2009
DOI: 10.1109/tac.2008.2010897
|View full text |Cite
|
Sign up to set email alerts
|

Flocking of Multi-Agents With a Virtual Leader

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

1
538
0

Year Published

2009
2009
2018
2018

Publication Types

Select...
4
4
2

Relationship

0
10

Authors

Journals

citations
Cited by 839 publications
(539 citation statements)
references
References 29 publications
1
538
0
Order By: Relevance
“…3). The remaining agents are controlled indirectly through interaction between neighbors [35] or through interaction with the leader [36], [37]. The problem of tracking the trajectory of the virtual leader or the desired collective behavior for agents with highly nonlinear dynamics (e.g., swarms rigid bodies with dynamics on SE(3) or agents with multi-DOF manipulators) can be addressed simultaneously with the problem of synchronization with neighboring agents [27].…”
Section: Synchronization With Leader Followingmentioning
confidence: 99%
“…3). The remaining agents are controlled indirectly through interaction between neighbors [35] or through interaction with the leader [36], [37]. The problem of tracking the trajectory of the virtual leader or the desired collective behavior for agents with highly nonlinear dynamics (e.g., swarms rigid bodies with dynamics on SE(3) or agents with multi-DOF manipulators) can be addressed simultaneously with the problem of synchronization with neighboring agents [27].…”
Section: Synchronization With Leader Followingmentioning
confidence: 99%
“…But general RRT or RRT* [14] can not guarantee global optimal, and the time required to achieve the asymptotic optimal still can not be guaranteed due the fact that it ignores the information of the configuration space. GART shares the same merits as artificial potential field(APF) [15] to obtain the local or global environmental information to decrease the random characteristic of RRT*, while it differs in: 1) GART introduces a bidirectional potential for random generated nodes (Fig. 2), that is, repulsion is achieved if the random node is in the obstacle region, and attraction when the random node outside the obstacle region.…”
Section: Path Plannermentioning
confidence: 99%
“…[9] discussed a flocking behavior of agents whereby only a certain number of agents were informed of the desired behavior. Flocking motion was achieved under this restriction, as in [8], which discussed a self-propelled particle system with limited communication between agents.…”
Section: Introductionmentioning
confidence: 99%