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This is a project about deep reinforcement learning autonomous obstacle avoidance algorithm for UAV.
| Date | Stars |
|---|---|
| 2026-07-31 | 724 |
| 2026-08-01 | 724 |
| 2026-08-02 | 725 |
| 2026-08-06 | 726 |
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[](https://git.io/typing-svg) # README This is a project about deep reinforcement learning autonomous obstacle avoidance algorithm for **UAV**. The whole project includes obstacle avoidance in **static environment** and obstacle avoidance in **dynamic environment**. In the static environment, **Multi-Agent Reinforcement Learning** and **artificial potential field algorithm** are combined. In the dynamic environment, the project adopts the combination of **disturbed flow field algorithm** and **single agent reinforcement learning algorithm**. As this is my undergraduate graduation project and my academic level is limited, I wrote a conference paper at that time. The address of the paper is: [Adaptive Interfered Fluid Dynamic System Algorithm Based on Deep Reinforcement Learning Framework](https://link.springer.com/chapter/10.1007/978-981-16-9492-9_139) ## Static environment There are four methods to solve: 1. **MADDPG** 2. **Fully Centralized DDPG** 3. **Fully Decentralized DDPG** 4. **Fully Centralized TD3** The third and the fourth methods perform better than others. ## Dynamic environment There are four methods to solve: 1. **PPO+GAE(with multi-processing )** 2. **TD3** 3. **DDPG** 4. **SAC** The first three methods perform just the same. PPO convergence needs less episodes. TD3 and DDPG converge fast. Though Soft Actor-Critic is an outstanding algorithm in DRL, it has no obvious effect in my environment. ## Traditional methods for UAV path planning Three traditional methods are written with **MATLAB**: 1. **A * search algorithm*** 2. **RRT algorithm** 3. **Ant colony algorithm** **C++:** 1. **D star algorithm** The experiments show that A* search algorithm is much better than others but it is less effective than reinforcement learning path planning. ## Artificial potential field algorithm This project provides the MATLAB and Python realization of artificial potential field algorithm. **Python realization**: ./APF/APFPy2.py ./APF/APFPy3.py ./APF/ApfAlgorithm.py (two-dimensional and three-dimensional) **Matlab realization**: ./APF/APF_matlab (two-dimensional) ## IFDS and IIFDS algorithm This is an obstacle avoidance planning algorithm based on flow field. I realize it with matlab. The code is in folder **IIFDS_and_IFDS**. ## How to begin trainning For example, you want to train the agent in dynamic environment with TD3, what you need to do is just running the **main.py**, then **test.py**, finally open matlab and run the **test.m** to draw. If you want to test the model in the environment with 4 obstacles, you just need to run **Multi_obstacle_environment_test.py**. ## Requirements numpy torch matplotlib seaborn==0.11.1 ## Files to illustrate **calGs.m**: calculate the index Gs which shows the performance of the route. **calLs.m**: calculate the index Ls which shows the performance of the route. **draw.py**: this file includes the Painter class which can draw the reward curve of various methods. **config.py**: this file give the setting of the parameters in trainning process of the algorithm such as the MAX_EPISODE, batch_size and so on. **Method.py**: this file concludes many important methods such as how to calculate the reward of the agents. **static_obstacle_environment.py**: there are many static obstacle environments' parameters in this file. **dynamic_obstacle_environment.py**: there are many dynamic obstacle environments' parameters in this file. **Multi_obstacle_environment_test.py**: this file test the dynamic model in the environment in dynamic_obstacle_environment.py. **data_csv**: this file save some data such as the trace of UAV and the reward in trainning. **AntColonybenchmark.m**: ACO algorithm realized by MATLAB. **Astarbenchmark.m**: A\* algorithm realized by MATLAB. **RRTbenchmark.m**: RRT algorithm realized by MATLAB.
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matched fp:9c0217a4908ef7ec, topic:reinforcement-learning, desc:reinforcement learning