000202714 001__ 202714
000202714 005__ 20190317000036.0
000202714 0247_ $$2doi$$a10.1007/s11721-014-0101-7
000202714 022__ $$a1935-3812
000202714 02470 $$2ISI$$a000344790700004
000202714 037__ $$aARTICLE
000202714 245__ $$aDecentralized Self-Selection of Swarm Trajectories: From Dynamical Systems to Robotic Implementation
000202714 269__ $$a2014
000202714 260__ $$aNew York$$bSpringer Verlag$$c2014
000202714 300__ $$a23
000202714 336__ $$aJournal Articles
000202714 520__ $$aIn this paper, we present a distributed control strategy, enabling agents to converge onto and travel along a consensually selected curve among a class of closed planar curves. Individual agents identify the number of neighbors within a finite circular sensing range and obtain information from their neighbors through local communication. The information is then processed to update the control parameters and force the swarm to converge onto and circulate along the aforementioned planar curve. The proposed mathematical framework is based on stochastic differential equations driven by white Gaussian noise (diffusion processes). Using this framework, there is maximum probability that the swarm dynamics will be driven toward the consensual closed planar curve. In the simplest configuration where a circular consensual curve is obtained, we are able to derive an analytical expression that relates the radius of the circular formation to the agent’s interaction range. Such an intimate relation is also illustrated numerically for more general curves. The agent-based control strategy is then translated into a distributed Braitenberg-inspired one. The proposed robotic control strategy is then validated by numerical simulations and by implementation on an actual robotic swarm. It can be used in applications that involve large numbers of locally interacting agents, such as traffic control, deployment of communication networks in hostile environments, or environmental monitoring.
000202714 6531_ $$aspatio-temporal pattern
000202714 6531_ $$adistributed swarm control
000202714 6531_ $$aBrownian agents
000202714 6531_ $$amixed canonical-dissipative dynamics
000202714 6531_ $$amean-field approach
000202714 6531_ $$aBraitenberg control mechanism
000202714 6531_ $$arobotics experimental validation
000202714 700__ $$0246298$$aSartoretti, Guillaume Adrien$$g221993
000202714 700__ $$0240354$$aHongler, Max-Olivier$$g105416
000202714 700__ $$0247253$$aElias de Oliveira, Marcelo$$g236205
000202714 700__ $$0240589$$aMondada, Francesco$$g102717
000202714 773__ $$j8$$k4$$q329-351$$tSwarm Intelligence
000202714 8564_ $$s1594633$$uhttps://infoscience.epfl.ch/record/202714/files/art3A10.10072Fs11721-014-0101-7.pdf$$yPublisher's version$$zPublisher's version
000202714 909C0 $$0252100$$pLPM
000202714 909C0 $$0252016$$pLSRO
000202714 909CO $$ooai:infoscience.tind.io:202714$$pSTI$$particle$$qGLOBAL_SET
000202714 917Z8 $$x105416
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000202714 917Z8 $$x221993
000202714 917Z8 $$x221993
000202714 917Z8 $$x102717
000202714 937__ $$aEPFL-ARTICLE-202714
000202714 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000202714 980__ $$aARTICLE