SwarmPath：利用人工势场与阻抗控制在杂乱环境中进行无人机集群导航
SwarmPath: Drone Swarm Navigation through Cluttered Environments Leveraging Artificial Potential Field and Impedance Control
AI总结：
提出SwarmPath技术，融合人工势场与阻抗控制，实现无人机集群无碰撞导航，减少30%旅行时间，轨迹误差仅6%。
摘要：
在多无人机系统领域，从起点到目标点穿越动态环境，同时提供无碰撞轨迹和高效路径规划是一项重大挑战。为解决这一问题，我们提出了一种新颖的SwarmPath技术，该技术将人工势场（APF）与阻抗控制器相结合。所提出的方法提供了一种基于无碰撞的领导者-跟随者行为的解决方案，使无人机能够适应环境。此外，领导者是虚拟的，而无人机是物理跟随者，利用APF路径规划方法寻找到达目标的最短可能路径。同时，无人机动态调整阻抗链接，使其能够与障碍物建立虚拟链接以避免碰撞。与传统APF相比，所提出的SwarmPath系统不仅提供了平滑的避障功能，还使智能体能够高效通过狭窄通道，将总旅行时间减少30%，同时确保无人机连接的安全性。最后，结果还表明，模拟环境与真实环境之间的差异，无人机轨迹的平均绝对百分比误差（APE）为6%。这突显了我们的解决方案在现实场景中的可靠性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
