无人机按空域密度动态调整跟车行为，提升整体效率。
Adaptive Traffic-Following Scheme for Orderly Distributed Control of Multi-Vehicle Systems
- 根据空域密度自动调节跟车程度，实现自适应控制
- 动态跟车使飞行时间减少，仅轻微增加空域混乱度
- 适合大规模分布式无人系统，提升可扩展性
我们提出一种自适应控制方案，使分布式自主多智能体系统中自发形成有序状态。以往研究显示，在高密度条件下，交通跟随行为可降低旅行时间；而在低密度时，选择直接路径更优。本文基于此发现，让飞机根据当前空域状态自主动态调整其交通跟随程度，仅在有益时才跟随其他飞行器。定量分析表明，动态交通跟随行为在仅带来微小额外无序的前提下，显著降低了飞行时间。同时考察了该收益对时间和空间预测范围的敏感性。总体而言，本工作凸显了在分布式自主多智能体系统中引入自组织行为的益处，甚至可能是实现可扩展性的必要条件。
原文摘要 · Abstract (English)
We present an adaptive control scheme to enable the emergence of order within distributed, autonomous multi-agent systems. Past studies showed that under high-density conditions, order generated from traffic-following behavior reduces travel times, while under low densities, choosing direct paths is more beneficial. In this paper, we leveraged those findings to allow aircraft to independently and dynamically adjust their degree of traffic-following behavior based on the current state of the airspace. This enables aircraft to follow other traffic only when beneficial. Quantitative analyses revealed that dynamic traffic-following behavior results in lower aircraft travel times at the cost of minimal levels of additional disorder to the airspace. The sensitivity of these benefits to temporal and spatial horizons was also investigated. Overall, this work highlights the benefits, and potential necessity, of incorporating self-organizing behavior in making distributed, autonomous multi-agent systems scalable.
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