Enabling Urgency-aware Robot Swarm Intralogistics using Smart IoT Tags

2026-08-05

AI Summary

This work addresses the limitation of conventional decentralized robotic swarms in differentiating cargo urgency, which results in high-priority items—such as pharmaceuticals and perishables—experiencing the same waiting times as ordinary goods. To resolve this, the authors propose an item-level dynamic prioritization mechanism that equips payloads with ultra-low-power IoT tags broadcasting urgency levels via BLE, enabling robots to autonomously balance path distance against task priority without centralized coordination. By embedding timeliness information directly into the cargo itself, the approach integrates decentralized swarm control with a priority–distance task selection strategy. Physical experiments demonstrate an improvement in priority alignment from 0.41 to 0.64, while simulations show that under system scaling, P95 latency is reduced by 5.2%–11.8%, priority alignment increases by 41.7%–51.6%, and throughput remains above 98.8% of baseline levels.

Abstract

Warehouse items differ in how urgently they must be moved: perishable goods, pharmaceutical shipments, and just-in-time production materials must be delivered sooner than the rest of the stock. Decentralised robot swarms suit warehouses that cannot justify fixed automation infrastructure, but current swarm controllers treat all items alike or rely on an external scheduler to set priorities, so urgent items wait as long as ordinary ones. This paper presents a swarm logistics system in which each warehouse carrier holds an ultra-low-power Internet-of-Things (IoT) tag that broadcasts the urgency of its item over Bluetooth Low Energy (BLE). Robots read these broadcasts directly and weigh urgency against travel distance when choosing which carrier to serve, so prioritisation happens at the item level without central scheduling. The system is evaluated in simulation and validated on real robots and IoT-tagged carriers against a proximity-only baseline. In the physical trials, priority alignment (i.e. proportion of urgent items served first), improved from 0.41 to 0.64, with a nonsignificant trend toward lower 95th-percentile (P95) delivery latency and throughput within 1.2% of the baseline. In simulation, the benefit grew with system size: across three larger configurations, P95 latency fell by 5.2% to 11.8% and priority alignment improved by 41.7% to 51.6%. Attaching urgency to the items themselves therefore allows a decentralised swarm to serve time-critical stock sooner while keeping the low infrastructure requirements that make swarm systems attractive for warehouse automation.

Problem

• robot swarm
• intralogistics
• urgency-aware
• IoT tags
• priority scheduling

Innovation

• urgency-aware
• robot swarm
• IoT tags
• decentralized scheduling
• intralogistics