Robotic Motion Imaging for Real-Time Failure Mode Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Robotic devices often encounter unrecoverable errors due to undetected failure modes during robotic motion processes, particularly in tasks like pick and place cycles, which can lead to inefficiencies and the need for human intervention.
Innovation Solution
Implement a computer vision system with multiple cameras positioned strategically to capture images at various points along the robotic motion trajectory, using deep learning to detect failure modes in real-time by analyzing images from different angles and using segmentation techniques to identify distinct objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a robot performs tasks autonomously without human intervention, then productivity increases, but unrecoverable errors occur due to undetected failure modes
Solution Approach 1:
The system captures images at multiple predetermined positions along the robotic motion trajectory before the robot completes its task. By proactively checking for failure modes at each position (first position, intermediary position, final position), the system detects errors early before they become unrecoverable, allowing the robot to maintain high productivity while improving reliability through preventive error detection.
2Measurement precision
If multiple images are captured at different positions to detect failure modes, then error detection accuracy improves, but system complexity increases
Solution Approach 1:
The imaging process is segmented into three distinct stages corresponding to three different robot positions: (1) capturing images before the robot attempts a task at the first position, (2) capturing images during the task execution at the intermediary position, and (3) capturing images after task completion at the final position. Each stage checks for specific failure modes appropriate to that phase, dividing the complex detection task into manageable segments that improve accuracy without overwhelming system complexity.
3Reliability
If images are captured at multiple positions along the trajectory, then failure detection capability improves, but processing time increases
Solution Approach 1:
The system performs preliminary error detection at the first position before the robot commits to a task action. By checking for failure modes in advance using images captured at predetermined positions, the system identifies potential issues early in the motion trajectory, allowing for quick corrective actions that minimize processing time while maintaining high reliability through multi-position verification.
Data Source
AI summary
Various embodiments of the present technology generally relate to robotic devices, artificial intelligence, and computer vision. More specifically, some embodiments relate to an imaging process for detecting failure modes in a robotic motion environment. In one embodiment, a method of detecting failure modes in a robotic motion environment comprises collecting one or more images of a multiple scenes throughout a robotic motion cycle. Images may be collected by one or more cameras positioned at one or more locations for collecting images with various views. Images collected throughout the robotic motion cycle may be processed in real-time to determine if any failure modes are present in their respective scenes, report when failure modes are present, and may be used to direct a robotic device accordingly.