无显式通信的障碍物环境下连通性保持去中心化无人机集群导航
Connectivity Preserving Decentralized UAV Swarm Navigation in Obstacle-laden Environments without Explicit Communication
AI总结：
本文针对障碍物环境下无人机集群在无显式通信时的导航问题，提出基于控制障碍函数的新型控制方法，克服现有人工势场方法的缺陷，通过优化或近似修正控制输入，经仿真和真实实验验证有效性。
摘要：
本文提出一种新型控制方法，用于障碍物环境下的无人机集群导航，无需无人机间数据传输即可保持传感网络连通性。该方法利用控制障碍函数（CBFs）的约束，旨在克服现有基于人工势场（APFs）方法的局限性，如振荡行为和频繁约束违反。具体而言，该方法首先通过考虑CBF约束而非排斥APF来确定期望控制输入，再通过求解含软约束的数值优化问题对期望输入进行最小修正。除优化方法外，还提出无需数值优化的近似方法。通过大量仿真评估所提方法有效性，对比CBF基方法与APF基方法的性能，并给出真实四旋翼无人机的实验结果。
英文摘要：
This paper presents a novel control method for a group of UAVs in obstacle-laden environments while preserving sensing network connectivity without data transmission between the UAVs. By leveraging constraints rooted in control barrier functions (CBFs), the proposed method aims to overcome the limitations, such as oscillatory behaviors and frequent constraint violations, of the existing method based on artificial potential fields (APFs). More specifically, the proposed method first determines desired control inputs by considering CBF-based constraints rather than repulsive APFs. The desired inputs are then minimally modified by solving a numerical optimization problem with soft constraints. In addition to the optimization-based method, we present an approximate method without numerical optimization. The effectiveness of the proposed methods is evaluated by extensive simulations to compare the performance of the CBF-based methods with an APF-based approach. Experimental results using real quadrotors are also presented.
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。
