As the world continues to explore advancements in drone technology, deep reinforcement learning (DRL) stands out as a powerful solution for enabling UAVs (Unmanned Aerial Vehicles) to autonomously navigate complex environments. This blog will guide you through the implementation of an obstacle avoidance algorithm using DRL for both static and dynamic environments.

Understanding the Project

This project is centered around a deep reinforcement learning autonomous obstacle avoidance algorithm for UAVs. It addresses crucial scenarios: navigating through static environments and maneuvering in dynamic environments. Let’s break down this concept further.

Static Environment

In static environments, we combine several methods as follows:

• MADDPG (Multi-Agent Deep Deterministic Policy Gradient)
• Fully Centralized DDPG
• Fully Decentralized DDPG
• Fully Centralized TD3

Among these methods, the Fully Decentralized DDPG and Fully Centralized TD3 have showcased superior performance.

Dynamic Environment

For dynamic scenarios, the following algorithms are used:

• PPO+GAE (Proximal Policy Optimization with Generalized Advantage Estimation, using multi-processing)
• TD3 (Twin Delayed Deep Deterministic Policy Gradient)
• DDPG (Deep Deterministic Policy Gradient)
• SAC (Soft Actor-Critic)

While PPO convergence requires fewer episodes, both TD3 and DDPG converge rapidly. Despite the popularity of SAC, its performance in this project was not effective.

Traditional vs. Reinforcement Learning Methods

For UAV path planning, traditional methods coded in MATLAB include:

• A* Search Algorithm
• RRT Algorithm
• Ant Colony Algorithm

Additionally, a D* Algorithm was developed in C++. The A* Search Algorithm performed significantly better than other traditional methods but still fell short compared to reinforcement learning approaches.

Artificial Potential Field Algorithm

The project offers implementations of the artificial potential field algorithm in both MATLAB and Python, enhancing the utility of the project.

Getting Started with Training

To train the agent in a dynamic environment using TD3, follow these steps:

1. Run the main.py script.
2. Execute the test.py script.
3. Open MATLAB and run test.m to visualize the results.

If you want to test the model against four obstacles, simply run Multi_obstacle_environment_test.py.

Requirements

You will need the following libraries for successful execution:

• numpy
• pytorch
• matplotlib
• seaborn (version 0.11.1)

Illustrative Files

The project includes several essential files, including:

• calGs.m: Computes the performance index Gs of the route.
• draw.py: Includes a Painter class to visualize the reward curves of various methods.
• config.py: Contains settings for parameters during the training process.
• static_obstacle_environment.py: Contains parameters for static obstacle environments.
• Multi_obstacle_environment_test.py: Tests the model in dynamic environments.

Code Explanation Analogy

Imagine programming the drone’s journey like crafting a plan for a treasure hunt, where each waypoint represents an obstacle that the drone must navigate around. The various algorithms serve as different strategies for the treasure hunters:

• MADDPG: Many treasure hunters (agents) working together, sharing their discoveries.
• PPO+GAE: A treasure hunter with a keen sense of danger, adjusting their route based on previous attempts.
• A* Algorithm: The most efficient treasure map, optimizing the shortest path but still missing out on some valuable insights that ML can provide over time.

Each algorithm finds a way to reach the treasure, but with different approaches and varying degrees of efficiency.

Troubleshooting

If you encounter issues while running the code, consider the following troubleshooting tips:

• Ensure all required libraries are installed correctly.
• Check for any syntax errors in your Python and MATLAB scripts.
• Verify that your Python environment matches the specified library versions.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.