neroae.blogg.se - .gz serial cloner

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Another good candidate is the Avnet - Ultra96-V2 Board which has similar characteristics.

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The hardware selected for this project is the Xilinx KRIA KV260 board, which was awarded as free hardware in this challenge. The weight of the assembled ATRover is around 6Kg.

The chassis is modular, so it is possible to reuse for example the KV260 enclosure for future projects. The ATRover chassis is a custom-made 3D printed model. An that is just triangulation, PID motor control, route planning, and context awareness by themself are quite complex, doable but limited, and space-constrained - and this is why and where AI/ML (CNN) keeps growing in many fields. In the long term, my final goal is to explore and implement a fully autonomous robot with self-learning capabilities, where the required effort for initial calibration is minimized.Īs an example, you can watch OpenCV Python videos from Clayton Darwin where he does a good explanation of the required process to do triangulation using two cameras and OpenCV. Stereo Vision, DC motor control, route planning, and navigation require a lot of calibration.

TTGO-T1 board (slave) for Dual DC motor control.

KRIA KV260 board (master) with AI Object Identification with YOLOv4 and OpenCV Stereo Vision for distance estimation running Ubuntu with PYNQ.

The KV260-ATROVER (mini) has the following features:

Go into AI (Artificial Intelligence) and ML (Machine Learning).

Learn about the Xilinx Zynq Ultrascale+ devices.

My objectives when joining the challenge were: So I decided to step back, and start with a basic ATRover (AuTonomous Rover) and the KRIA KV260-ATRover mini version was born - the prototype of an autonomous self-driving mini-rover based on the Kria KV260 Vision AI Starter Kit (Zynq Ultrascale+)

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But after starting from almost zero knowledge on both Xilinx Zynq Ultrascale+ and AI/ML (Artificial Intelligence/Machine Learning) I run out of time, plus I live in Canada, so a lawnmower was out of the question during winter.

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I started working around a full autonomous acreage lawn mower which I wanted to have an FPGA (just because), when I came across the Adaptive Computing Challenge 2021 on HacksterIO - and that was my application for free hardware. With the passing of the years, I have enjoyed the benefits of keeping my floors clean (I suffer a lot from dust allergies), but with the passing of the years, I keep seeing the same problems - which became worst when your family grows and your kids behave like kids.Īnd then we moved to a rural house outside Ottawa, and cutting the grass although relaxing, is time-consuming. First, it started with my personal experience with some robot vacuums which became a relation of love and not-that-much hate. The idea of having a self-driving vehicle platform has been going around my head for a long time now. Vitis-AI v1.4 and v1.4.1 share the same database, and I found that the information available is more complete on v1.4.1 Readers are welcome to go further, as new releases came with significant improvements. ⚠ All links and references are set to match the tools version used in this project, which most of the time are not the latest. ⚠ This project uses high DC amperage which can be ☠ - please use extreme caution. As part of the application process, I received a free Kria KV260 AI Starter Kit + Basic Accessory Pack. A copy of this document and the corresponding project files are located at the KV260-ATRover GitHub repository.

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Most of the code and 3D models are under The MIT License | Open Source Initiative or Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) unless specified to the contrary. A journey through machine learning and Xilinx Kria KV260 Vision AI Starter Kitĭisclaimer: The following project was done for the Adaptive Computing Challenge 2021.