From 2aadfabb15417aaaa6bbeb0192039ba272b95413 Mon Sep 17 00:00:00 2001 From: Chen-Albert-FENG Date: Sat, 6 Apr 2024 23:18:26 +0800 Subject: [PATCH] [docs]: update doc --- index.html | 36 ++++++++++++++--------------------- static/images/soccer-ball.svg | 1 + 2 files changed, 15 insertions(+), 22 deletions(-) create mode 100644 static/images/soccer-ball.svg diff --git a/index.html b/index.html index 589fc68..70c6d19 100644 --- a/index.html +++ b/index.html @@ -52,7 +52,8 @@
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FC-Planner: A Skeleton-guided Planning Framework for Fast Aerial Coverage of Complex 3D Scenes

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⚽ FC-Planner

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A Skeleton-guided Planning Framework for Fast Aerial Coverage of Complex 3D Scenes

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Abstract

- 3D coverage path planning for UAVs is a crucial - problem in diverse practical applications. However, existing - methods have shown unsatisfactory system simplicity, computation efficiency, and path quality in large and complex - scenes. To address these challenges, we propose FC-Planner, - a skeleton-guided planning framework that can achieve fast - aerial coverage of complex 3D scenes without pre-processing. - We decompose the scene into several simple subspaces by a - skeleton-based space decomposition (SSD). Additionally, the - skeleton guides us to effortlessly determine free space. We - utilize the skeleton to efficiently generate a minimal set of - specialized and informative viewpoints for complete coverage. Based on SSD, a hierarchical planner effectively divides - the large planning problem into independent sub-problems, - enabling parallel planning for each subspace. The carefully - designed global and local planning strategies are then incorporated to guarantee both high quality and efficiency in - path generation. We conduct extensive benchmark and realworld tests, where FC-Planner computes over 10 times faster - compared to state-of-the-art methods with shorter path and - more complete coverage. The source code will be open at - https://github.com/HKUST-Aerial-Robotics/FC-Planner. + 3D coverage path planning for UAVs is a crucial problem in diverse practical applications. + However, existing methods have shown unsatisfactory system simplicity, computation efficiency, and path quality in large and complex scenes. + To address these challenges, we propose FC-Planner, a skeleton-guided planning framework that can achieve fast aerial coverage of complex 3D scenes without pre-processing. + We decompose the scene into several simple subspaces by a skeleton-based space decomposition (SSD). + Additionally, the skeleton guides us to effortlessly determine free space. + We utilize the skeleton to efficiently generate a minimal set of specialized and informative viewpoints for complete coverage. + Based on SSD, a hierarchical planner effectively divides the large planning problem into independent sub-problems, enabling parallel planning for each subspace. + The carefully designed global and local planning strategies are then incorporated to guarantee both high quality and efficiency in path generation. + We conduct extensive benchmark and real-world tests, where FC-Planner computes over 10 times faster compared to state-of-the-art methods with shorter path and more complete coverage. + The source code will be made publicly available to benefit the community at https://github.com/HKUST-Aerial-Robotics/FC-Planner. + Project page: https://hkust-aerial-robotics.github.io/FC-Planner.

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BibTeX

- This page was built using the Academic Project Page Template which was adopted from the Nerfies project page. - You are free to borrow the of this website, we just ask that you link back to this page in the footer.
This website is licensed under a Creative - Commons Attribution-ShareAlike 4.0 International License. + This page was built using the Academic Project Page Template which was adopted from the Nerfies project page. This page is designed by Chen Feng.

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