无需通信的无人机群在障碍环境中保持连通性导航
Connectivity Preserving Decentralized UAV Swarm Navigation in Obstacle-laden Environments without Explicit Communication
- 用控制屏障函数替代斥力势场设计控制输入
- 通过软约束优化最小调整，避免振荡和约束违反
- 支持真实四轴飞行器实验，适合无人集群协同场景
本文提出一种新型控制方法，使一群无人机在复杂障碍环境中实现无显式通信的连通性保持导航。与基于人工势场（APFs）的方法相比，该方法采用控制屏障函数（CBFs）构建约束，先确定期望控制输入，再通过带软约束的数值优化问题对输入进行最小化修正，有效克服了原有方法中常见的振荡行为和频繁约束违反问题。此外，还提出一种无需数值优化的近似方法。通过大量仿真对比了基于CBF的方法与基于APF的方法性能，并展示了真实四轴飞行器平台上的实验结果。
原文摘要 · Abstract (English)
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.
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