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David L. Krongauz, Amir Ayali, and Gal A.
Kaminka. Vision-Based Collective Motion: A Locust-Inspired Reductionist Model. PLOS Computational Biology,
20(1):e1011796, 2024.
The file here contains also the supplementary materials. Availalbe on bioarxiv as https://www.biorxiv.org/content/10.1101/2023.01.17.524210v2
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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.
@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 = {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. Availalbe on bioarxiv as \url{https://www.biorxiv.org/content/10.1101/2023.01.17.524210v2}},
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.
},
}
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