Adaptive distributed formation maintenance for multiple UAVs: Exploiting proximity behavior observations

Abstract

The formation maintenance of multiple unmanned aerial vehicles (UAVs) based on proximity behavior is explored in this study. Individual decision-making is conducted according to the expected UAV formation structure and the position, velocity, and attitude information of other UAVs in the azimuth area. This resolves problems wherein nodes are necessarily strongly connected and communication is strictly consistent under the traditional distributed formation control method. An adaptive distributed formation flight strategy is established for multiple UAVs by exploiting proximity behavior observations, which remedies the poor flexibility in distributed formation. This technique ensures consistent position and attitude among UAVs. In the proposed method, the azimuth area relative to the UAV itself is established to capture the state information of proximal UAVs. The dependency degree factor is introduced to state update equation based on proximity behavior. Finally, the formation position, speed, and attitude errors are used to form an adaptive dynamic adjustment strategy. Simulations are conducted to demonstrate the effectiveness and robustness of the theoretical results, thus validating the effectiveness of the proposed method.

摘要

本文研究了基于邻近行为信息状态反馈的多无人机编队队形集结与保持问题。无人机个体根据期望的编队结构以及邻居无人机所在方位角区域的位置、速度和姿态信息进行自主决策, 解决了传统分布式编队控制方法中通信拓扑强连通问题和多维度状态信息耦合问题。针对现有分布式编队控制算法中多无人机编队机动灵活性差的问题, 以邻近无人机行为信息为观测量建立了一种自适应的分布式编队保持模型。通过已建立的相对方位角区域来获取邻近无人机的状态信息, 确保编队中无人机之间位置、速度和姿态的一致性。另外, 在邻近行为的状态更新方程中引入关联度因子, 利用编队间的位置、速度和姿态误差构成自适应动态调整策略。仿真结果验证了本文所提方法的有效性和鲁棒性。