CanEduDev offers a modular hardware and software ecosystem for developing and testing robotics and automotive control systems. Its CAN-based platform integrates with ROS2, accelerating prototyping and automation for research and industrial applications.
Funding
Funding not disclosed
Founders
Product
Problem
Developing and testing advanced robotics and automotive control systems requires specialized hardware and software integration. Traditional development cycles can be lengthy and costly, especially when dealing with complex communication protocols like CAN and the need for real-time data processing.
Solution
CanEduDev provides a modular ecosystem of hardware components and software tools designed to streamline the development, testing, and automation of robotics and automotive control systems. The platform leverages CAN-based communication and integrates with ROS2, offering a flexible and cost-effective solution for both academic research and industrial prototyping. This approach accelerates the engineering process by providing a robust foundation for prototyping, simulation, and real-world application testing.
Target Audience
The primary users are university research departments, engineering students, and industrial R&D teams focused on robotics, autonomous systems, and automotive control development.
Features
- Modular Rover platform with a durable, lightweight anodized aluminum chassis for easy component integration.
- CAN-based control system with dedicated CAN nodes for motor, steering, and battery monitoring, enabling high-speed data communication.
- ROS2 compatibility for seamless integration with robotics middleware, facilitating advanced algorithm development and real-time data acquisition.
- Wheel Speed Module for precise, real-time velocity data transmission over CAN, supporting vehicle dynamics and odometry research.
- Motor Control Board supporting brushed and brushless motors with PWM, Dshot, and CAN compatibility, featuring AM32 firmware and modular FET design.
- Integrated compute platforms (e.g., Nvidia Orin) for AI-driven sensor fusion, path planning, and machine learning capabilities.
- Support for various sensors including LiDAR, cameras, ultrasonic sensors, and GNSS/IMU for comprehensive environmental perception and localization.
- Open-source software and detailed hardware schematics to facilitate customization and rapid iteration.