用虚拟领航+阻抗控制，让无人机群高效避障通过狭窄区域
SwarmPath: Drone Swarm Navigation through Cluttered Environments Leveraging Artificial Potential Field and Impedance Control
- 虚拟领航+人工势场规划路径，物理无人机跟随
- 动态调节阻抗实现避障，比传统方法快30%且不丢失连接
- 仿真与真实误差仅6%，适合实际部署的无人机编队
在多无人机系统中，如何在动态环境中从起点到目标安全导航并生成无碰撞轨迹，是重大挑战。本文提出一种名为SwarmPath的新技术，将人工势场（APF）与阻抗控制相结合。该方法采用虚拟领航者-物理跟随者结构，无人机利用APF规划最短路径；同时动态调整阻抗连接，形成与障碍物的虚拟连接以实现避障。相比传统APF，SwarmPath不仅实现平滑避障，还能使无人机群更高效通过狭窄通道，总行程时间减少30%，同时保证编队连接性。实验表明，仿真与真实环境间的轨迹平均绝对百分比误差（APE）仅为6%，证明了该方案在现实场景中的可靠性。
原文摘要 · Abstract (English)
In the area of multi-drone systems, navigating through dynamic environments from start to goal while providing collision-free trajectory and efficient path planning is a significant challenge. To solve this problem, we propose a novel SwarmPath technology that involves the integration of Artificial Potential Field (APF) with Impedance Controller. The proposed approach provides a solution based on collision free leader-follower behaviour where drones are able to adapt themselves to the environment. Moreover, the leader is virtual while drones are physical followers leveraging APF path planning approach to find the smallest possible path to the target. Simultaneously, the drones dynamically adjust impedance links, allowing themselves to create virtual links with obstacles to avoid them. As compared to conventional APF, the proposed SwarmPath system not only provides smooth collision-avoidance but also enable agents to efficiently pass through narrow passages by reducing the total travel time by 30% while ensuring safety in terms of drones connectivity. Lastly, the results also illustrate that the discrepancies between simulated and real environment, exhibit an average absolute percentage error (APE) of 6% of drone trajectories. This underscores the reliability of our solution in real-world scenarios.
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