SwarmPath：人工ポテンシャル場とインピーダンス制御を活用した混雑環境下でのドローン群ナビゲーション
SwarmPath: Drone Swarm Navigation through Cluttered Environments Leveraging Artificial Potential Field and Impedance Control
人工ポテンシャル場とインピーダンス制御を組み合わせ、仮想リーダーに追従するドローン群が障害物を避けつつ狭い通路を効率的に通過する手法を提案した論文。
著者: Roohan Ahmed Khan, Malaika Zafar, Amber Batool, Aleksey Fedoseev, Dzmitry Tsetserukou
分類: cs.RO
原文アブストラクト
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.
