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Hotwheels-Cluster 1.2
Creation of Cluster APP for SEA:ME project.
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Hotwheels-Cluster 1.2
Creation of Cluster APP for SEA:ME project.
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The Instrument Cluster module aims to develop a sophisticated instrument cluster for a model car focusing on integrating various components such as sensors, motors, and displays to create a functional and interactive system. The primary goals of the module are to enhance the driving experience, provide real-time data to the driver, and ensure seamless communication between different components.
The communication architecture of the project is based on the CAN-BUS protocol. CAN-BUS (Controller Area Network) is a robust vehicle bus standard designed to allow microcontrollers and devices to communicate with each other without a host computer. In this module, CAN-BUS is used for communication between the Nvidea board and the arduino.
This communication system and its components can be visualized as follows:
The software architecture of the module is designed to leverage the capabilities of the Nvidea board. The decision to use this board was based on its powerful GPU and AI tools, which are essential for advanced computer vision and sensor fusion tasks, providing the necessary processing power to run the main control operations for the motors and other components.
Our software architecture is simple, easy to understand and well organized allowing all team members to work on the code simultaneously and the expansion of the app for the future modules.
Unfortunately this version of the Jetson Nano doesn't provide the CAN interface necessary to read CAN messages directly. Being so we were forced to improvise and adapt the QT app to read CAN messages using SPI (Serial Peripheral Interface) pins.
This adaptation allows the system to interface with the CAN-BUS protocol through the pins on the Nvidea board guaranteeing proper communication between the components.
To ensure compatibility with the Nvidea board, the project employs a cross-compilation method from x86 to aarm64. This method involves compiling the software on an x86 machine and then deploying it on the aarm64 architecture of the board ensuring that the software runs efficiently on the target hardware.
Below is a small video of our final results.
https://github.com/user-attachments/assets/36f429f6-f533-4ab9-9822-74d56cfc7189
1.13.2