Git Product home page Git Product logo

ssingh82's Projects

but_velodyne icon but_velodyne

ROS packages for Velodyne 3D LIDARs provided by Robo@FIT group.

pick_place_with_ur5 icon pick_place_with_ur5

The repository contains the MATLAB codes for the Implementation of pick and place tasks with the UR5 robot using Inverse Kinematics, Resolved Rate control and Gradient Descent control algorithms.

quadruped icon quadruped

The code presented here is for various gaits implemented on the Quadruped that I developed as a part of my Bachelors Thesis Project.

rl_nav icon rl_nav

This is the accompannying code for the paper "SLAM-Safe Planner: Preventing Monocular SLAM Failure using Reinforcement Learning" and "Data driven strategies for Active Monocular SLAM using Inverse Reinforcement Learning" To run the code, download this repository and a modified version of PTAM from https://github.com/souljaboy764/ethzasl_ptam/ to your catkin workspace and compile it. For running the agent on maps: In the turtlebot_gazebo.launch change the argument "world_file" to the corresponding map world file (map1.world, map2.world, map3.world, corridor.world or rooms.world) and set the corresponding initial positions in joystick.launch Open 4 new terminals Terminal 1: roslaunch rl_nav turtlebot_gazebo.launch Terminal 2: roslaunch ptam ptam.launch Terminal 3: roslaunch rl_nav joystick.launch Terminal 4: rosrun rviz rviz -d `rospack find rl_nav`/ptam.rviz Press the "start" button on the xbox joystick or publish a message of type "std_msgs/Empty" to /rl/init Once PTAM is initialized, give an intermediate point using the "2D Pose Estimate" button in rviz and give the goal location using "2D Nav Goal" For traning the agent, In the turtlebot_gazebo.launch change the argument "world_file" to training.world Open 3 new terminals Terminal 1: roslaunch rl_nav turtlebot_gazebo.launch Terminal 2: roslaunch ptam ptam.launch Terminal 3: roslaunch rl_nav train.launch Press the "start" button on the xbox joystick or publish a message of type "std_msgs/Empty" to /rl/init Once PTAM is initialized, press the "A" button on the xbox controller to start training. For testing the agent on steps to breakage, In the turtlebot_gazebo.launch change the argument "world_file" to training.world Open 3 new terminals Terminal 1: roslaunch rl_nav turtlebot_gazebo.launch Terminal 2: roslaunch ptam ptam.launch Terminal 3: roslaunch rl_nav test.launch Press the "start" button on the xbox joystick or publish a message of type "std_msgs/Empty" to /rl/init Once PTAM is initialized, press the "A" button on the xbox controller to start testing. For running the IRL agent, just change the weights in qMatData.txt to the weights in qMatData_SGD.txt and run any of the above. For training the IRL agent, run IRLAgent.py with the data from https://www.dropbox.com/s/qnp8rs92kbmqz1e/qTrain.txt?dl=0 in the same folder as IRLAgent.py, which will save the final Q values in qRegressor.pkl

rsp icon rsp

This is the accompanying code for the project " MULTI - AGENT PATH PLANNING FOR SWARM MOTION". The project following ROS uses: a) Ar_track Alvar b) Joystick c) Edumip(Self-Balancing Robot) Balancing Code d) USB cam Using the above mentioned, packages and with the move_base Package in ROS I developed a pipeline for the path planning of multi self- balancing robots Edumips which can find their path from given location to desired location avoiding the obstacles and the other robots in the environment. Link for the video: https://drive.google.com/open?id=0B8cnf5a4CLP_REozbzFMMHkxM1U

smart_rain_barrel icon smart_rain_barrel

The arduino code presented here is for the design and fabrication of an autonomous solar-powered smart rain barrel that stores rain water for future use, water for use in garden and drains the excess water from roof to prevent the flooding of the garden. The sensors used are pressure sensor, soil humidity sensor and solenoid valves with Arduino microcontroller to fabricate the rain barrel.

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.