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ros_autonomous_slam
ROS package which uses the Navigation Stack to autonomously explore an unknown environment with help of GMAPPING and constructs a map of the explored environment. Finally, a path planning algorithm from the Navigation stack is used in the newly generated map to reach the goal. The Gazebo simulator is used for the simulation of the Turtlebot3 Waffle Pi robot. Various algorithms have been integrated for Autonomously exploring the region and constructing the map with help of the 360-degree Lidar sensor. Different environments can be swapped within launch files to generate a map of the environment.realsense_explorer_bot
Autonomous ground exploration mobile robot which has 3-DOF manipulator with Intel Realsense D435i mounted on a Tracked skid-steer drive mobile robot. The robot is capable of mapping spaces, exploration through RRT, SLAM and 3D pose estimation of objects around it. This is an custom robot with self built URDF model.The Robot uses ROS's navigation stacks .realsense_bot
This is a ROS package for Intel realsense D435i with 3-DOF Manipulator robot that can be used for Indoor Mapping and localization of objects in the world frame with an added advantage of the robot's dexterity. The 3-DOF Manipulator is a self-built custom robot where the URDF with the depth sensor is included. The package covers the Rosserial communication with Arduino nodes or I2C with the Jetson Nano to control the robot's Joint States and PCL pipelines required for autonomous mapping/Localization/Tracking of the objects in real-time.AppliedDeepLearning
This repository consists a set of Jupyter Notebooks with a different Deep Learning methods applied. Each notebook gives walkthrough from scratch to the end results visualization hierarchically. The Deep Learning methods include Multiperceptron layers, CNN, GAN, Autoencoders, Sequential and Non-Sequential deep learning models. The fields applied includes Image Classification, Time Series Prediction, Recommendation Systems , Anomaly Detection and Data Analysis.virtual_pen_MNIST
This is a python program which uses deep learning and image processing to create virtual pen where the user can hover with the configured colour tip over the webcam to write digits. The deep learning model trained using mnist is used to recognize the digits. It uses keras for deep learning and opencv for image processing.robot_algorithms_projects
pattern_recognition
This repository contains various jupyter pages written by me working on the MNIST datasets for my course Pattern Recognition. It uses different learning methods such as Support Vector Machines, Neural Networks, Generative Models, Probabilistic Graphic Models and Linear Discriminant functions. It uses keras and tensorflow for most of the codes.myotron_wrist_control
This project proposes and delivers a novel approach to train and test a Convolutional Neural Network (CNN) model for muscle synergy controlled prosthetic hands. The project is focused on providing a solution for precise control and real-time testing of prosthetic hand control used by below-elbow amputees having independent control over the prosthetic fingers. Multiple EMG sensors that are placed on the forearm will be used to control the prosthetic hand using the trained model. CNN allows us to extract features from raw EMG signals without the requirement for manual feature engineering done over raw data in traditional methods. Furthermore, the trained model will be evaluated in real-time within a Virtual Reality environment developed using the Mujoco Physics environment with the HTC Vive VR headset. The developed algorithm will be tested on ten healthy participants and their data will be analyzed to show the performance of the presented controller.Love Open Source and this site? Check out how you can help us