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@article{ploscb24,
  author = {David L. Krongauz and Amir Ayali and Gal A. Kaminka},
  title = {Vision-Based Collective Motion: A Locust-Inspired Reductionist Model},
  year = {2024},
  volume = {20},
  number = {1}, 
  journal = {{PLOS} Computational Biology},
  pages = {e1011796},
  doi = {https://doi.org/10.1371/journal.pcbi.1011796},
  OPTurl = {https://www.biorxiv.org/content/10.1101/2023.01.17.524210v2},
  wwwnote = {The file here contains also the supplementary materials.}, 
  abstract = { Naturally occurring collective motion is a fascinating phenomenon in which swarming individuals aggregate
      and coordinate their motion. Many theoretical models of swarming assume idealized, perfect perceptual capabilities,
      and ignore the underlying perception processes, particularly for agents relying on visual perception. Specifically,
      biological vision in many swarming animals, such as locusts, utilizes monocular non-stereoscopic vision, which
      prevents perfect acquisition of distances and velocities. Moreover, swarming peers can visually occlude each 
      other, further introducing estimation errors. In this study, we explore necessary conditions for the emergence of 
      ordered collective motion under restricted conditions, using non-stereoscopic, monocular vision. We present a model 
      of vision-based of collective motion for locust-like agents: elongated shape, omni-directional visual sensor 
      parallel to the horizontal plane, and lacking stereoscopic depth perception. The model addresses (i) the 
      non-stereoscopic estimation of distance and velocity, (ii) the presence of occlusions in the visual field. 
      We consider and compare three strategies that an agent may use to interpret partially-occluded visual 
      information at the cost of the computational complexity required for the visual perception processes. 
      Computer-simulated experiments conducted in various geometrical environments (toroidal, corridor, and ring-shaped 
      arenas) demonstrate that the models can result in an ordered or near-ordered state. At the same time, they differ 
      in the rate at which order is achieved. Moreover, the results are sensitive to the elongation of the agents. 
      Experiments in geometrically constrained environments reveal differences between the models and elucidate possible 
      tradeoffs in using them to control swarming agents. These suggest avenues for further study in biology and robotics.
     },
}