requirements for critical applications.
Implementation Method 1
point cloud data is generated for the viewing space based on reflected pulses received at a photo-detector array of the 3D TOF camera. The point cloud data comprises distance values representing distances from respective pixels of the photo-detector array to corresponding points on surfaces within the viewing space
Data Source
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Figure 3A~3B
AI summary
A safety system for autonomously mobile machinery (e.g., automated guided vehicles) achieves safety-rated collision avoidance functionality by detecting objects located in the field of view of a three-dimensional (3D) time-of-flight (TOF) vision system or camera. Incorporating a 3D TOF camera into a collision avoidance system allows a large volume to be monitored for object intrusion, improving reliability of object detection. To ensure reliability of the safety system's obstacle detection capabilities, the collision avoidance system also includes self-diagnostic capabilities that verify the accuracy of the TOF camera's distance measurements even in the absence of a test object within the camera's field of view. This is achieved by tilting the TOF camera downward to include the floor within the camera's field of view, allowing the floor to act as a test object that can be leveraged to verify accuracy of the camera's distance measurements.