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

Chen Feng, Haojia Li, Mingjie Zhang, Xinyi Chen, Boyu Zhou, Shaojie Shen

TL;DR

FC-Planner is proposed, a skeleton-guided planning framework that can achieve fast aerial coverage of complex 3D scenes without pre-processing and computes over 10 times faster compared to state-of-the-art methods with shorter path and more complete coverage.

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 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. Project page: https://hkust-aerial-robotics.github.io/FC-Planner.

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

TL;DR

FC-Planner is proposed, a skeleton-guided planning framework that can achieve fast aerial coverage of complex 3D scenes without pre-processing and computes over 10 times faster compared to state-of-the-art methods with shorter path and more complete coverage.

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 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. Project page: https://hkust-aerial-robotics.github.io/FC-Planner.
Paper Structure (17 sections, 9 equations, 6 figures, 3 tables, 2 algorithms)

This paper contains 17 sections, 9 equations, 6 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. System Overview
  4. Skeleton-based Space Decomposition
  5. Skeleton Extraction
  6. Skeleton Decomposition
  7. Space Allocation
  8. Skeleton-guided Viewpoint Generation
  9. Internal Space and Viewpoint Sampling Ray
  10. Bidirectional Ray Casting
  11. Iterative Updates of Viewpoint Pose
  12. Hierarchical Coverage Planning
  13. Experiments
  14. Implementation Details
  15. Benchmark Comparisons and Analysis
  16. ...and 2 more sections

Figures (6)

  • Figure 1: (a) An aerial coverage test conducted in a challenging scene. (b) Showcase of space decomposition details and coverage trajectory.
  • Figure 2: The overview of the proposed skeleton-guided planning framework for fast aerial coverage in complex 3D scenes.
  • Figure 3: (a) Generation of internal space and viewpoint sampling rays (Sect.\ref{['subs:i_and_vr']}). (b) Gravitation-like model used to update the pose of query viewpoint (Sect.\ref{['subs:obvs']}).
  • Figure 4: Comparisons on coverage situation and generated path of the proposed method, SIP bircher2015structural, and HCPP cao2020hierarchical in three complex scenarios.
  • Figure 5: Comparisons of results before and after using local path refinement.
  • ...and 1 more figures