Jetson Nano Jetbot

Jetbot is a multi-functional AI smart robot car based on NVIDIA JESTON NANO that responds quickly to a variety of Al visual gameplay. The vehicle is made of green aluminum alloy, coupled with a unique mechanical structure that makes it different in appearance from other cars. Equipped with 3-degrees of freedom lifting platform and 8 million HD camera, which can real-time view surrounding scenes. And it can accomplish Automatic Avoiding, Object Tracking, Color Recognition / tracking, detecting desktop edge to anti-drop, etc. You can also train a variety of different runway models and control JetBot complete the autopilot function. Programmable RGB strips are also designed on both sides to light up high-brightness colorful light in dark environments. Users can remote control it by APK or Handle, and we will provide a lots of reference tutorials for users.

Yahboom JetBot with HD Camera Coding with Python for Jetson Nano

Artificial intelligence is the direction of technological development. Jetbot is a creative learning kit that combines AI and robots. Building, learning and remote control operation robots has never been more fun!【This is a very challenging robot kit, Yahboom recommends that people with a LINUX knowledge base order】

Professional Robot Designed for JETSON NANO (JETSON NANO Board is NOT Included)

• Handle control
• APP Control
• APP Page
• Handle Control

Autopilot:

Analyze the current road environment in real time through the AI framework, neural network, and AI pre-training model and identify the courtesy object to automatically plan the driving route of the current road for driving.

Color Tracking:

Motion tracking of captured heads or selected recognized colors in real time by a high-precision algorithm, then move along with the recognized color.

Object Following:

The AI framework, neural network and AI pre-training model are used to identify the selected objects in the current field of view in real time, and the selected tracking objects are followed in real time by the position analysis algorithm and the high-precision motion algorithm.

Automatic Obstacle Avoidance:

Through self-training or using pre-trained multi-environment AI models up to 217M in size, the neural network can be used to make a steering judgment on the front environment of the current motion trajectory in real time to realize the function of dropping and avoiding obstacles.