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DDPG_AMR_Control

Description

This repo contains implementation for DDPG based controller for continous mapless navigation for autonomous mobile robot. The agent used here is burger model of ROS turtlebot3.

State Space: The state vector is abstracted from 90-dimensional laser range findings, the previous action, the relative target position (relative dis- tance and heading) are merged together as a 94-dimensional input vector. The laser range findings are sampled from the raw laser findings between 0 and 360 degrees in a trivial and fixed angle distribution of 4 degrees. The range information is normalized to (0, 1). The 2-dimensional target position is represented in polar coordinates (distance and angle) with respect to the mobile robot coordinate frame.

Action Space: The 2-dimensional action of every time step includes the angular and the linear velocities of the differential mobile robot. To constrain the range of angular velocity in (−0.5, 0.5), a hyperbolic tangent function (tanh) is used as the activation function. Moreover, the range of the linear velocity is constrained in (0, 0.5) through a sigmoid function for forward motion and the range of the linear velocity is constrained in (−0.5, 0.5) through a tanh function for backward motion. Considering the real dynamics of the robot we clip the angular velocity at 1 radian per second and linear velocity at 0.5 meter per second.

The below gif shows the algorithm tested in a complex environment. The red box is the goal and agent has learned to reach the goal without any stored map.

Dependencies for Running Locally

  • ROS Noetic
  • python >= 3.4
  • Pytorch
    • Install using "pip install torch==1.6"
  • Colorama
    • Install using "pip install colorama==0.4.4"
  • Numpy
    • Install using "pip install numpy"
  • Wandb - For logging graphs click here for reading more about it
    • You will need to setup an account on wandb to use there platform. It is completely free for use.
    • Install using "pip instal wandb"

Basic Build Instructions

  1. Clone this repo.

  2. Go into the repo directory: cd DDPG_AMR_Control

  3. Compile the source code: catkin_make -j4

Run Simulation for stage 1

source devel/setup.bash
export TURTLEBOT3_MODEL = burger
roslaunch turtlebot3_gazebo turtlebot3_stage_1.launch

Run Simulation for stage 4

source devel/setup.bash
export TURTLEBOT3_MODEL = burger
roslaunch turtlebot3_gazebo turtlebot3_stage_4.launch

Run Training

  1. Run the simulation using the above steps
  2. In a saperate terminal, run
cd src/ddpg_control/scripts/
python3 train.py

Run testing

  1. Run the simulation using the above steps
  2. In a saperate terminal, run
cd src/ddpg_control/scripts/
python3 test.py

Code Instructions

  1. Change the configs for your own settings for training from src/ddpg_control/scripts/config.py
  2. Change the configs for your own settings for testing from src/ddpg_control/scripts/config_test.py
  3. Change the stage number in src/ddpg_control/scripts/config.py according to stage you want to use for training
  4. Turn the LOAD_PRETRAINED flag to false in src/ddpg_control/scripts/config.py if you want to run dtraining from scratch