Feature request: Drone Navigation Detection using Reinforcement Learning

[Panchadip-128]

Is your feature request related to a problem? Please describe. Drone Navigator is an advanced software solution designed to empower autonomous drones with the capability to navigate complex environments efficiently and safely using Reinforcement Learning (RL) techniques.

Describe the solution you'd like Drone Navigator

Project Description

Drone Navigator is an advanced software solution designed to empower autonomous drones with the capability to navigate complex environments efficiently and safely using Reinforcement Learning (RL) techniques.

Key Features

Adaptive Navigation Obstacle Detection and Avoidance Efficient Pathfinding User-Friendly Interface Real-World Applications Technical Features Programming Language: Python Dependency Management: requirements.txt Modular Design Learning Mechanism Technologies Used Python: The primary programming language for implementing the RL algorithms. TensorFlow/PyTorch: For building and training neural networks. OpenAI Gym: For simulating the drone's environment. NumPy: For numerical operations. Matplotlib: For visualizing results and training progress. ROS (Robot Operating System) (optional): For real-world application and control. Installation: Create a virtual python environment python -m venv env

Activate the virtual environment: .\env\Scripts\activate

Clone the repository: git clone https://github.com/Panchadip-128/Drone-Navigation-using-Reinforcement-Learning-

Navigate to the project directory: cd drone-navigator

Install the required packages: pip install -r requirements.txt

Once you have the requirements and dependencies installed, you can train your PPO model on the DroneEnv. Here's a full example, including resetting and training the model:

import gym import numpy as np from stable_baselines3 import PPO

class DroneEnv(gym.Env): def init(self): super(DroneEnv, self).init() self.action_space = gym.spaces.Discrete(4) # 0: Up, 1: Down, 2: Left, 3: Right self.observation_space = gym.spaces.Box(low=0, high=10, shape=(2,), dtype=np.float32) self.state = np.array([5, 5]) # Starting position of the drone self.target = np.array([8, 8]) # Target position self.obstacles = [np.array([6, 6]), np.array([7, 7])] # Sample obstacles

def reset(self): self.state = np.array([5, 5]) return self.state

def step(self, action): if action == 0: # Up self.state[1] += 1 elif action == 1: # Down self.state[1] -= 1 elif action == 2: # Left self.state[0] -= 1 elif action == 3: # Right self.state[0] += 1

# Check for collisions
reward = -1
done = False
if any(np.array_equal(self.state, obs) for obs in self.obstacles):
    reward = -10  # Penalty for hitting an obstacle
    done = True
elif np.array_equal(self.state, self.target):
    reward = 10  # Reward for reaching the target
    done = True

return self.state, reward, done, {}

def render(self): print(f"Drone Position: {self.state}, Target: {self.target}, Obstacles: {self.obstacles}") Create the environment env = DroneEnv()

Create the model model = PPO("MlpPolicy", env, verbose=1)

Train the model for a number of timesteps model.learn(total_timesteps=10000)

Test the trained model obs = env.reset() for in range(100): action, = model.predict(obs) obs, reward, done, info = env.step(action) env.render() if done: obs = env.reset()

Explanation of Code

Environment Class: This class creates a drone environment with actions to move in four directions, collision detection, and target reaching.

Training: The PPO model is created with the MLP policy and trained for 10,000 timesteps.

Testing the Model: After training, the model is tested in a loop where it predicts actions based on the current state, steps through the environment, and renders the state.

Future Scopes:

Parameter Tuning: Experiment with different hyperparameters for the PPO model to improve performance. Enhanced Environment: Add more obstacles, or features, or increase the complexity of the environment. Logging: Implement logging of rewards and actions to analyze the agent's performance. License This project is licensed under the MIT License - see the LICENSE file for details.

Describe alternatives you've considered A clear and concise description of any alternative solutions or features you've considered.

Approach to be followed (optional) A clear and concise description of the approach to be followed.

Additional context Add any other context or screenshots about the feature request here.

[github-actions[bot]]

Thanks for creating the issue in ML-Nexus!🎉 Before you start working on your PR, please make sure to:

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[Panchadip-128]

Can this be assigned to me? @UppuluriKalyani . Please label the project as Level 2 or 3( since it contains advanced Deep Learning and Reinforcement Learning concepts) accordingly so that i can start working

[github-actions[bot]]

Hello @Panchadip-128! Your issue #462 has been closed. Thank you for your contribution!