Task Allocation for a robotic swarm based on an Adaptive Response Threshold Model

Castello, Eduardo; Yamamoto, Tomoyuki; Nakamura, Yutaka; Ishiguro, Hiroshi

doi:10.1109/iccas.2013.6703905

Cited by 13 publications

(16 citation statements)

References 12 publications

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“…Preliminary simulation results obtained with ARTM were presented in Castello et al (2013) and Castello et al (2014). In addition to the more extensive results included in this paper, we demonstrate that ARTM can achieve adaptive division of labour in a small size robot swarm in an efficacious and robust way.…”

Section: Introductionmentioning

confidence: 62%

“…The benefits of using dynamical threshold models (such as ARTM), in terms of increase of the survivability and adaptability of swarm systems in changing environments, was previously shown only by computer simulations in Castello et al (2013) and Castello et al (2014). This paper aims at confirming those simulation results in a new set of experiments conducted using real robots.…”

Section: Real-robot Experimentsmentioning

confidence: 71%

“…The Adaptive Response Threshold Model (ARTM), first described in Castello et al (2013), is an extension of the classical Fixed Response Threshold Model (FRTM), in which the response threshold (θ) is calculated dynamically instead of remaining fixed. ARTM, like other response threshold methods, strongly relies on the concept of stimulus:…”

Section: Adaptive Response Threshold Model (Artm)mentioning

confidence: 99%

“…The major difference with previous fixed response threshold models is the Discrete Attractor Selection Model (DASM), represented by a rectangular box with dashed boundaries. DASM is the algorithm first introduced in Castello et al (2013), which dynamically calculates the θ threshold according to different environmental conditions. Once the process of updating θ is over, robots are able to calculate their response curve (defined in equation (2)) according to the new conditions and decide whether to begin the foraging process or wait, in standby mode, near the nest.…”

Section: Adaptive Response Threshold Model (Artm)mentioning

confidence: 99%

“…In foraging scenarios, θ has a great influence on the robot's behaviour, because it determines when the robot goes foraging (i.e., to expend energy looking for food within the environment), or rest (and conserve energy by staying in the nest) in case its work is not needed at that specific moment (Labella et al, 2006;. On one hand, prior work in the field has shown that a high value of θ can decrease interference among robots and therefore increase the system's performance (Zhang and Zeng, 2012), while on the other hand lower values of θ can beneficially affect the adaptation in response to abrupt changes in the availability of food in the environment (Castello et al, 2013). Tuning and optimising θ according to different environmental conditions in order to make a robot swarm adapt to dynamical situations is therefore not straightforward.…”

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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Section: Introductionmentioning

confidence: 62%

Section: Real-robot Experimentsmentioning

confidence: 71%

Section: Adaptive Response Threshold Model (Artm)mentioning

confidence: 99%

Section: Adaptive Response Threshold Model (Artm)mentioning

confidence: 99%