DroneRobot Documentation

DroneRobot Documentation

This documentation provides details about the DroneRobot simulation project.

Overview

The DroneRobot project simulates and controls a robotic drone in a Webots environment. It includes functionality for drone motion control, environment interaction, and reinforcement learning.

Features

• PID controller for precise drone motion control.
• Integrated observation and action spaces for environment interaction.
• Reward-based mechanism for task evaluation.
• Supports TensorBoard for monitoring progress.
• Reset and step functionality for episodic simulations.

Dependencies

• Webots (R2020b or later) for simulation.
• Python 3.6+ for scripting and control.
• Required Python libraries:
  • NumPy
  • OpenAI Gym
  • TensorFlow (optional, for TensorBoard)

Usage

1. Set up the Webots environment and install the required dependencies.
2. Initialize the DroneRobot class to control the drone in the simulation.
3. Use the provided methods such as step, reset, and apply_action for interaction and control.
4. Utilize the reward system and TensorBoard for reinforcement learning tasks.

Authors

The project uses several different opensource libraries. All rights reserved by their respective owners

© 2024 DroneRobot Project.