Abstract
Collective behaviors of biological swarms have attracted significant interest in recent years, but much attention and correlative effort has been focused on constant speed models in which all agents are assumed to move with the same constant speed. One limitation of the constant speed models without attraction functions is that it is quite difficult or even practically impossible for the swarm to form large biological cluster(s) if the speed is relatively fast or the sensory radius is small. In this chapter, we propose an adaptive velocity model with more reasonable assumptions in which every agent not only adjusts its moving direction but also adjusts its speed based on the degree of direction consensus among its local neighbors. It is also a nearest neighbor rule but much easier for swarm agents to form a giant cluster or only one cluster in the adaptive velocity model if each agent moves with a speed that is proportional to its local direction consensus, even though the steady-state speed is still fast. The adaptive velocity strategy also shows that attraction actions or dominant leaders of swarms are not necessities for swarm cohesion. Therefore, the adaptive velocity model provides a powerful mechanism for coordinated motion in biological and technological multi-agent systems.
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Li, W., Wang, X. (2008). Coordination of Multi-agent Systems Using Adaptive Velocity Strategy. In: Wang, FY., Liu, D. (eds) Networked Control Systems. Springer, London. https://doi.org/10.1007/978-1-84800-215-9_10
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DOI: https://doi.org/10.1007/978-1-84800-215-9_10
Publisher Name: Springer, London
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