Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Hexapod | 51 | 6 years ago | November 18, 2023 | Go | ||||||
Golang program for my junky hexapod | ||||||||||
Mjrobot Web Rpi Robot | 25 | 7 years ago | 1 | HTML | ||||||
HTML Pages and scrips for the Web controlled robot based on Raspberry Pi | ||||||||||
Nox_robot | 23 | 4 years ago | 2 | gpl-3.0 | C++ | |||||
Nox robot project | ||||||||||
Peabot Library | 14 | 7 years ago | 3 | mit | C | |||||
Peabot: quadruped robot library for Raspberry Pi | ||||||||||
Robot_motion | 13 | a year ago | gpl-3.0 | Python | ||||||
Turn an RPi into a variable speed ESC for robots & electric vehicles with brushed DC motors. | ||||||||||
Rpi_general_robotics_toolbox_py | 12 | 2 | 9 months ago | 11 | August 10, 2023 | bsd-3-clause | Python | |||
Robotics Python toolbox containing mathematical functions and utilities | ||||||||||
Mockbot | 11 | 5 years ago | 1 | HTML | ||||||
A homebrew robot - custom hardware and brickpi | ||||||||||
Fsrover | 10 | 7 years ago | JavaScript | |||||||
Raspberry PI robot powered with FSharp | ||||||||||
Ajnodebot | 9 | 9 years ago | mit | JavaScript | ||||||
4WD RPI + Arduino based node bot . | ||||||||||
Self Driving Robot Using Neural Network | 9 | 3 years ago | mit | Python | ||||||
This project introduces the autonomous robot which is a scaled down version of actual self-driving vehicle and designed with the help of neural network. The main focus is on building autonomous robot and train it on a designed track with the help of neural network so that it can run autonomously without a controller or driver on that specific track. The robot will stream the video to laptop which will then take decisions and send the data to raspberry pi which will then control the robot using motor driver. This motor driver will move the robot in required directions. Neural Network is used to train the model by first driving the robot on the specially designed track by labeling the images with the directions to be taken. After the model is trained it can make accurate predictions by processing the images on computer. This approach is better than conventional method which is done by extracting specific feature from images. |