DeRP：信息受限环境下使用递归分支的供电网络自组装算法
DeRP: An Algorithm for Self-Assembly of Power-Delivery Networks using Recursive Branching in Information-Limited Environments
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中文总结 AI 辅助
本文提出DeRP算法，使机器人群仅通过局部通信和方位感知自组装供电网络，其性能接近全局基准，可在传统基础设施部署困难的环境中实现自适应供电。
中文摘要 AI 辅助
在非结构化野外环境中，使用预先规划的有线网络或基于电池的解决方案为分布式设备提供持续供电，会带来巨大的基础设施和物流挑战。本文提出了树突状递归枢轴算法（Dendritic Recursive Pivoting，DeRP），这是一种用于机器人群多目标网络形成的去中心化框架，仅基于局部通信和对汇聚点（Sinks）的方位感知运行。我们设想的系统中，机器人作为传导介质，从公共源自组装成供电网络，在局部选定的枢轴点处形成分支，这些枢轴点近似于斯坦纳树（Steiner trees）的斯坦纳点，从而高效路由至多个汇聚点。该分支操作以递归方式执行，无需全局规划即可实现可扩展且自适应的网络形成。我们从总网络长度和估计功率损耗两个方面对所提方法进行评估，并与需要完整知晓汇聚点位置的全局基准（如最小生成树和斯坦纳树解决方案GeoSteiner）进行定量比较。具体而言，我们发现DeRP形成的网络渐近达到全局最小长度的约125%，且相对于欧几里得斯坦纳树，功率损耗降低了65%。此外，我们通过测量模拟完成时间来经验性地表征缩放行为，随着汇聚点和机器人数量增加，我们发现对于多达100个汇聚点，该缩放呈亚线性。所提方法能够在传统基础设施部署具有挑战性的环境中实现弹性、自适应的供电。
英文摘要
Delivering sustained power to distributed equipment in unstructured field environments using pre-planned wired networks or battery-based solutions presents significant infrastructure and logistics challenges. This paper presents Dendritic Recursive Pivoting (DeRP), a decentralized framework for multi-target network formation in robot swarms based solely on local communication and bearing-based sensing toward sinks. We envision a system in which robots, acting as a conduit, self-assemble a power network from a common source, forming branches at locally selected pivot points that approximate the Steiner points of Steiner trees to efficiently route to multiple Sinks. This branching operation is performed recursively to enable scalable and adaptive network formation without global planning. The proposed method is evaluated in terms of the total network length and estimated power loss, and is quantitatively compared against global baselines such as the Minimum Spanning Tree and Steiner tree solutions (GeoSteiner), which require complete knowledge of Sink locations. Specifically, we found that the networks formed by DeRP asymptotically form approximately 125\% of the global minimum length while reducing power losses to 65\% relative to Euclidean Steiner trees. In addition, we empirically characterize scaling behavior by measuring simulation completion time as the number of Sinks and robots increases, and find that this scaling was sub-linear for up to 100 sinks. The proposed approach enables resilient, adaptive power delivery in environments where deployment of traditional infrastructure is challenging.
发表机构
- New Jersey Institute of Technology（新泽西理工学院）
- NJIT Grace Hopper AI Research Institute（NJIT格蕾丝·霍珀人工智能研究院）
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