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.

翻译：人工势场（APF）方法被广泛用于反应式集群控制，但常面临死锁和局部极小值等挑战，尤其在存在障碍物的环境中。现有解决方案通常是被动的，导致群体导航缓慢且低效。因此，许多APF方法仅在无障碍环境或简化的伪三维仿真中得到验证。本文提出GO-Flock——一种融合规划与基于APF的反应式控制的混合集群框架。GO-Flock包含上游感知模块与下游集体导航模块：感知模块处理深度图以提取用于避障的路径点和虚拟智能体；集体导航模块采用新型APF策略，在复杂环境中实现高效集群行为。我们通过对比被动式APF方法评估GO-Flock，展示其在集群行为与克服局部极小值能力等方面的优势。最后，我们在充满障碍物的环境中验证GO-Flock，并进行了硬件在环实验，成功在森林环境中实现了九架无人机（六架实体机与三架虚拟机）的集群飞行。