AI and Robotics Educational Products, Creating an Innovative Education Ecosystem

FAQ

The AI  Experimental Box is suitable for a wide range of educational fields, including:

  1. Artificial Intelligence: It provides an ideal environment for AI fundamentals, machine learning, and deep learning education.
  2. Robotics: It can be used to teach robotic control systems, robot perception, and robot programming.
  3. Computer Vision: The experimental box includes computer vision systems, making it suitable for teaching visual perception and image processing.
  4. Embedded Systems: The box helps in teaching embedded system development and IoT integration.
  5. Data Science: It supports data processing, analysis, and visualization, making it suitable for data science education.
  6. Automation and Industry 4.0: The box integrates with robotics and AI, which is highly relevant to teaching automation and Industry 4.0 concepts.
  7. Electronics and Electrical Engineering: The experimental box can be used to teach sensor integration, circuit design, and signal processing.

Yes, the AI Experimental Box comes with a comprehensive set of course materials, including:

  1. Experiment Manuals: Detailed instructions for carrying out experiments, suitable for different educational levels.
  2. Course Resources: Pre-designed curriculum resources tailored to AI, robotics, and machine learning education.
  3. Code Samples: Example codes for students to experiment with and understand core concepts.
  4. Project Guides: Step-by-step guides for various projects, helping students to apply theoretical knowledge to real-world scenarios.
  5. Tutorials: Educational tutorials and learning modules covering key topics like computer vision, AI algorithms, and robotics.

Yes, both the hardware and software of the AI Experimental Box are fully customizable and open for secondary development.

  1. Hardware Customization: The box is designed with open-source hardware components, allowing users to modify or add new modules according to their specific needs. The hardware interfaces support easy integration with external devices like sensors, cameras, and additional robotics components.

  2. Software Customization: The software is built on an open-source platform, with accessible source code. Users can modify the code, create new algorithms, and develop custom applications. It also supports popular programming languages like Python, enabling further development in machine learning, computer vision, and robotics.

  3. SDK and API: An SDK and API are provided, which allow users to integrate their own software, control hardware components, and develop specialized functions for their applications. This makes the experimental box a flexible platform for both learning and research.

Yes, the operating system used in the AI Experimental Box is open-source. It supports a variety of development environments and tools.

  1. Operating System: The experimental box runs on a Linux-based open-source operating system, providing users with full access to the system’s source code for custom modifications.

  2. Supported Development Environments:

    • Python: Python is the primary programming language supported, making it easy for users to develop machine learning, artificial intelligence, and robotics applications.
    • OpenCV: For computer vision tasks, OpenCV is supported for image processing and vision-based tasks.
    • TensorFlow/PyTorch: These popular machine learning frameworks are supported for AI model development and training.
    • ROS (Robot Operating System): ROS is supported for robotics development, allowing integration with various sensors, actuators, and robotic systems.
    • Jupyter Notebooks: Ideal for teaching and experimenting with AI concepts, users can directly code and run AI algorithms in an interactive environment.
  3. Customization: As the operating system is open-source, users can modify and adapt it to suit their specific needs, whether for research, education, or product development.

Yes, the AI algorithm library built into the AI Experimental Box supports the training and deployment of deep learning and machine learning models. It offers various commonly used algorithms, including but not limited to object recognition, target detection, face recognition, and speech processing. This allows students and researchers to train and test relevant models. Additionally, the experimental box supports programming languages like Python and is compatible with deep learning frameworks such as TensorFlow and PyTorch, enabling customized development as per user requirements.

The AI Experimental Box supports software upgrades through network connection.  We offer long-term software update services, including regular patches and the introduction of new features to ensure system stability and compatibility. The updates also include new AI algorithm libraries, support for deep learning frameworks, and system performance optimizations, helping users to always use the latest technology in their teaching and research activities.

Yes, the external interfaces of the AI Experimental Box support remote debugging and control. Through network interfaces and supported communication protocols, users can remotely connect to the box for debugging, monitoring, and control. This provides great convenience for remote teaching, experimental operations, and system maintenance.

Yes, the AI Experimental Box supports customized integration solutions. Based on the specific needs of the customer, we can provide custom hardware and software development, integrating specific sensors, modules, or functions to meet various teaching, research, or industry application requirements.

The AI Experimental Box supports various aspects of OEM customization, including but not limited to:

  1. Hardware Customization: Customization of hardware configurations, such as sensors, actuators, interfaces, and processors, based on customer requirements.
  2. Appearance Customization: Custom design of the enclosure, colors, and branding to align with customer needs.
  3. Functionality Expansion: Integration of additional modules or systems, such as machine vision, speech recognition, etc.
  4. Curriculum Customization: Tailored experimental content and teaching resources based on educational needs, providing customized course support.

The AI Experimental Box offers various training methods to help users better understand and operate the device, enhancing their practical skills. The main training methods include:

  1. Online Training Materials: Provide detailed online tutorials, video lessons, and user manuals to help users gradually master the device from basic to advanced usage.
  2. Technical Support and Consultation: Provide long-term technical support services, including answering technical questions via phone, email, and remote meetings.
1.Can the six-axis robotic arm be integrated with AI large models?

Yes, the six-axis robotic arm can be deeply integrated with AI large models. By combining with AI large models, the robotic arm can autonomously learn and perform complex tasks. AI models can provide smarter decision-making and operational capabilities for the arm, supporting various functions from image recognition to voice control. Users can train the AI model based on their application needs, enabling autonomous task planning and operation, which further enhances the arm’s flexibility and intelligence.

Yes, the six-axis robotic arm can be integrated with various external devices, including sensors, vision systems, voice modules, and more. With open interfaces and control systems, the robotic arm can seamlessly interface with sensors (e.g., temperature and humidity sensors, torque sensors) as well as vision systems (e.g., 2D/3D cameras, depth sensors), enabling more complex tasks. This integration allows the robot to be used in applications such as smart manufacturing, automated production lines, object recognition, and precise grasping, enhancing its intelligence and operational accuracy.

Yes, the control system of the six-axis robotic arm is open source. We provide the complete open-source code, including motor control, kinematic algorithms, sensor interfaces, and more. Users can customize and develop the system according to their needs. This allows developers to deeply understand and modify control algorithms, extend and optimize functionality, and meet personalized application requirements. The open-source control system significantly enhances flexibility for researchers, educators, and engineers, supporting secondary development.

Yes, the robotic arm is fully compatible with the Robot Operating System (ROS). It supports seamless integration, allowing for efficient programming, motion control, and communication with other robots or systems in ROS-based environments. This makes it ideal for research, development, and advanced robotic applications.

1.What is the warranty period for the products? Do you offer long-term maintenance services?

We provide a 1-year warranty for all our products. During this period, if any quality problems occur, we will provide free repair or replacement services. In addition, we also provide long-term maintenance services, including regular inspections, software updates, technical support, etc.

The products we provide support remote upgrades. We ensure that we provide long-term technical support and upgrade services during the product life cycle to maintain the latest functions and performance of the products.

However, hardware or complex system updates may require the product to be returned to the factory for upgrade.

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