面向机器人群体最小长度通信网络的去中心化构建
Towards Decentralized Formation of Minimum-Length Communication Networks Using Robot Swarms
- University of Bristol（布里斯托大学）
- University of Sheffield（谢菲尔德大学）
- Technical University of Darmstadt（达姆施塔特工业大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出一种完全去中心化的机器人群体框架，通过局部交互动态构建和优化通信网络，最小化网络长度并保证连通性，实验验证接近最优性能。
AI中文摘要：
在缺乏基础设施的环境中，多机器人任务通常依赖于空间分离位置之间的可靠通信链路。我们提出了一种完全去中心化的框架，用于构建和动态维护通信网络，无需集中式拓扑规划或全球定位基础设施。仅由局部交互驱动，机器人在遵守通信约束的同时，重新配置局部网络拓扑并调整其物理位置，以最小化整体网络长度。形式化分析表明，我们的局部重配置操作保证了网络的持续连通性，每次分支转移都严格减少网络长度，并将最坏情况性能限制在最短星型树内。实体仿真和物理多机器人实验证实，我们的方法形成的网络长度接近集中计算的欧几里得斯坦纳树。此外，系统能动态适应移动目标，并通过仅使用必要的连接器来优化部署，将多余的机器人保留用于辅助任务。这项工作使自主群体能够在通信受限环境中自组织自适应的临时通信基础设施，可支持从地下探索到行星任务等多种应用。
英文摘要：
Multi-robot missions in infrastructure-denied environments frequently rely on reliable communication links between spatially separated locations. We propose a fully decentralized framework for constructing and dynamically maintaining communication networks without centralized topology planning or global positioning infrastructure. Driven strictly by local interactions, robots reconfigure local network topologies and adjust their physical positions to minimize overall network length while adhering to communication constraints. Formal analysis shows that our local reconfiguration operations guarantee continuous network connectivity, strictly decrease network length with every branch transfer, and bound worst-case performance to the shortest starlike tree. Embodied simulations and physical multi-robot experiments confirm that our approach forms networks near the length of centrally computed Euclidean Steiner trees. Additionally, the system dynamically adapts to moving targets and optimizes deployment by utilizing only necessary connectors, preserving excess robots for auxiliary tasks. This work enables autonomous swarms to self-organize adaptive ad hoc communication infrastructure in communication-denied environments, which could support applications varying from subterranean exploration to planetary missions.
