Number Adaptive Formation Flight Planning via Affine Deformable Guidance in Narrow Environments
🤖 AI Summary
Maintaining and converging formation geometry for UAV swarms in confined environments is challenging when swarm size dynamically changes. Method: This paper proposes an adaptive formation planning method based on an affine-deformable virtual structure. It employs continuous spatiotemporal affine transformations to enable rapid reconfiguration during real-time swarm expansion or reduction; integrates Lloyd partitioning with the Hungarian algorithm to preserve geometric consistency; and combines motion-primitive-based path search, nonlinear trajectory optimization, and distributed collision avoidance for safe navigation. Contributions/Results: Simulation results demonstrate that the system rapidly restores formation integrity under ±15% dynamic UAV addition/removal, achieving significantly faster convergence and superior environmental adaptability compared to state-of-the-art approaches—thereby validating its effectiveness and robustness.
Application Category
📝 Abstract
Formation maintenance with varying number of drones in narrow environments hinders the convergence of planning to the desired configurations. To address this challenge, this paper proposes a formation planning method guided by Deformable Virtual Structures (DVS) with continuous spatiotemporal transformation. Firstly, to satisfy swarm safety distance and preserve formation shape filling integrity for irregular formation geometries, we employ Lloyd algorithm for uniform $underline{PA}$rtitioning and Hungarian algorithm for $underline{AS}$signment (PAAS) in DVS. Subsequently, a spatiotemporal trajectory involving DVS is planned using primitive-based path search and nonlinear trajectory optimization. The DVS trajectory achieves adaptive transitions with respect to a varying number of drones while ensuring adaptability to narrow environments through affine transformation. Finally, each agent conducts distributed trajectory planning guided by desired spatiotemporal positions within the DVS, while incorporating collision avoidance and dynamic feasibility requirements. Our method enables up to 15% of swarm numbers to join or leave in cluttered environments while rapidly restoring the desired formation shape in simulation. Compared to cutting-edge formation planning method, we demonstrate rapid formation recovery capacity and environmental adaptability. Real-world experiments validate the effectiveness and resilience of our formation planning method.
Problem
Research questions and friction points this paper is trying to address.
Maintaining drone formations when drones join or leave in narrow spaces
Achieving smooth formation transitions with varying numbers of drones
Ensuring collision-free formation planning in cluttered environments
Innovation
Methods, ideas, or system contributions that make the work stand out.
Deformable Virtual Structures with continuous spatiotemporal transformation
Lloyd and Hungarian algorithms for uniform partitioning and assignment
Affine transformation enables adaptive transitions in narrow environments
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2024-04-02Citations: 1
