Product & Technology

Minimum Viable Product
The product combines 3D printing and metal components for durability, with custom electronics for seamless hardware integration. It features an NVIDIA Jetson for strong performance and supports real-time wireless communication.

Software MVP
Our Software MVP offers an advanced tennis simulation and training platform, featuring adaptive training UI, realistic match simulations, and an annotation tool for player behavior analysis.

Hardware Iteration 1
Our hardware PoC is a fully functional prototype with a LED board, fast and accurate shot placement, digital control, and wireless communication.

Minimal Viable Product:
- Manufacturing using a combination of 3D printing and metal sheets for durability and precision.
- Includes custom PCB design and prototyping for efficient hardware integration.
- Runs on a NVIDIA Jetson, ensuring robust performance and versatility.
- Wireless communication implemented using MQTT, enabling seamless real-time interactions.

Hardware Design
The hardware design focuses on robustness and precision. Key components include precision motors and servos for shot execution and ball feeding, a 180-degree camera for real-time tracking, and two DC motors for dynamic shot variations. A 100px x 100px LED matrix provides visual feedback, displaying court locations of the virtual opponent and the score.

Circuit Design
The circuit design ensures a responsive and efficient system with:
- Precision motor drivers for high accuracy.
- A Raspberry Pi 4 for real-time processing.
- Wireless communication via MQTT.
- High-speed signal pathways to minimize latency.

PCB Design
The PCB design integrates all components efficiently, featuring:
- Multi-layered PCBs for robust connections.
- Optimized layout for heat dissipation.
- Connectors for easy interfacing with sensors and actuators.

HARDWARE ITERATION 1:
- Fully functional hardware prototype
- constructed using Catia V5
- manufactured using 3D Printing & Laster Cutter
- incl. PCB Design & Prototype
- running on Raspberry Pi 4
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