FC-Planner: A Skeleton-guided Planning Framework for Fast Aerial Coverage of Complex 3D Scenes (2309.13882v3)

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.

• The paper introduces a novel skeleton-guided framework that combines topology-aware path finding with gradient-based optimization to overcome local minima.
• The methodology efficiently computes collision-free UAV trajectories by expanding the trajectory search space in complex 3D environments.
• The approach promises enhanced UAV navigation performance, offering faster, safer aerial coverage in dynamic and obstacle-rich settings.

Overview of Topology Graph Aided Gradient-based Trajectory Optimization for Robust UAV Replanning

The paper presents an approach for enhancing unmanned aerial vehicle (UAV) trajectory optimization by integrating topology graphs with gradient-based methods. The work addresses the challenges faced in local trajectory replanning for UAVs, focusing on overcoming obstacles with strategies that improve upon traditional methods.

Key Contributions

1. Gradient-based Trajectory Optimization: The paper explores the limitations of existing gradient-based optimization techniques, noting that they typically falter due to poor initial paths or local optima. These traditional methods necessitate a collision-free initial path for higher success but do not inherently guarantee optimality.
2. Topology Path Finding: The authors incorporate topology graphs to distinguish paths within the same homotopy class, thus expanding on 3-D pathfinding capabilities. This integration aims to circumvent the pitfalls of local optima by encapsulating a broader range of trajectory options in complex environments.
3. Structural Analysis of Existing Methods:
   • CHOMP and STOMP: These methods, while influential, are critiqued for their limitations in cluttered environments and their reliance on discrete waypoints, which constrain trajectory representation and performance.
   • Convex Optimization and Continuous Time Methods: Existing methods leverage convex regions for subproblem solving and continuous time representations for improved dynamic constraints handling but encounter challenges related to region computation and path initialization.
   • PRM Variants: The probabilistic roadmap variants and H-signature methods are discussed for their use in topology path finding, although their direct application in 3-D spaces remains problematic.

Results and Evaluations

The paper refrains from delineating specific experimental results but implies that the theoretical integration of topology graphs in trajectory optimization could result in enhanced replanning success rates and an ability to navigate more dynamically through environments with complex obstacle distributions.

Implications and Future Directions

The incorporation of topology graphs in gradient-based trajectory optimization signals a conceptual advancement in UAV replanning strategies. This integration holds potential practical implications, particularly in autonomous navigation and obstacle avoidance tasks, where UAVs must maintain high levels of safety and efficiency. Theoretically, the approach contributes to an expanded framework for understanding pathfinding and trajectory optimization in multidimensional spaces.

Future developments might focus on refining the computational efficiency of these combined methods and validating their effectiveness in real-world UAV applications, where computational constraints and unpredictable scenarios pose significant challenges. Further exploration into adaptive algorithms that incorporate real-time sensor data could also augment the robustness of UAV trajectory planning capabilities in dynamic environments.