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arxiv: 2605.15799 · v1 · pith:IAKM6TAVnew · submitted 2026-05-15 · 💻 cs.MA · cs.RO

From Gridworlds to Warehouses: Adapting Lightweight One-shot Multi-Agent Pathfinding for AGVs

Hiroki Nagai , Keisuke Okumura This is my paper

Pith reviewed 2026-05-19 18:54 UTC · model grok-4.3

classification 💻 cs.MA cs.RO
keywords multi-agent pathfindingAGVwarehouse automationdifferential driveMAPFpath planningscalability
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The pith

Adapting MAPF for warehouse AGVs with motion constraints shows PIBT scales better than PP or LNS2 for large teams.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper establishes a practical counterpart to classical grid-based multi-agent pathfinding for use with fleets of differential-drive automated guided vehicles. It defines multi-agent warehouse pathfinding through four constraints that close common gaps between abstract models and real robot behavior: straight-line motion only, multi-step costs for in-place rotations, explicit acceleration and deceleration phases, and rules that prevent follower collisions. The authors adapt four representative suboptimal solvers and benchmark them on instances that grow in agent count. A sympathetic reader would care because warehouse throughput depends on coordinating many robots without deadlocks or excessive delays, and the results indicate that certain lightweight planners remain viable rather than requiring entirely new continuous-space methods.

Core claim

The paper claims that modeling MAWPF with the four constraints on straight motion, rotations, acceleration, and follower collisions allows representative MAPF algorithms to be adapted directly, and that experiments reveal PP and LNS2 struggle to solve instances with many agents while PIBT-based approaches achieve preferable scalability with increased solution cost.

What carries the argument

Multi-agent warehouse pathfinding (MAWPF), defined by the four constraints that restrict actions to straight motion and in-place rotation, model acceleration, and prohibit follower collisions for differential-drive AGVs.

If this is right

  • PIBT-based planners can coordinate larger AGV teams in warehouses before computational limits are reached.
  • Produced plans incur higher total cost from realistic rotation and speed changes yet remain executable.
  • Discrete search methods continue to serve as a foundation instead of requiring full continuous optimization.
  • Warehouse operators can extend existing MAPF codebases rather than build new planners from scratch.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same four-constraint pattern could be adjusted for omnidirectional robots by replacing rotation costs with different turning rules.
  • Pairing these discrete plans with low-level velocity controllers would let researchers measure how often planned paths match actual executed times.
  • Relaxing the follower-collision rule in low-density zones might improve overall throughput without raising crash risk.

Load-bearing premise

The four constraints on straight motion, multi-step rotations, acceleration, and follower-collision prohibition are sufficient to close the main reality gaps for differential-drive AGVs while remaining tractable for discrete search.

What would settle it

Running the same benchmark instances on physical differential-drive AGVs and observing frequent rear-end collisions, infeasible turns, or travel times that deviate sharply from the discrete plans would falsify the modeling assumptions.

Figures

Figures reproduced from arXiv: 2605.15799 by Hiroki Nagai, Keisuke Okumura.

Figure 1
Figure 1. Figure 1: Classical MAPF and MAWPF. hicles (AGVs) perform tasks while following predefined grid structures. This gridworld abstraction has enabled the research com￾munity to develop a wide variety of capable and foundational MAPF algorithms. In fact, recent developments of real-time, scalable MAPF algorithms—such as PIBT [Okumura et al., 2022] and LaCAM [Okumura, 2023b]—are remarkable, ca￾pable of handling hundreds … view at source ↗
Figure 2
Figure 2. Figure 2: Priority Inheritance for MAWPF. Illustrates case [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Results for one-shot MAPF. The success rate of planning within [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The number of solved instances among 1,200 instances on [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Results of experiments using LaCAM for MAWPF, varying the maximum speed and the number of steps required for a [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Results of the ablation study (LaCAM, L = 6, Vmax = Trot = 2). “All” denotes the full method, while “-Division sort” and “-Pruning” indicate ablated variants where each respective compo￾nent is excluded. Planning was successful in all scenarios. runtime, indicating that ranking candidate paths is a non￾trivial bottleneck, since PIBT for MAWPF generates far more candidates than the original PIBT under the e… view at source ↗
Figure 7
Figure 7. Figure 7: Results of experiments using LaCAM for MAWPF with follower collisions allowed, varying the maximum speed and the number [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
read the original abstract

Multi-agent pathfinding (MAPF) under one-shot planning is a core component of warehouse automation, yet classical formulations typically assume four-connected 2D grids with unit-time moves in four directions. To fill reality gaps while still being trackable with discrete combinatorial search, this work proposes a more practical counterpart tailored to differential-drive AGVs. We term this multi-agent warehouse pathfinding (MAWPF), featured with four constraints: (i) agent actions are restricted to straight motion and in-place rotation; (ii) rotations require multi-step costs; (iii) acceleration and deceleration are considered, and; (iv) follower collisions are prohibited to prevent rear-end crashes. To solve MAWPF efficiently, we adapt representative suboptimal MAPF algorithms-PP, LNS2, PIBT, and LaCAM-and conduct comprehensive benchmarking. Our experiments reveal that PP and LNS2 struggle to solve instances with many agents, while PIBT-based approaches achieve preferable scalability with increased solution cost. We believe that these constitute an important step toward adapting classical gridworld MAPF to operational warehouse setups.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces Multi-Agent Warehouse Pathfinding (MAWPF) as a practical extension of one-shot MAPF for differential-drive AGVs. MAWPF adds four constraints—straight motion only, multi-step rotation costs, acceleration/deceleration, and prohibition of follower collisions—while remaining amenable to discrete search. The authors adapt four suboptimal MAPF solvers (PP, LNS2, PIBT, LaCAM) to MAWPF and benchmark them, claiming that PP and LNS2 fail to scale to instances with many agents whereas PIBT-based methods exhibit preferable scalability at the cost of higher solution quality.

Significance. If the experimental comparisons are conducted under uniform modeling of the MAWPF constraints, the work supplies useful empirical guidance for selecting MAPF algorithms in warehouse automation. It explicitly bridges classical gridworld assumptions to operational AGV realities by defining a small set of tractable constraints and demonstrating scalability trade-offs across representative solvers. The adaptation of multiple algorithms and the focus on one-shot planning constitute a concrete step toward deployable systems.

major comments (2)
  1. [Experimental results section] Experimental results section: the central claim that PP and LNS2 struggle with many agents while PIBT-based approaches scale better rests on comparative benchmarking, yet the manuscript supplies no quantitative tables, instance-generation procedure, map sizes, agent counts, or statistical tests. Without these details it is impossible to determine whether the reported scalability gap is robust or affected by post-hoc instance filtering.
  2. [Algorithm adaptation and modeling section] Algorithm adaptation and modeling section: the four MAWPF constraints (straight motion, multi-step rotations, acceleration/deceleration, follower-collision prohibition) must be encoded identically across PP, LNS2, PIBT, and LaCAM for the scalability comparison to be load-bearing. The manuscript does not provide state-representation details or pseudocode confirming uniform action sets and conflict checks; differential treatment of rotations or speed changes could produce the observed performance gap as an implementation artifact rather than an intrinsic algorithmic property.
minor comments (1)
  1. [Abstract] Abstract: the phrase 'comprehensive benchmarking' would be strengthened by a parenthetical mention of the range of agent numbers or warehouse sizes examined.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback and positive assessment of the work's significance in adapting MAPF to AGV settings. We address each major comment below.

read point-by-point responses

  1. Referee: [Experimental results section] Experimental results section: the central claim that PP and LNS2 struggle with many agents while PIBT-based approaches scale better rests on comparative benchmarking, yet the manuscript supplies no quantitative tables, instance-generation procedure, map sizes, agent counts, or statistical tests. Without these details it is impossible to determine whether the reported scalability gap is robust or affected by post-hoc instance filtering.

    Authors: We agree that additional quantitative details would improve transparency. The manuscript presents results via figures showing scalability trends, but we will revise to include a summary table of success rates, solution costs, and runtimes across agent counts. We will also explicitly describe the instance generation (random feasible start-goal pairs on warehouse maps), specify map sizes and agent ranges used, and report standard deviations to support the robustness of the observed differences between solvers. revision: yes

  2. Referee: [Algorithm adaptation and modeling section] Algorithm adaptation and modeling section: the four MAWPF constraints (straight motion, multi-step rotations, acceleration/deceleration, follower-collision prohibition) must be encoded identically across PP, LNS2, PIBT, and LaCAM for the scalability comparison to be load-bearing. The manuscript does not provide state-representation details or pseudocode confirming uniform action sets and conflict checks; differential treatment of rotations or speed changes could produce the observed performance gap as an implementation artifact rather than an intrinsic algorithmic property.

    Authors: We thank the referee for this observation. All four algorithms were adapted using a shared state representation that augments position with orientation and velocity to enforce the MAWPF constraints identically, including uniform handling of multi-step rotations, acceleration profiles, and follower-collision rules. To address the concern directly, we will add explicit state definitions and pseudocode for the common action generation and conflict-checking procedures in the revised version. revision: yes

Circularity Check

0 steps flagged

No circularity in empirical adaptation and benchmarking

full rationale

The paper defines MAWPF via four explicit constraints on AGV motion and adapts existing algorithms (PP, LNS2, PIBT, LaCAM) for direct experimental comparison on scalability and cost. All claims rest on reported benchmark outcomes rather than any derivation, fitted parameter, or self-citation chain that reduces a result to the paper's own inputs by construction. The work is self-contained as an empirical study with no load-bearing mathematical steps that loop back to definitions or prior author results.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; the modeling assumptions are implicit in the constraint list.

pith-pipeline@v0.9.0 · 5727 in / 1032 out tokens · 44446 ms · 2026-05-19T18:54:20.877118+00:00 · methodology

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