让无人机和地面机器人在狭小空间安全协同，支持无人机在移动车上起降。
Barriers on the EDGE: A scalable CBF architecture over EDGE for safe aerial-ground multi-agent coordination
- 用时变控制屏障函数约束碰撞、着陆与空间限制。
- 通过边缘计算动态激活相关约束，降低网络负担。
- 适合需要高安全性的多机器人协同场景，如救援或物流。
本文提出一种用于空中（无人机）与地面（无人车）机器人在受限任务空间中安全协同的控制架构。考虑空中与地面操作耦合的情况，利用无人机可在移动地面机器人上着陆的能力。方法采用时变控制屏障函数（CBF），实现三类安全约束：（i）机器人间避撞，（ii）无人机在移动无人车上着陆，（iii）任务空间边界限制。针对随机器人数量增加导致的CBF约束急剧增长问题，提出混合中心-分布式协调机制，由部署于边缘计算集群的中心节点（Watcher）实时判断并激活每个空地机器人相关的约束集，显著降低网络复杂度与机器人本地算力需求。各机器人本地运行名义控制器与安全过滤器，以分布式方式执行约束，有效应对延迟等网络非理想情况。
原文摘要 · Abstract (English)
In this article, we propose a control architecture for the safe, coordinated operation of a multi-agent system with aerial (UAVs) and ground (UGVs) robots in a confined task space. We consider the case where the aerial and ground operations are coupled, enabled by the capability of the aerial robots to land on moving ground robots. The proposed method uses time-varying Control Barrier Functions (CBFs) to impose safety constraints associated with (i) collision avoidance between agents, (ii) landing of UAVs on mobile UGVs, and (iii) task space restriction. Further, this article addresses the challenge induced by the rapid increase in the number of CBF constraints with the increasing number of agents through a hybrid centralized-distributed coordination approach that determines the set of CBF constraints that is relevant for every aerial and ground agent at any given time. A centralized node (Watcher), hosted by an edge computing cluster, activates the relevant constraints, thus reducing the network complexity and the need for high onboard processing on the robots. The CBF constraints are enforced in a distributed manner by individual robots that run a nominal controller and safety filter locally to overcome latency and other network nonidealities.
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