reiniscimurs/DRL-Robot-Navigation-ROS2
Deep Reinforcement Learning for mobile robot navigation in ROS2 Gazebo simulator. Using DRL (SAC, TD3) neural networks, a robot learns to navigate to a random goal point in a simulated environment while avoiding obstacles.
This project helps robotics engineers and researchers train mobile robots to navigate safely and efficiently in simulated environments. It takes sensor data (like laser readings for obstacles and goal coordinates) as input and outputs a trained deep reinforcement learning model. This model enables a robot to reach a specified goal while autonomously avoiding collisions with obstacles.
194 stars. No commits in the last 6 months.
Use this if you need to develop and test autonomous navigation strategies for mobile robots in a simulated setting using deep reinforcement learning.
Not ideal if you are looking for a plug-and-play solution for real-world robot deployment without any prior experience in robotics simulation or deep learning.
Stars
194
Forks
22
Language
Python
License
MIT
Category
Last pushed
Jan 30, 2025
Commits (30d)
0
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