Hey everyone, I’m jumping into the Element14 EZ-EV Challenge with a build I’m calling Project GEPARD V5.0 (Ground EV Perception and Autonomous Routing Dock).
I am John, I am currently in university specializing in Business Data Analytics and Supply Chain (BBA/BCom). In my current co-op, I spend my days designing compliance workflow pipelines, writing scripts to optimize workflows, and mapping out routing logic for utility systems.
Because of that background, when I look at an autonomous UGV and a self-charging dock, I don't just see an RC car. I see a closed-loop supply chain and a mobile telemetry node. The goal for this challenge is to demonstrate how industrial EV technologies like autonomous movement, sensor fusion, edge computing, and EMI-resistant docking can scale down into a consumer-sized prototype.
The Brains of the Operation
The core of this rover relies heavily on the sponsor-provided Arduino UNO Q. Instead of just using it for basic motor reflexes, I’ll be taking full advantage of its dual-brain architecture. The onboard STM32U585 will handle real-time motor control and sensor acquisition, while the Qualcomm QRB2210 Linux SBC will run Python and edge compute. The ESP32-S3 will sit shotgun, strictly handling video streaming to keep the UNO Q's pipeline clear of bottlenecks.
The Scope: MVP vs. The Dream If you try to build a fully sentient AI robot all at once, you usually end up with nothing working. Instead, I am treating this build as a scalable modular platform.
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The MVP (Challenge Scope): A reliable, manually driven (WASD) tracked chassis featuring a dedicated battery bay and a removable electronics tray for easy servicing. It will stream video, continuously log power/odometry telemetry to a dashboard, and execute a proof-of-concept autonomous docking handshake.
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Phase 2 (The Dream): Once the MVP foundation is locked, I will use the reserved "brain bay" space to integrate grid mapping, SLAM, and local LLM processing via a federated compute model with my laptop.
A Question for the Community
Because the UNO Q is so new to the market, deep technical resources and documentation are still pretty sparse. I need a robust way to "glue" the UNO Q (handling the heavy edge compute and motors) to the ESP32-S3 (handling the video stream).
My initial thought is to use a standard UART serial bridge or maybe a local UDP link to pass basic state commands between them, but I want to ensure absolute data integrity. Has anyone here had hands-on experience bridging the new UNO Q with an ESP board? What protocol or physical link did you find most reliable?
Oh, and for those wondering about the name: Project GEPARD. Yes, it’s named after the Flakpanzer Gepard from War Thunder. If you play, you know that SPAA is infamous for being an absolute menace that shreds both aircraft and main battle tanks alike. While my V5.0 is just shooting ultrasonic pings instead of 35mm armor-piercing belts, I’m hoping it proves to be just as relentless as a telemetry node.
Stay tuned. In the next post, I’ll drop the full parts list and dive deeper into how I'm using the ADI ADM3068E evaluation boards to build an EMI-resistant handshake for the docking station.
(Note: I tried to generate a prototype image to be as similar to my plans as possible, but the AI had a mind of its own and this is the closest one. Stay tuned, I will be posting the actual 3D CAD model in a couple of days sorry for not posting it earlier! All the parts I need have been ordered through AliExpress, fingers crossed all of them work. If not, mighty Amazon will save me.)
