AoI-Aware Multi-Robot Sensing and Transport on Connected Graphs
For multi-robot monitoring systems, this work provides a theoretical framework and optimal strategies to minimize AoI, though it is an incremental extension of known resource allocation and routing concepts to a specific setting.
This paper addresses the problem of minimizing Age of Information (AoI) in a multi-robot sensing and transport system on a connected graph. It derives a lower bound for AoI and proposes a greedy water-filling algorithm for optimal sensing allocation, along with a conveyor architecture that achieves the bound in a full-conveyor regime.
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.