erfan-ashtari/Path-planning
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 quickly generate safe and efficient movement paths for robots within complex indoor environments. Given a starting and ending point in an environment with obstacles, it produces a sequence of movements that avoids collisions. This is ideal for anyone working with robotic navigation or simulations in varied 3D settings.
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Use this if you need to rapidly plan collision-free paths for robots in environments with static polygonal obstacles using deep learning methods.
Not ideal if your robots operate in outdoor, highly dynamic, or unstructured environments, or if you prefer traditional path planning algorithms over deep learning.
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Feb 15, 2023
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