Distributed cooperative control and collision avoidance for multiple kinematic agents

Dimarogonas, DV; Kyriakopoulos, KJ

dc.contributor.author	Dimarogonas, DV	en
dc.contributor.author	Kyriakopoulos, KJ	en
dc.date.accessioned	2014-03-01T02:44:00Z
dc.date.available	2014-03-01T02:44:00Z
dc.date.issued	2006	en
dc.identifier.issn	01912216	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31615
dc.subject	Algebraic Graph Theory	en
dc.subject	Collision Avoidance	en
dc.subject	Common Value	en
dc.subject	Control Strategy	en
dc.subject	Cooperative Control	en
dc.subject	Distributed Control	en
dc.subject	State Space	en
dc.subject	Steady State	en
dc.subject	Multi Agent System	en
dc.subject.other	Convergence of numerical methods	en
dc.subject.other	Multi agent systems	en
dc.subject.other	Vectors	en
dc.subject.other	Velocity measurement	en
dc.subject.other	Distributed cooperative control	en
dc.subject.other	Multiple kinematic agents	en
dc.subject.other	Velocity vectors	en
dc.subject.other	Distributed computer systems	en
dc.title	Distributed cooperative control and collision avoidance for multiple kinematic agents	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/CDC.2006.376884	en
heal.identifier.secondary	http://dx.doi.org/10.1109/CDC.2006.376884	en
heal.identifier.secondary	4177320	en
heal.publicationDate	2006	en
heal.abstract	This paper contains two main contributions: (i) a provably correct distributed control strategy for collision avoidance and convergence of multiple holonomic agents to a desired feasible formation configuration and (ii) a connection between formation infeasibility and flocking behavior in holonomic kinematic multi-agent systems. In particular, it is shown that when inter-agent formation objectives cannot occur simultaneously in the state-space then, under certain assumptions, the agents velocity vectors and orientations converge to a common value at steady state, under the same control strategy that would lead to a feasible formation. Convergence guarantees are provided in both cases using tools from algebraic graph theory and Lyapunov analysis. © 2006 IEEE.	en
heal.journalName	Proceedings of the IEEE Conference on Decision and Control	en
dc.identifier.doi	10.1109/CDC.2006.376884	en
dc.identifier.spage	721	en
dc.identifier.epage	726	en