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Title: A Bio-Inspired Model for Bee Simulations
As eusocial creatures, bees display unique macro col- lective behavior and local body dynamics that hold potential ap- plications in various fields, such as computer animation, robotics, and social behavior. Unlike birds and fish, bees fly in a low-aligned zigzag pattern. Additionally, bees rely on visual signals for foraging and predator avoidance, exhibiting distinctive local body oscilla- tions, such as body lifting, thrusting, and swaying. These inherent features pose significant challenges to realistic bee simulations in practical animation applications. In this article, we present a bio-inspired model for bee simulations capable of replicating both macro collective behavior and local body dynamics of bees. Our approach utilizes a visually-driven system to simulate a bee’s local body dynamics, incorporating obstacle perception and body rolling control for effective collision avoidance. Moreover, we develop an oscillation rule that captures the dynamics of the bee’s local bodies, drawing on insights from biological research. Our model extends beyond simulating individual bees’ dynamics; it can also represent bee swarms by integrating a fluid-based field with the bees’ in- nate noise and zigzag motions. To fine-tune our model, we utilize pre-collected honeybee flight data. Through extensive simulations and comparative experiments, we demonstrate that our model can efficiently generate realistic low-aligned and inherently noisy bee swarms.  more » « less
Award ID(s):
2005430
PAR ID:
10637057
Author(s) / Creator(s):
; ; ; ; ; ;
Publisher / Repository:
IEEE
Date Published:
Journal Name:
IEEE Transactions on Visualization and Computer Graphics
Volume:
31
Issue:
4
ISSN:
1077-2626
Page Range / eLocation ID:
2073 to 2085
Subject(s) / Keyword(s):
Bio-inspired, bee simulations, inherent noise, zigzag patterns, insect swarms, crowd simulation, data-driven calibration.
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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