多机器人协同感知与运输，降低信息时效性延迟。
AoI-Aware Multi-Robot Sensing and Transport on Connected Graphs
- 分阶段优化：先算最优感知分配，再设计运输路径
- 感知延迟由群组平均时间决定，传输延迟由最短路径决定
- 适合需要实时数据采集的物联网或监控系统
一群移动机器人在连通图上监测空间分布的过程并把测量结果传回基站，以感知开始时刻起算的年龄（AoI）为指标，同时考虑随机并行感知延迟和基于跳数的传播延迟。在非基站节点，多个机器人可协作感知，形成依赖节点的几何群组感知时间；其余机器人作为移动传送带，在单位时间边上传输样本。论文首先推导出按节点和全网的AoI下界，其分解为由均值群组感知时间决定的感知项和由最短路径距离决定的传播项。随后证明，最小化感知项等价于一个可分离的离散凸资源分配问题，可通过贪心水填充算法最优求解。构建了基于最短路径树的传送架构，并通过欧拉回路部署实现全传送模式下的下界可达。数值仿真展示了感知分配与传送部署对AoI性能的影响。
原文摘要 · Abstract (English)
A team of mobile robots monitors spatially distributed processes and delivers measurements to a base, where AoI is measured from sensing start, capturing both stochastic parallel sensing delays and hop-based propagation. At each non-base node, multiple robots may collaborate, yielding node-dependent geometric group sensing times, while other robots act as mobile conveyors that transport samples along unit-time edges. The paper first derives a per-node and network-wide AoI lower bound that decomposes into a sensing term, determined by mean group sensing times, and a propagation term, given by shortest-path distances. It then shows that minimizing the sensing component yields a separable discretely convex resource allocation problem, solved optimally by a greedy water-filling algorithm. A shortest-path-tree conveyor architecture with an Euler-walk deployment is constructed and proven to attain the lower bound in a full-conveyor regime. Numerical simulations illustrate the impact of sensing allocation and conveyor deployment on AoI performance.
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