Robotic Arm Learns Basketball Detection And Throwing
In this project, the author uses an Elephant Robotics myCobot 280 robotic arm to detect, pick up, and throw a basketball by integrating an Edge Impulse-trained computer vision model. The team trained a FOMO object-detection model on a synthetic "Sports Balls" dataset from VisionDatasets.com and validated real-time performance with a camera feed. The system demonstrates rapid prototyping for edge robotics using synthetic data and lightweight models.
Key Points
- 1Demonstrates a myCobot 280 detecting and manipulating basketballs using an Edge Impulse-trained FOMO model.
- 2Uses synthetic 'Sports Balls' dataset from VisionDatasets.com to speed training and ensure varied photorealistic samples.
- 3Enables real-time robotic pick-and-throw via lightweight FOMO detection on edge, practical for prototyping.
Scoring Rationale
Practical, reproducible robotics prototype demonstrating edge CV and dataset reuse; limited broader novelty and relies on single-source documentation.
Sources
Public references used for this report.
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