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Article | Open Access

SW-DWNS: A Single-Wave Autonomous Navigation System in Partially Observable, Highly Dynamic Warehouses

Xianhui Fan1Zongwei Li1( )Yuxuan Zhai1Zhenyu Li2
School of Economics and Management, Shanghai Institution of Technology, Shanghai, China
School of Cultural Heritage and Information Management, Shanghai University, Shanghai, China
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Abstract

Single-wave order picking in dynamic warehouses is a sequential multi-goal navigation problem. A robot must visit an ordered set of shelves and then a delivery station while avoiding moving obstacles under partial observability. Existing approaches either entangle long-horizon task logic with low-level obstacle avoidance or rely on static-environment assumptions that limit responsiveness in dynamic settings. This paper proposes the Single-Wave Dynamic Warehouse Navigation System (SW-DWNS), a lightweight scheduling framework that extends a pretrained ColorDynamic point-to-point local planner to ordered warehouse picking without retraining. The scheduler maintains a shelf queue, exposes only the active subgoal to the local policy, and advances the queue through an arrival-gated state machine with a geometrically derived pickup threshold. We evaluate SW-DWNS in a controlled dynamic warehouse simulation benchmark with three layouts, pickup horizons K ∈ {1, …, 5}, stochastic obstacle motion, sensor noise, and obstacle-size randomization. Across the full layout–horizon grid, SW-DWNS achieves 87.3% average success, compared with 26.8% for A* search combined with the Dynamic Window Approach (A* + DWA) and 1.3% for ColorDynamic-only. The gain is not obtained by sacrificing safety: the average collision rate is reduced from 69.0% for A* + DWA to 7.5% for SW-DWNS. Factor-wise ablation shows uneven robustness across disturbances: sensing noise and obstacle-size randomization have limited impact (main effects of +1 and +4.5 percentage points), whereas disabling obstacle motion costs 33 percentage points and shifts failures from collision to timeout. A pickup-threshold sweep further shows that reported success rates become inflated outside the admissible geometric band ε ∈ [14, 31] cm. These results show that a decoupled scheduling layer can make a pretrained dynamic local planner effective for sequential warehouse picking, while also revealing the conditions under which that extension remains valid.

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Computers, Materials & Continua
Article number: 76

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Cite this article:
Fan X, Li Z, Zhai Y, et al. SW-DWNS: A Single-Wave Autonomous Navigation System in Partially Observable, Highly Dynamic Warehouses. Computers, Materials & Continua, 2026, 88(3): 76. https://doi.org/10.32604/cmc.2026.083491

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Received: 04 April 2026
Accepted: 11 June 2026
Published: 23 July 2026
© The Author 2026.

This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.