Read the following Part I of the project I started implementing. I’ll provide you all the necessary code later. Part I - Building the Environment

Introduction

Goal → To implement two independent ROS nodes for part - I.
ROS Node - I → Implemented a node that enables the car to scan its environment using a laser range finder and avoid obstacles in front of it.
ROS Node - II → Implemented a node that scans the environment of the car and saves the cartesian coordinates of the scanned obstacles while accounting for motion.
Prerequisites


Dependencies: ROS (Robot Operating System) ; Gazebo (Simulation Environment)
The car uses Ackermann driving. You can drive the car by publishing AckermannDrive messages to the topic car_1/command .
The car is also equipped with a laser scanner which publishes LaserScan messages on the topic car_1/scan.
The laser scanner on the car has a field of view from -3π/4 to 3π/4 with a resolution of 0.0043633 radians.
The car publishes its odometry on the topic car_1/base/odom using an Odometry message (nav_msgs/odometry) . You can get the global position and orientation of the car from this message.




Setup

Launch the Gazebo simulator using
roslaunch audubon-gazebo world.launch
This will launch the testing environment in gazebo.
To set up a custom Gazebo world file with a maze-like environment by using the launch file pa2_spawner.launch to populate the testing space with randomly placed obstacles
roslaunch audubon-gazebo spawner.launch
Note: Make sure that the env is populated with various obstacles and walls to create a challenging environment for the robot.
Once all obstacles are spawned, you can control the car using your keyboard.
Note : Gazebo terminal needs to be in focus to take keyboard inputs.






















Part II - Implementing the Deep Reinforcement Learning Algorithm


To Implement a deep Q-network (DQN) to learn the control policy for robot navigation.

Here’s the general outline of the DQN algorithm that I’m going to implement:

import numpy as np
import tensorflow as tf
from collections import deque
import random

# Define DQNAgent class
class DQNAgent:

def init(self):
# Initialize DQN architecture, experience replay memory, etc.
pass

def remember(self, state, action, reward, next_state, done):
# Store experience in replay memory
pass

def act(self, state):
# Choose an action based on the current state
pass

def train(self, batch_size):
# Sample a batch of experiences from replay memory and train the DQN model
pass

def save(self, name):
# Save the trained model weights
pass


First, Create an ROS action server and client to send control commands i.e linear and angular velocities based on the actions chosen by the DQNAgent.

Implement the training loop that consists of the following steps:
Read the sensor data from the robot’s sensors and preprocess it as the state input for the DQN.
Choose an action using the DQN and send it to the robot using the action server.
Calculate the reward based on the robot’s current position in the environment.
Update the DQN with the new state, action, and reward information.
Repeat until a certain number of episodes or a termination criterion is reached.
Test the trained navigation policy in the Gazebo simulation to see how well the robot can navigate the maze.


Keep in mind that this is just a high-level outline, and the actual implementation will require much more effort, including experimenting with different DQN architectures, tuning hyperparameters, and potentially implementing methods to incorporate sensory feedback from the robot’s sensors to improve navigation performance.