Computer Vision Robotic Singulation for Heavy Item Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manual singulation in parcel and distribution centers is labor-intensive and inefficient, and robotic singulation faces challenges due to cluttered workstations and dynamic item flows, making it difficult for robotic arms to identify, grasp, and separate items effectively.
Innovation Solution
A robotic singulation system that uses computer vision and sensors to detect items, determine their attributes, and implement plans to efficiently pick and place them on a conveyor, including adapting to heavy or special handling items by adjusting paths and trajectories, and coordinating multiple robotic arms to avoid collisions and optimize throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual singulation is used, then items can be separated accurately, but labor costs increase and throughput decreases
Solution Approach 1:
The system uses computer vision to automatically detect item attributes and robotic arms to autonomously perform singulation, eliminating the need for human workers to manually separate items while maintaining high accuracy and throughput
Solution Approach 2:
Manual mechanical singulation by human workers is replaced with an automated system combining computer vision for detection and robotic manipulation for physical separation, achieving both precision and high-speed operation
2Productivity
If robotic singulation is implemented, then throughput increases, but difficulty in identifying and grasping items in cluttered workstations increases
Solution Approach 1:
The computer vision system performs preliminary detection and classification of items in the cluttered workstation before the robotic arm attempts grasping, identifying target items and their attributes in advance to facilitate successful manipulation
Solution Approach 2:
The computer vision system acts as an intermediary between the cluttered workstation environment and the robotic arm, translating complex visual scenes into structured data about item locations, attributes, and grasping opportunities
3Productivity
If robotic arms attempt to grasp heavy items, then singulation can be performed, but risk of damage or failure increases
Solution Approach 1:
The computer vision system detects item weight attributes before the robotic arm attempts grasping, allowing the system to pre-plan appropriate grasping strategies or identify alternative handling methods for heavy items
Solution Approach 2:
The system uses real-time feedback from computer vision about item attributes including weight to adjust robotic arm grasping force and strategy dynamically, preventing damage to heavy items while maintaining singulation effectiveness
Data Source
AI summary
The present application discloses a system, a method, and a computer system for detecting objects that require special handling. The method includes (i) receiving image data from one or more cameras associated with a source conveyor configured to convey items to a pick location, (ii) determining, based at least in part on the image data, that an item requiring special handling has entered the source conveyor, and (iii) providing an output indicating that the item requiring special handling has been detected.