ssingh82's repositories
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
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.
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
but_velodyne
ROS packages for Velodyne 3D LIDARs provided by Robo@FIT group.
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.