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ftcvision

Computer Vision library for FIRST Tech Challenge

gaitech-bci

Gaitech BCI is Brain computer Interfacing platform for ROS based robots, The hardware it provides is H10C, which is a high quality EEG headset. It includes handy ROS programs for carrying out Brain Robot Interfacing research

gap

Gazebo plugins for applying domain randomization

gazebo_domain_randomization

Domain randomization for gazebo simulator

gazebo_ros_demos

Example robots and code for interfacing Gazebo with ROS

gazebodomainrandom

Implementation of some Domain Randomization tools within the ROS+Gazebo framework, following the work of Tobin et al. "Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worl" (https://arxiv.org/abs/1703.06907)

haros

H(igh) A(ssurance) ROS - Static analysis of ROS application code.

hindsight-experience-replay

Implementation of HER algorithm in the bit-flipping environment.

homemade-machine-learning

🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained

image-labeling-software

Manual Image Labeling Software for a social science research project at Lee Kuan Yew Centre for Innovative Cities (LKYCIC)

kugle-matlab

Main repository for the Kugle robot project. The repository contains the MATLAB code and Simulink models for the Kugle robot developed as part of the master thesis work. This includes a non-linear Quaternion ballbot model, Sliding mode attitude controller, Quaternion Extended Kalman filter and ACADO MPC for path-following.

lane-detection

Lane Detection for autonomous driving done in OpenCV

learn-python

📚 Playground and cheatsheet for learning Python. Collection of Python scripts that are split by topics and contain code examples with explanations.

learnopencv

Learn OpenCV : C++ and Python Examples

lidar_camera_calibration

ROS package to find a rigid-body transformation between a LiDAR and a camera

line_follower_turtlebot

A repo created to simulate a line following turtlebot in Gazebo environment using ROS

linear-cpp

An attempt at a C++ tutorial.

linorobot

ROS Compatible ground robots (2WD, 4WD, Ackermann Steering, Mecanum Drive)

machine-learning-by-stanford-university

Programming assignments

machine-learning-with-python

Python code for common Machine Learning Algorithms

machine-learning-with-python-1

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

machine_learning_basics

Plain python implementations of basic machine learning algorithms

machine_learning_examples

A collection of machine learning examples and tutorials.

machine_learning_journey

This is the Curriculum for "Machine Learning Journey" By Siraj Raval on Youtube

mapping-and-localization-of-turtlebot-using-ros

The motto of the project is to gain experience in the implementation of different robotic algorithms using ROS framework. The first step of task is to build a map of the environment and navigate to a desired location in the map. Next, we have to sense the location of marker (e.g. AR marker, color markers etc) in the map, where there is pick and place task, and autonomously localise and navigate to the desired marker location. After reaching to the desired marker location, we have to precisely move towards the specified location based on visual servoing. At the desired location, we have a robotic arm which picks an object (e.g a small cube) and places on our turtlebot (called as pick and place task). After, the pick and place task, again the robot needs to find another marker, which specifies the final target location, and autonomously localise and navigate to the desired marker location, which finishes the complete task of the project.

mapping_localizationofarmarkers_navigation_ros_turtlebot

master-thesis

Robot localization and 3D mapping - Demo

master_thesis

Matlab code for our master thesis in mechatronics 2017. Code is post processing of a outdoor mobile robot, filtering GPS, odometry and IMU data to global positioning and mapping the environment.

master_thesis-1

My master thesis on the topic of Multi-Hypotheses Kalman Filter based Self-Localization for Autonomous Soccer Robots. (incomplete, img not uploaded)

master_thesis_local_planning_algorithms_in_ros

The main goal of this work is to compare several local planning algorithms (planners). The assumption is to compare, two algorithms which are already implemented in ROS environment and two selected motion planning algorithms. Based on the performed research of the available motion planning approaches, two algorithms have been selected, Potential field based algorithm and BUG0 algorithm (Chapters 2-3). In order to achieve the main goal of this master thesis, the whole test environment based on ROS has been created. The Gazebo2 simulator and the Pioneer 3-DX robot model have been used in that order. The Gazebo2 simulator and the robot model have been configured with the ROS environment compatibility (Chapter 4). Selected algorithms have been implemented in Python 2.7 programming language. Implemented algorithms and ROS algorithms have been configured with previously created test environment (Chapters 5-6). The robot working area became the rectangular building wit dimensions, 100x30[m]. About 40 obstacles, with different size, have been created in the building (Chapter 7.1). Next, the tests have been performed, in the prepared working area, in order to obtain the optimal parameters sets for each algorithm.