用深度图实现三维未知环境中的智能蜂群导航，避免死锁与局部最优。
GO-Flock: Goal-Oriented Flocking in 3D Unknown Environments with Depth Maps
- 融合感知与规划，通过深度图生成虚拟代理和路径点。
- 在复杂障碍环境中实现9架无人机（6实3虚）协同飞行。
- 硬件在环实验验证，克服传统方法的局部极小问题。
人工势场（APF）方法广泛用于反应式蜂群控制，但在障碍物存在时常遭遇死锁和局部极小问题。现有解决方案多为被动式，导致集体导航效率低下。许多APF方法仅在无障碍或简化伪3D仿真中验证。本文提出GO-Flock，一种将规划与基于APF的控制相结合的混合蜂群框架。该框架包含上游感知模块，利用深度图提取路径点与虚拟代理以避障；以及下游集体导航模块，采用新型APF策略，在复杂环境中实现有效蜂群行为。通过对比被动式APF方法，验证了其在蜂群一致性与突破局部极小方面的优势。最终在含障碍物环境及软硬件联合实验中成功实现9架无人机（6架物理、3架虚拟）在森林场景下的协同飞行。
原文摘要 · Abstract (English)
Artificial Potential Field (APF) methods are widely used for reactive flocking control, but they often suffer from challenges such as deadlocks and local minima, especially in the presence of obstacles. Existing solutions to address these issues are typically passive, leading to slow and inefficient collective navigation. As a result, many APF approaches have only been validated in obstacle-free environments or simplified, pseudo 3D simulations. This paper presents GO-Flock, a hybrid flocking framework that integrates planning with reactive APF-based control. GO-Flock consists of an upstream Perception Module, which processes depth maps to extract waypoints and virtual agents for obstacle avoidance, and a downstream Collective Navigation Module, which applies a novel APF strategy to achieve effective flocking behavior in cluttered environments. We evaluate GO-Flock against passive APF-based approaches to demonstrate their respective merits, such as their flocking behavior and the ability to overcome local minima. Finally, we validate GO-Flock through obstacle-filled environment and also hardware-in-the-loop experiments where we successfully flocked a team of nine drones, six physical and three virtual, in a forest environment.
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