Releases: NVIDIA-AI-IOT/jetbot
Releases · NVIDIA-AI-IOT/jetbot
Support JetPack 4.5
Platform | JetPack Version | JetBot Version | Download | MD5 Checksum |
---|---|---|---|---|
Jetson Nano 2GB | 4.5 | 0.4.3 | jetbot-043_nano-2gb-jp45.zip | e6dda4d13b1b1b31f648402b9b742152 |
Jetson Nano (4GB) | 4.5 | 0.4.3 | jetbot-043_nano-4gb-jp45.zip | 760b1885646bfad8590633acca014289 |
Changes
Added
Added LocalController class which allows users to directly connect controller to JetBot
Fix for 3rd party motor variant
Adds a small fix to enable later revision of waveshare JetBot.
Platform | JetPack Version | JetBot Version | Download |
---|---|---|---|
Jetson Nano 2GB | 4.4.1 | 0.4.2 | jetbot-042_nano-2gb-jp441.zip |
Jetson Nano (4GB) | 4.4.1 | 0.4.2 | jetbot-042_nano-4gb-jp441.zip |
Jetson Nano 2G / Docker
This release is the first to support JetBot 2G as well as facilitate docker.
Platform | JetPack Version | JetBot Version | Download |
---|---|---|---|
Jetson Nano 2GB | 4.4.1 | 0.4.1 | jetbot-041_nano-2gb-jp441.zip |
Jetson Nano (4GB) | 4.4.1 | 0.4.1 | jetbot-041_nano-4gb-jp441.zip |
Support JetPack 4.3
JetBot SD card image based on JetPack 4.3 (required for B01 revision of Jetson Nano Developer Kit ):
https://drive.google.com/open?id=1G5nw0o3Q6E08xZM99ZfzQAe7-qAXxzHN
Object Detection Engine:
https://drive.google.com/open?id=1KjlDMRD8uhgQmQK-nC2CZGHFTbq4qQQH
v0.3.0
SD Card image
Found here: jetbot_image_v0p3p0.zip
- PyTorch 1.0 pre-installed
- TensorFlow 1.13 pre-installed
- 4GB swap enabled
- default power model 5W
- Jupyter Lab service installed
- Status display service installed
Python package
Heartbeat
class to determine disconnects from browserCamera
class to generate eventful camera framesRobot
class for controlling robot locomotionMotor
class for event based motor controlObjectDetector
class for detecting objects using the subpackagejetbot.ssd_tensorrt
TRTModel
class for abstracting TensorRT model execution on numpy arrays
Notebooks
Robot
- basic motion teaches programmatic robot control
- teleoperation demonstrates video streaming and gamepad control
- collision avoidance
- data collection to collect
free
andblocked
labels - live demo to demonstrate simple wander / avoid obstacles
- data collection to collect
- object following
- live demo to demonstrate multi network COCO object following and simultaneous collision avoidance
Host
- collision avoidance
- training to demonstrate training image classification network with PyTorch