ArXiv

Stigmergic Graph Memory: An Environment-Aware Approach for Many-to-Many Multi-Agent Pickup and Delivery

2026-07-16 · 16:34 UTC ·Aditya Dutta, Joon-Seok Kim ·1 min read
Authors
Aditya Dutta, Joon-Seok Kim
Categories
cs.MA, cs.RO
arXiv
https://arxiv.org/abs/2607.15182v1
PDF
https://arxiv.org/pdf/2607.15182v1

Brief

The paper tackles many-to-many Multi-Agent Pickup and Delivery (MAPD) where requests specify SKUs rather than fixed endpoints. It proposes Stigmergic Graph Memory (SGM), a decaying, environment-aware memory on nodes and edges that biases which feasible goals enter the planner (and how they are ranked) without altering planner validity. Across five layouts, three load levels, and 25 seeds per condition, SGM improved throughput by 20.5–36.7% versus two baselines. Summary based on the provided abstract and metadata; full text was not reviewed.

Why it matters

Stigmergic Graph Memory (SGM) is a bounded, decaying memory layer that records recent execution signals on warehouse nodes and directed edges to rank feasible endpoints and route preferences; it does this without changing collision constraints or planner validity.

Key details

  • In experiments on five layouts × three load levels (15 map-load conditions) with 25 random seeds per condition, SGM beat two reconstructed many-to-many allocation baselines in all conditions, producing paired throughput gains of 20.5%–36.7%.
Source evidence

Abstract

Automated fulfillment warehouses must continuously assign and execute pickup-and-delivery work while avoiding congestion. In many-to-many Multi-Agent Pickup and Delivery (MAPD), a request specifies a stock-keeping unit rather than fixed endpoints, requiring the controller to select an agent, source, and destination before path planning. Existing graph-guidance methods primarily influence routing after goals are fixed, leaving endpoint instantiation uninformed by recent traffic. We introduce Stigmergic Graph Memory (SGM), a bounded, decaying memory layer that records recent execution signals on warehouse nodes and directed edges to rank feasible endpoints and route preferences without altering collision constraints or planner validity. Across paired request streams on five layouts, three load levels, and 25 seeds per condition, SGM outperforms two reconstructed many-to-many allocation baselines in all 15 map-load conditions, with paired throughput gains of 20.5-36.7%. These results show that recent execution memory can improve warehouse throughput by shaping which feasible goals enter the planner, not only how agents travel to already fixed goals.

Comment: 16 pages total: 7 pages main text, 2 pages references, and 7 pages appendix