ntnu-arl/semantic-RL-inspection
This repository provides the source code for the paper Semantically-driven Deep Reinforcement Learning for Inspection Path Planning.
This project helps robotics engineers and researchers develop and test drone inspection policies in simulated environments. It takes drone control inputs and environmental configurations (like objects and obstacles) to generate simulated drone flight paths. The output is a trained policy that enables drones to autonomously inspect specific objects while avoiding obstacles, visible in a simulated environment.
No commits in the last 6 months.
Use this if you are a robotics engineer or researcher working on autonomous drone inspection and need to train or evaluate intelligent path planning policies in a simulated setting.
Not ideal if you are looking for a plug-and-play solution for real-world drone deployment, as this is a research framework for simulation and policy training.
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Language
Python
License
BSD-3-Clause
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Last pushed
Sep 26, 2025
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