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@ -20,7 +20,7 @@ Some realworld experiment: [YouTube](https://youtu.be/LHvtbKmTwvE), [bilibili](h
**Faster and Simpler:** The code is greatly simplified and refactored in Python/PyTorch. We also replaced the simulator with our CUDA-accelerated randomized environment, which is faster, lightweight, and boundless. For the stable version consistent with our paper, please refer to the [main](https://github.com/TJU-Aerial-Robotics/YOPO/tree/main) branch. **Faster and Simpler:** The code is greatly simplified and refactored in Python/PyTorch. We also replaced the simulator with our CUDA-accelerated randomized environment, which is faster, lightweight, and boundless. For the stable version consistent with our paper, please refer to the [main](https://github.com/TJU-Aerial-Robotics/YOPO/tree/main) branch.
### Hardware: ### Hardware:
Our drone designed by [@Mioulo](https://github.com/Mioulo) is also open-source. The hardware components are listed in [hardware_list.pdf](hardware/hardware_list.pdf), and the SolidWorks file of carbon fiber frame can be found in [/hardware](hardware/). Our drone designed by [@Mioulo](https://github.com/Mioulo) is also open-source. The hardware components are listed in [hardware_list.pdf](hardware/hardware_list.pdf), and the SolidWorks file of carbon fiber frame can be found in [/hardware](hardware/) (complete assembly files are included in the [Release](https://github.com/TJU-Aerial-Robotics/YOPO/releases/tag/hardware)).
## Introduction: ## Introduction:
We propose **a learning-based planner for autonomous navigation in obstacle-dense environments** which integrates (i) perception and mapping, (ii) front-end path searching, and (iii) back-end optimization of classical methods into a single network. We propose **a learning-based planner for autonomous navigation in obstacle-dense environments** which integrates (i) perception and mapping, (ii) front-end path searching, and (iii) back-end optimization of classical methods into a single network.