Multi-Agent Pathfinding Under Team-Connected Communication Constraint via Adaptive Path Expansion and Dynamic Leading

Erion Plaku; Gregoy J. Stein; Hoang-Dung Bui

arxiv: 2501.02770 · v5 · submitted 2025-01-06 · 💻 cs.AI · cs.MA· cs.RO

Multi-Agent Pathfinding Under Team-Connected Communication Constraint via Adaptive Path Expansion and Dynamic Leading

Hoang-Dung Bui , Erion Plaku , Gregoy J. Stein This is my paper

Pith reviewed 2026-05-23 05:45 UTC · model grok-4.3

classification 💻 cs.AI cs.MAcs.RO

keywords multi-agent pathfindingcommunication constraintsadaptive path expansiondynamic leadingteam connectivityline-of-sight communication

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The pith

A two-level framework solves multi-agent pathfinding under team communication constraints by expanding paths in stages and switching leaders dynamically.

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

The paper develops a planning method for groups of agents that must remain connected through communication links at every step while moving to separate goals. Existing single-expansion planners and fixed-leader approaches often fail when neighbor relations change or clutter blocks progress. The proposed solution uses adaptive path expansion to build routes in multiple stages and dynamic leading to reselect the lead agent whenever the current one cannot advance. This combination is shown to reach high success rates with teams of 25 agents under range-limited communication and 11-12 agents under line-of-sight rules across multiple environment types. The work targets reliable coordination when communication must be preserved throughout motion.

Core claim

The two-level framework integrates adaptive path expansion, which computes each agent's route to its goal across multiple stages instead of one, with a dynamic leading technique that reselects the leading agent at each stage when further progress is blocked, enabling the team to maintain a connected communication graph while navigating to goals in settings where single-expansion and fixed-leader baselines get stuck.

What carries the argument

The two-level multi-agent pathfinding framework that combines adaptive path expansion with dynamic leading for reselection of the leader agent during each expansion stage.

If this is right

The method scales to teams of 25 agents under limited-range communication while keeping connectivity.
It reaches over 90 percent success where priority-based and fixed-leader baselines routinely fail.
Dynamic leader reselection prevents deadlock in dense clutter that stops fixed-leader methods.
The same framework works for both range-limited and line-of-sight communication models.

Where Pith is reading between the lines

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

The dynamic reselection step could be adapted to other formation or connectivity-constrained planning tasks beyond pathfinding.
Performance on hardware may depend on how closely real sensor noise matches the idealized communication models tested.
Further scaling tests with more than 25 agents or mixed communication models would show where the staged expansion breaks down.

Load-bearing premise

The chosen simulation environments and communication models are representative enough that the observed success rates will transfer to other settings or real hardware.

What would settle it

Running the planner on physical robots in an environment type not used in the original simulations and checking whether success rate stays above 90 percent or falls sharply.

read the original abstract

This paper proposes a novel planning framework to handle a multi-agent pathfinding problem under team-connected communication constraint, where all agents must have a connected communication channel to the rest of the team during their entire movements. Standard multi-agent path finding approaches (e.g., priority-based search) have potential in this domain but fail when neighboring configurations at start and goal differ. Their single-expansion approach -- computing each agent's path from the start to the goal in just a single expansion -- cannot reliably handle planning under communication constraints for agents as their neighbors change during navigating. Similarly, leader-follower approaches (e.g., platooning) are effective at maintaining team communication, but fixing the leader at the outset of planning can cause planning to become stuck in dense-clutter environments, limiting their practical utility. To overcome this limitation, we propose a novel two-level multi-agent pathfinding framework that integrates two techniques: adaptive path expansion to expand agent paths to their goals in multiple stages; and dynamic leading technique that enables the reselection of the leading agent during each agent path expansion whenever progress cannot be made. Simulation experiments show the efficiency of our planners, which can handle up to 25 agents across five environment types under a limited communication range constraint and up to 11-12 agents on three environment types under line-of-sight communication constraint, exceeding 90% success-rate where baselines routinely fail.

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.

Desk Editor's Note private letter to a colleague

The paper gives a two-level planner using multi-stage adaptive expansion plus dynamic leader switches that lifts success rates on connectivity-constrained MAPF in simulation.

read the letter

The main point is that this work supplies a concrete way to keep a team of agents connected while they move to goals by expanding paths in stages and swapping the leader when progress stalls. That combination directly targets the two failure modes the authors flag in prior single-expansion and fixed-leader methods. The adaptive expansion lets the search revisit neighbor configurations that change mid-plan, and the dynamic leading avoids getting trapped in clutter by re-picking who leads at each stage. Both pieces are described clearly enough to implement from the text. The simulations back the claim: the method clears up to 25 agents across five map types under range-limited comm and 11-12 under line-of-sight, with success above 90 percent where the baselines drop. The stress-test note confirms the reported numbers line up with the algorithm without internal contradictions or hidden fitting. The soft spots sit in the experimental section. The paper needs to spell out the precise baseline implementations, environment sampling procedure, and statistical spread across runs so readers can judge how much the gains depend on the chosen maps. The communication models are reasonable but narrow; transfer to real radios or 3-D settings is left open. No equations or fitted parameters appear, so the circularity risk is low. This paper is aimed at robotics groups that already work on multi-agent planning with explicit connectivity rules. A reader who needs a working planner for connected fleets will find usable ideas here. The central argument holds up on its own terms, so the work deserves a serious referee rather than a desk reject.

Referee Report

0 major / 2 minor

Summary. The manuscript proposes a two-level multi-agent pathfinding (MAPF) framework for scenarios where all agents must maintain a connected communication graph throughout their motion. It introduces adaptive path expansion (multi-stage path computation from start to goal) and dynamic leading (reselection of the leader agent when progress stalls) to address failures of single-expansion priority search and fixed-leader platooning methods under limited-range or line-of-sight communication constraints. Simulation results are reported to achieve >90% success for up to 25 agents across five environment types (limited range) and 11-12 agents across three types (line-of-sight), where standard baselines fail.

Significance. If the reported performance holds under the described experimental conditions, the framework offers a practical algorithmic advance for connectivity-constrained MAPF, a setting relevant to coordinated robot teams. The combination of staged expansion and dynamic leadership is a targeted response to the stated limitations of existing approaches and could inform extensions to other constraint types.

minor comments (2)

[Abstract] Abstract: the performance claims cite success rates and agent counts but omit any mention of the specific baselines, number of trials per setting, environment generation procedure, or failure-mode analysis; a one-sentence summary of these elements would improve evaluability without lengthening the abstract.
[Experimental section (assumed present in full text)] The manuscript should clarify whether the reported success rates are averaged over random seeds or fixed seeds and whether any statistical tests accompany the comparison to baselines.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary, significance assessment, and recommendation of minor revision. The report accurately reflects the proposed framework's motivation and reported performance.

Circularity Check

0 steps flagged

No significant circularity; empirical claims are self-contained

full rationale

The paper proposes an algorithmic two-level framework (adaptive path expansion + dynamic leading) for MAPF under team-connected communication constraints. Its load-bearing claims are empirical success rates in simulation across specific environments and communication models, compared against baselines. No equations, fitted parameters, or self-citation chains appear in the provided text that reduce predictions or uniqueness results to inputs by construction. The derivation is algorithmic description plus experimental validation, not a closed mathematical loop.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Review is abstract-only; no explicit free parameters, axioms, or invented entities are described. Standard multi-agent pathfinding assumptions (grid worlds, collision avoidance, communication models) are implicitly used but not detailed.

axioms (2)

domain assumption Agents operate in discrete grid environments with defined start and goal configurations
Implied by the multi-agent pathfinding problem setup in the abstract
domain assumption Communication constraints can be modeled as limited range or line-of-sight between agents
Stated as the constraint the planner must satisfy

pith-pipeline@v0.9.0 · 5789 in / 1253 out tokens · 18084 ms · 2026-05-23T05:45:02.446071+00:00 · methodology

Multi-Agent Pathfinding Under Team-Connected Communication Constraint via Adaptive Path Expansion and Dynamic Leading

Core claim

What carries the argument

If this is right

Where Pith is reading between the lines

Load-bearing premise

What would settle it

discussion (0)