our-projects-github/Safe-Deep-Learning-Based-Global-Path-Planning-Using-a-Fast-Collision-Free-Path-Generator
Implementation of "Safe Deep Learning-Based Global Path Planning Using a Fast Collision-Free Path Generator". We present a global path planning method in this project which is based on an LSTM model that predicts safe paths for the desired start and goal points in an environment with polygonal obstacles, using a new loss function (MSE-NER).
This project helps robotics engineers and researchers design safe and efficient navigation for robots. It takes the desired start and end points within an environment that has polygonal obstacles. The output is a predicted collision-free path for the robot to follow, allowing for quick deployment and testing of autonomous navigation systems.
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Use this if you need to generate safe and globally optimized paths for robots in environments with static obstacles.
Not ideal if your environment involves highly dynamic obstacles or requires real-time, reactive local path adjustments beyond global planning.
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Feb 15, 2023
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