跨时代的AI新品——JetMax机械臂,带来人工智能新玩法!

幻尔科技发布JetMax智能视觉机械臂,该产品基于Jetson Nano主板,融合人工智能与桌面机械臂,实现机器学习应用。JetMax在海外众筹成功,受到好评。其开放的AI创意项目涵盖人脸识别、物品抓取、智能监控等多个领域,适用于学习、研究及工业模拟。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

作为一家秉承初心的AI教育机器人公司,我们一直为大家提供各种有趣且开源的AI机器人产品。

2020年下半年,我们幻尔的工程师们打算在机器人上注入更多高级的人工智能元素,打造出一个跨时代的、更高级的AI机器人系列,来帮助大家更好地学习机器人技术和人工智能技术,更方便的将AI创意变成现实!

在幻尔工程师小哥哥们的努力下,经过近一年的打磨,这个系列的首款产品—JetMax智能视觉机械臂。

在海外Kickstarter平台上圆满完成众筹,并受到了海外媒体和粉丝的广泛好评!

这款AI视觉机械臂-JetMax,是真正做到将人工智能与桌面机械臂完美结合的产品,它基于Jetson Nano主板开发,将其算力方面的优势发挥到了极致!

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通过机器学习,我们用JetMax机械臂开发出了海量的AI创意项目,并计划将所有创意玩法全

一种人工职能的机械的实物report。The intelligent classification robotic arm is an automated system that can automatically capture and classify objects. It uses PC as control system to realize automatic recognition and classification of objects through deep learning. It has real-time synchronization, controllability and intelligence. CNN Intelligent robotic arms are high-tech automated production equipment that can be programmed to perform a variety of expected tasks. The goal of this technology is to be applied in the machinery industry, which includes simple, repetitive or harsh conditions. Using intelligent arms to replace human labor improves work efficiency, which is why they have been widely used in various fields. In this project, the design of an intelligent classification robotic arm based on STM32 microcontroller is introduced. We combine the robotic arm with an image classification algorithm based on deep learning. On the PC end, we use the Convolutional Neural Network (CNN) to classify images and control the arm by coordinating STM32 microcontroller with the PC to achieve the objectives. In this design, we use a camera to capture the object, and realize image classification using the trained convolutional neural network. The PC then sends commands to the robot arm through serial communication, which completes item transfer. In this paper, we first introduce the various hardware and algorithms applied in the system in detail, and propose specific implementation schemes. Next, we show the test results for the entire project and analyze the test results to arrive at some of the causes of the errors. The design can be applied to industrial environments to complete different classification problems, reduce human burden, and improve classification accuracy and efficiency. With adjustments, this project will hold a wide application prospect for the development of intelligent societies in the future.
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