Digitization Can Stall Swarm Transport: Commensurability Locking in Quantized-Sensing Chains

ORAL

Abstract

We present a minimal model for autonomous robotic swarms in one- and higher-dimensional spaces, where identical, field-driven agents interact pairwise to self-organize spacing and independently follow local gradients sensed through quantized digital sensors. We show that the collective response of a multi-agent train amplifies sensitivity to weak gradients beyond what is achievable by a single agent. We discover a fractional transport phenomenon in which, under a uniform gradient, collective motion freezes abruptly whenever the ratio of intra-agent sensor separation to inter-agent spacing satisfies a number-theoretic commensurability condition. This commensurability locking persists even as the number of agents tends to infinity. We find that this condition is exactly solvable on the rationals -- a dense subset of real numbers -- providing analytic, testable predictions for when transport stalls. Our findings establish a surprising bridge between number theory and emergent transport in swarm robotics, informing design principles with implications for collective migration, analog computation, and even the exploration of number-theoretic structure via physical experimentation.

*This work was supported by the National Natural Science Foundation of China (Nos.T2350007, 12404239, 12174041) and the US National Science Foundation (PHY-1659940 and PHY1734030). S.L. was supported by the National Science Foundation, through the Center for the Physics of Biological Function (PHY-1734030) in Princeton University.

Publication: https://arxiv.org/abs/2510.17117

Presenters

  • Miro Rothman

    • Pitzer College

Authors

  • Trung Phan

    • Claremont Colleges (Scripps and Pitzer)
  • Miro Rothman

    • Pitzer College
  • Caroline N Cappetto

    • Scripps College
  • Penelope Messinger

    • Scripps College
  • Kaitlyn S Yasumura

    • Scripps College
  • Robert H Austin

    • Princeton University
  • Liyu Liu

    • Chongqing University
  • Gao Wang

    • Wenzhou University
  • Shengkai Li

    • Princeton University
  • Tuan K Do

    • University of California, Santa Barbara