X-SYCON: Xylem-Inspired Passive Gradient Control for Communication-Free Swarm Response in Dynamic Disaster Environments

TL;DR

The paper presents X-SYCON, a xylem-inspired, communication-free swarm controller in which demands and hazards create diffusing fields that guide carriers via a local utility $U=\phi_{DE}-\kappa\phi_{HZ}$. A beaconing mechanism accelerates completion without affecting time-to-first-response, and a hydraulic length scale $\ell \approx \sqrt{D/\lambda}$ together with an Ohm-law-like bound explain recruitment reach and capacity scaling. Through extensive simulations across hazard densities, carrier counts, and arrivals, the approach yields low miss rates, sublinear throughput growth, and a clear energy–reliability trade-off, demonstrating a robust, distributed, passive-computation paradigm for communication-denied autonomy. The work provides design guidance for tuning hazard penalties, sink strength, and recruitment dynamics, and frames a path toward human-in-the-loop deployment using beacons as lightweight, local cues. Overall, X-SYCON offers a principled, physics-inspired route to scalable swarm coordination when explicit communication is impractical or impossible.

Abstract

We present X-SYCON, a xylem-inspired multi-agent architecture in which coordination emerges from passive field dynamics rather than explicit planning or communication. Incidents (demands) and obstructions (hazards) continually write diffusing and decaying scalar fields, and agents greedily ascend a local utility $U=φ_{\mathrm{DE}}-κ\,φ_{\mathrm{HZ}}$ with light anti-congestion and separation. A beaconing rule triggered on first contact temporarily deepens the local demand sink, accelerating completion without reducing time-to-first-response. Across dynamic, partially blocked simulated environments, we observe low miss rates and stable throughput with interpretable, tunable trade-offs over carrier count, arrival rate, hazard density, and hazard sensitivity $κ$. We derive that a characteristic hydraulic length scale $\ell\approx\sqrt{D/λ}$ predicts recruitment range in a continuum approximation, and we provide a work-conservation (Ohm-law) bound consistent with sublinear capacity scaling with team size. Empirically: (i) soft hazard penalties yield fewer misses when obstacles already block motion; (ii) throughput saturates sublinearly with carriers while reliability improves sharply; (iii) stronger arrivals can reduce misses by sustaining sinks that recruit help; and (iv) phase-stability regions shrink with hazard density but are recovered by more carriers or higher arrivals. We refer to X-SYCON as an instance of Distributed Passive Computation and Control, and we evaluate it in simulations modeling communication-denied disaster response and other constrained sensing-action regimes.

Figures (11)

• Figure 1: Botany (xylem). Water flows upward from roots (higher $\Psi$) to leaves (low $\Psi$) driven by a potential drop $\Delta\Psi$, with Ohm-like relation $Q=K_h\,\Delta\Psi$. The vessel bundle (right) acts as a parallel conduit; conductance $K_h$ reflects lumen geometry and connectivity. Near distal branchings the water column is under high tension, increasing embolism risk (red zone).
• Figure 2: X--SYCON (passive fields) schematic. The red circle marks the demand site; the stacked red bars depict the demand field $\phi_{\mathrm{DE}}$ that weakens with distance (a sink). The dashed arc indicates the recruitment radius $\ell$, within which the sink effectively attracts carriers. A gray block depicts a hazard region (hazard field $\phi_{\mathrm{HZ}}$) that imposes a soft resistance. Blue triangles illustrate carriers moving up the net utility gradient, following the motion rule $\nabla U = \nabla(\phi_{\mathrm{DE}} - \kappa \phi_{\mathrm{HZ}})$ toward the demand while bending around hazards. This panel is a schematic—not a map—intended to show how demand attraction, hazard penalty, and recruitment radius jointly shape carrier flow.
• Figure 3: Qualitative snapshot of the simulated world. Blue triangles: carriers; red circles: demands; dark gray squares: blocked cells (hazard sources). Red glow indicates the demand field $\phi_{\mathrm{DE}}$.
• Figure 4: Beaconing timeline and effect on completion latency. Top row: Pre-contact ($t=t_0$) shows carriers (blue) ascending the demand field $\phi_{\mathrm{DE}}$ around obstacles (dark gray); the demand site is at the center. First contact ($t=t_1$) switches a local beacon (beacon-bonus in \ref{['eq:sde']}), deepening $\phi_{\mathrm{DE}}$ and bending nearby flows. Completion ($t=t_2$) turns the beacon off and the field relaxes. Bottom: the orange curve is task urgency $u_d$ rising linearly and clamping at $u_{\max}$; the blue curve is the local $\phi_{\mathrm{DE}}$ at the demand site, which jumps at $t_1$ due to beaconing and then decays as service proceeds. ... The dashed marker at $t_1$ denotes time-to-first-response (TTFR), which is unchanged by construction. In our implementation we report time in system (creation$\to$completion/miss); completion latency ($t_2{-}t_1$) is qualitatively reduced by beaconing when first contact occurs.
• Figure 5: Per-tick local controller: sense $\phi_{DE}, \phi_{HZ}$; compute $U$; separation/wander checks; random/greedy step; service; environment diffuses/decays. No communication.