Self-organized flocking in mobile robot swarms

Turgut, Ali Emre; Çelikkanat, Hande; Gökçe, Fatih; Şahi̇n, Erol

doi:10.1007/s11721-008-0016-2

Cited by 248 publications

(194 citation statements)

References 29 publications

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“…In the last behavior, the robots rotate around the center of the swarm. Turgut et al (2008a) developed a virtual heading sensor which allows each robot to sense the heading direction of the other robots. With this information and knowing the distance of the neighbors (by means of an infrared sensor), the swarm was able to obtain coherent coordinated motion and obstacle avoidance in absence of a common goal direction (see Fig.…”

Section: Resultsmentioning

confidence: 99%

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Swarm robotics: a review from the swarm engineering perspective

et al. 2013

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Swarm robotics is an approach to collective robotics that takes inspiration from the self-organized behaviors of social animals. Through simple rules and local interactions, swarm robotics aims at designing robust, scalable, and flexible collective behaviors for the coordination of large numbers of robots. In this paper, we analyze the literature from the point of view of swarm engineering: we focus mainly on ideas and concepts that contribute to the advancement of swarm robotics as an engineering field and that could be relevant to tackle real-world applications. Swarm engineering is an emerging discipline that aims at defining systematic and well founded procedures for modeling, designing, realizing, verifying, validating, operating, and maintaining a swarm robotics system. We propose two taxonomies: in the first taxonomy, we classify works that deal with design and analysis methods; in the second taxonomy, we classify works according to the collective behavior studied. We conclude with a discussion of the current limits of swarm robotics as an engineering discipline and with suggestions for future research directions.

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Section: Resultsmentioning

confidence: 99%

“…Çelikkanat andŞahin (2010), extending the work of Turgut et al (2008a), showed that it is possible to insert some "informed" robots in the swarm in order to direct the movement of other "non-informed" robots. The informed robots are the only ones in the group with knowledge of the goal direction.…”

Section: Resultsmentioning

confidence: 99%

Swarm robotics: a review from the swarm engineering perspective

et al. 2013

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“…From a more biological perspective, the interesting questions are the evolutionary advantages and the biological and ecological function of collective behaviour in various species [1 -5], whereas physicist focus rather on universal laws and phase-transition behaviour by studying minimal models of collective motion [6 -13]. The design, control and stability of collective dynamics in multi-agent systems is also a major research topic in engineering [14][15][16][17], and the general properties of related mathematical models are under active investigation in mathematics [18,19].…”

Section: Introductionmentioning

confidence: 99%

Swarming and pattern formation due to selective attraction and repulsion

Romanczuk

Schimansky‐Geier

2012

Interface Focus.

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We discuss the collective dynamics of self-propelled particles with selective attraction and repulsion interactions. Each particle, or individual, may respond differently to its neighbours depending on the sign of their relative velocity. Thus, it is able to distinguish approaching (coming closer) and retreating (moving away) individuals. This differentiation of the social response is motivated by the response to looming visual stimuli and may be seen as a generalization of the previously proposed escape and pursuit interactions motivated by empirical evidence for cannibalism as a driving force of collective migration in locusts and Mormon crickets. The model can account for different types of behaviour such as pure attraction, pure repulsion or escape and pursuit, depending on the values (signs) of the different response strengths. It provides, in the light of recent experimental results, an interesting alternative to previously proposed models of collective motion with an explicit velocity -alignment interaction. We discuss the derivation of a coarse-grained description of the system dynamics, which allows us to derive analytically the necessary condition for emergence of collective motion. Furthermore, we analyse systematically the onset of collective motion and clustering in numerical simulations of the model for varying interaction strengths. We show that collective motion arises only in a subregion of the parameter space, which is consistent with the analytical prediction and corresponds to an effective escape and/or pursuit response.

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“…Such cooperative control strategies have received particular attention from different scientific communities with different aims and goals: first by the computer graphics community (Reynolds, 1987), then followed by the physics community (Vicsek and Zafeiris, 2012). The control community subsequently established a formal framework (Jadbabaie et al, 2003a;Olfati-Saber et al, 2007;Ren and Beard, 2008), which has been put into practice and expanded by multi-robot systems and swarm robotics community (Turgut et al, 2008;Brambilla et al, 2013). Recently, Virágh et al (2014) have established the connection between dynamical update rules of locally interacting agents and cooperative control strategies for flocks of autonomous flying robots.…”

Section: Cooperative Control Strategymentioning

confidence: 99%

Swarm-Enabling Technology for Multi-Robot Systems

et al. 2017

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Swarm robotics has experienced a rapid expansion in recent years, primarily fueled by specialized multi-robot systems developed to achieve dedicated collective actions. These specialized platforms are, in general, designed with swarming considerations at the front and center. Key hardware and software elements required for swarming are often deeply embedded and integrated with the particular system. However, given the noticeable increase in the number of low-cost mobile robots readily available, practitioners and hobbyists may start considering to assemble full-fledged swarms by minimally retrofitting such mobile platforms with a swarm-enabling technology. Here, we report one possible embodiment of such a technology-an integrated combination of hardware and software-designed to enable the assembly and the study of swarming in a range of general-purpose robotic systems. This is achieved by combining a modular and transferable software toolbox with a hardware suite composed of a collection of low-cost and off-theshelf components. The developed technology can be ported to a relatively vast range of robotic platforms-such as land and surface vehicles-with minimal changes and high levels of scalability. This swarm-enabling technology has successfully been implemented on two distinct distributed multi-robot systems, a swarm of mobile marine buoys and a team of commercial terrestrial robots. We have tested the effectiveness of both of these distributed robotic systems in performing collective exploration and search scenarios, as well as other classical cooperative behaviors. Experimental results on different swarm behaviors are reported for the two platforms in uncontrolled environments and without any supporting infrastructure. The design of the associated software library allows for a seamless switch to other cooperative behaviors-e.g., leader-follower heading consensus and collision avoidance, and also offers the possibility to simulate newly designed collective behaviors prior to their implementation onto the platforms. This feature greatly facilitates behavior-based design, i.e., the design of new swarming behaviors, with the possibility to simulate them prior to physically test them.

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Self-organized flocking in mobile robot swarms

Cited by 248 publications

References 29 publications

Swarm robotics: a review from the swarm engineering perspective

Swarm robotics: a review from the swarm engineering perspective

Swarming and pattern formation due to selective attraction and repulsion

Swarm-Enabling Technology for Multi-Robot Systems

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