AoI-Aware Multi-Robot Sensing and Transport on Connected Graphs

TL;DR

This paper develops an AoI optimization framework for multi-robot sensing and transport on connected graphs, deriving bounds and proposing optimal resource allocation and deployment strategies.

Contribution

It introduces a novel AoI lower bound decomposition, a convex resource allocation problem, and an Euler-walk conveyor architecture achieving optimal AoI performance.

Findings

Derived a network-wide AoI lower bound combining sensing and propagation terms.

Formulated and solved a convex resource allocation problem for sensing.

Designed a conveyor architecture that attains the AoI lower bound.

Abstract

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.