Large-scale micro-aerial vehicle (MAV) swarms provide promising solutions for situational awareness in applications such as environmental monitoring, urban surveillance, search and rescue, and so on. However, these scenarios do not provide localization infrastructure and limit cost and size of on-board capabilities of individual nodes, which makes it challenging for nodes to autonomously navigate to suitable preassigned locations. In this article, we present H-DrunkWalk , a collaborative and adaptive technique for heterogeneous MAV swarm navigation in environments not formerly preconditioned for operation. Working with heterogeneous MAV swarm, the H-DrunkWalk achieves high accuracy through collaboration but still maintains a low cost of the entire swarm. The heterogeneous MAV swarm consists of two types of nodes: (1) basic MAVs with limited sensing, communication, computing capabilities and (2) advanced MAVs with premium sensing, communication, computing capabilities. The key focus behind this networked MAV swarm research is to (1) rely on collaboration to overcome limitations of individual nodes and efficiently achieve system-wide sensing objectives and (2) fully take advantage of advanced MAVs to help basic MAVs improve their performance. The evaluations based on real MAV testbed experiments and large-scale physical-feature-based simulations show that compared to the traditional non-collaborative and non-adaptive method (dead reckoning with map bias), our system achieves up to 6× reductions in location estimation errors, and as much as 3× improvements in navigation success rate under the given time and accuracy constraints. In addition, by comprehensively considering the environment, heterogeneous structure, and quality of location estimation, our H-DrunkWalk brings 2× performance improvement (on average) as that of a hardware upgrade.

中文翻译：

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H-DrunkWalk

大型微型飞行器 (MAV) 群为环境监测、城市监控、搜索和救援等应用中的态势感知提供了有前景的解决方案。然而，这些场景不提供本地化基础设施并限制单个节点的板载能力的成本和大小，这使得节点自主导航到合适的预分配位置具有挑战性。在这篇文章中，我们介绍H-DrunkWalk，一种用于异构 MAV 集群导航的协作和自适应技术，用于在以前未预先进行操作的环境中。与异构 MAV 群一起工作，H-DrunkWalk通过协作实现了高精度，但仍然保持了整个swarm的低成本。异构 MAV 群由两种类型的节点组成：（1）具有有限传感、通信、计算能力的基本 MAV 和（2）具有高级传感、通信、计算能力的高级 MAV。这项网络化 MAV 群研究背后的关键焦点是 (1) 依靠协作来克服单个节点的限制并有效地实现系统范围的传感目标，以及 (2) 充分利用先进的 MAV 来帮助基本 MAV 提高其性能。基于真实 MAV 试验台实验和大规模基于物理特征的模拟的评估表明，与传统的非协作和非自适应方法（带有地图偏差的航位推算）相比，我们的系统在给定的时间和精度约束下，位置估计误差减少了 6 倍，导航成功率提高了 3 倍。此外，通过综合考虑环境、异构结构和位置估计质量，我们的H-DrunkWalk与硬件升级相比，带来 2 倍的性能提升（平均）。