Easily display the robot's status! It can be used for selecting and arranging heavy load materials.
The "CR12" is a self-learning collaborative robot that can adapt to various application scenarios. With wireless interconnectivity, it comprehensively understands everything. By using a high-performance wireless network card, the transmission speed can reach up to 433 Mbps. It features various loads, high cost performance, and safe collaboration, along with functions such as flexible deployment, one-handed demonstrations, collision detection, and trajectory reproduction. [Features] - Real-time dynamic monitoring at 0.01 seconds - Benefits of long-term high efficiency - Immediately usable with flexible deployment - Equipped with our proprietary smart interactive panel on the top of the sixth axis - Features a unique lighting system for easy display of the robot's status *For more details, please refer to the PDF materials or feel free to contact us.
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basic information
【Application Scenarios】 ■ Loading/Unloading of Machine Tools ■ Selection and Arrangement of Heavy Load Materials ■ Depalletizing/Palettizing ■ Material Processing (Polishing/Grinding) *For more details, please refer to the PDF document or feel free to contact us.
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Applications/Examples of results
【Applicable Industries】 ■Food Chemistry ■Furniture and Appliances ■Metal Processing ■Automotive Parts *For more details, please refer to the PDF document or feel free to contact us.
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GRIPS Corporation is focused on two main areas: technology for industry and education for the next generation of talent. For industry, we propose systems that do not rely on specific manufacturers, utilizing technologies such as robotic arms and single-board computers, allowing customers to develop and improve their own systems. For education, we conduct advanced technical training sessions for engineers and develop STE(A)M educational materials for elementary and middle school students. We aim to secure educational opportunities and provide high-quality materials for a wide range of generations, from those currently "working" in the field to those who will "work" in the future.