From 59933f7d8d478a62c827303036ed7a3aaabc27e2 Mon Sep 17 00:00:00 2001 From: TJU-Lu Date: Tue, 23 Dec 2025 15:35:26 +0800 Subject: [PATCH] update SolidWorks assembly files --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 4e3ad08..5601054 100644 --- a/README.md +++ b/README.md @@ -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. ### 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: 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.