Vision-Guided Ordered Loading of Bicycle Seat Parts
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Project Background
Amid industrial digital transformation, traditional labor-reliant production models are struggling with workforce shortages, rising costs, and inflexibility. DexForce's spatial and embodied intelligence suite, powered by the DexVerse™ engine, brings efficiency and intelligence to key processes, helping customers cut costs, boost productivity, and strengthen core competitiveness.
Project Challenges
⦁ Highly reflective and curved seat parts make imaging challenging.
⦁ Tightly stacked seat parts complicate visual recognition.
Case Video
Core Values
Complete Imaging of Highly Reflective Workpieces
Guaranteed by the high-precision 3D camera that can generate high-fidelity images for highly reflective seat parts.
Precise Positioning
Enabled by advanced vision algorithms that accurately recognize and locate highly reflective parts and guide robots for precise handling.
Product Recommendations
Higher Precision 3D Imaging Capability
Based on self-developed high-precision 3D imaging algorithms and HDR technology, achieving a Z-axis measurement accuracy of up to 50 µm at a working distance of 600mm, applicable to industrial inspection fields with high precision requirements
Integrated Imaging and Perception
Equipped with NVIDIA Jetson Nano core, built-in computing and control algorithms, endowing the camera with point cloud analysis capabilities, optimizing image quality; fast speed, more stable, enabling lightweight, edge deployment
Industrial-grade Design, Addressing Real-world Scenario Challenges
IP65 dustproof and waterproof design; wide temperature design, supports operation in environments from -10°C to 50°C, adaptable to most industrial scenarios; passed certifications such as EU CE, RoHS, US FCC, etc., better coping with harsh industrial environments like humidity, electromagnetic interference, dust
Extreme Working Condition Algorithm Development
Exposure modes specifically optimized for highly reflective object surfaces, effectively improving the camera's accuracy and stability in measuring highly reflective surfaces. Proprietary compensation algorithms can effectively reduce depth measurement errors, minimizing the impact of object surface textures on depth images
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