MickyasTA/DRL_robot_navigation_ros2
TD3-based Deep Reinforcement learning-based collision avoidance of mobile robots in ROS2.
This project helps robotics engineers and researchers quickly evaluate a mobile robot's ability to autonomously navigate a simulated environment while avoiding obstacles and simultaneously building a map. It takes a pre-trained robot model and a ROS2 simulation environment as input, and outputs the robot's movement, obstacle avoidance behavior, and a generated map of the room. This is for professionals developing or testing autonomous mobile robot systems.
No commits in the last 6 months.
Use this if you need to simulate and visualize a mobile robot performing collision-free navigation and mapping in a ROS2 environment.
Not ideal if you need to train a new robot model from scratch or require a global navigation strategy beyond local collision avoidance.
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Language
Python
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Last pushed
May 01, 2025
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