https://uto.asu.edu/events/Robo%20Hackathon
The primary goals of the hackathon are to train and challenge the participating students by:
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Applying their computer science and engineering knowledge and programming skills in robotics and AI application development;
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Learning the latest technology and platform in robotics and AI Programming;
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Exercising the engineering design process of design, implementation, simulation, and prototyping.
By the end of the hackathon, students should have:
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Successfully assembled SparkFun JetBot AI Kit Powered by NVIDIA Jetson Nano
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Developed a robotics application in AWS RoboMaker simulation environment and deployed the application to the physical SparkFun JetBot
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Trained the machine learning model to recognize the mascots of each participating school and redeploy the trained model to the robot
The winning team is the team that completes all missions with the highest score.
[More info] (https://uto.asu.edu/sites/default/files/robo_hackathon_2019.pdf)
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Assemble JetBot
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Bring JetBot to life (have the JetBot spin on its own)
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Bring JetBot to life Part 2 (have the JetBot navigate the track)
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Have JetBot navigate the track and recognize school mascots
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Design, develop and deploy new capabilities to JetBot
I: Instructions to install WIFI card on the Jetson Nano board
Please follow Step 7 and Step 8 of following instructions https://github.com/NVIDIA-AI-IOT/jetbot/wiki/hardware-setup#step-7---remove-jetson-nano-module-from-developer-kit
- In rooms 214, 221 or 222 find location with a monitor, keyboard, mouse and HDMI cord setup.
- Boot up JetBot by plugging into power
- Connect monitor, keyboard and mouse to JetBot
- Top right, select wifi icon > select "ASU Event" wifi and insert password
- Once connected select gear icon in top right corner and select "shut down" option
II: Instructions to assemble Sparkfun JetBot kit
Assembly guide for Sparkfun Jetbot
III: Test bot using these instructions
Follow steps for Example 1 - Basic Motion
Follow AWS Robomaker instructions
I: Robomaker instructions: TBD
II: To train new track navigation and mascots recognition models, follow
Follow AWS Machine Learning Instructions
Sky is the limit!
Sample video https://www.youtube.com/watch?v=Otbb4mF0jyg