Response threshold model of aggregation in a swarm: A theoretical and simulative comparison

Liu, Bailong; Zhang, Rubo; Shi, Changting

doi:10.1109/cec.2008.4630934

Cited by 1 publication

(2 citation statements)

References 17 publications

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“…Numerous studies (Yang et al, 2009;Yongming et al, 2010;Bailong et al, 2008) have applied simple algorithms based on Fixed Response Threshold Models (FRTM) (Bonabeau et al, 1998) to robotic foraging swarms. These models have shown similar behaviours to the mechanisms that regulate labour in insect colonies such as worker specialisation (Labella et al, 2006) or hierarchical differentiation (Theraulaz et al, 1990).…”

Section: Introductionmentioning

confidence: 99%

“…Despite its simplicity, FRTM is still very interesting because of the ability to adapt to changing situations (Yongming et al, 2010;Bailong et al, 2008) and different scenarios (Bonabeau et al, 1998;Yang et al, 2009). The fixed response threshold (θ) is an internal FRTM variable that determines the tendency of a robot to respond to a given stimulus in the environment and perform an associated task.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive foraging for simulated and real robotic swarms: the dynamical response threshold approach

et al. 2016

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Developing self-organized swarm systems capable of adapting to environmental changes as well as to dynamic situations is a complex challenge. An efficient labour division model, with the ability to regulate the distribution of work among swarm robots, is an important element of this kind of system. This paper extends the popular Response Threshold Model (RTM) and proposes a new Adaptive Response Threshold Model (ARTM). Experiments were carried out in simulation and in real-robot scenarios with the aim of studying the performance of this new adaptive model. Results presented in this paper verify that the extended approach improves on the adaptability of previous systems. For example, by reducing collision duration among robots in foraging missions, our approach helps small swarms of robots to adapt more efficiently to changing environments, thus increasing their self-sustainability (survival rate). Finally, we propose a minimal version of ARTM, which is derived from the conclusions obtained through real-robot and simulation results.

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