Flocking for multi-agent dynamic systems: algorithms and theory

In this paper, we present a theoretical framework for design and analysis of distributed flocking algorithms. Two cases of flocking in free-space and presence of multiple obstacles are considered. We present three flocking algorithms: two for free-flocking and one for constrained flocking. A compreh...

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Bibliographic Details
Published in:IEEE transactions on automatic control Vol. 51; no. 3; pp. 401 - 420
Main Author: Olfati-Saber, R.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.03.2006
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithm design and analysis
Algorithms
Applied sciences
Biosensors
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Consensus theory
Construction costs
Control theory. Systems
Cost function
Deviation
Distributed control
dynamic graphs
Dynamical systems
Dynamics
Exact sciences and technology
Heuristic algorithms
Lyapunov method
Maneuvers
mobile sensor networks
networked autonomous vehicles
Obstacles
Peer to peer computing
Self-assembly
self-assembly of networks
self-organizing systems
Software
Studies
swarms
Unmanned aerial vehicles
Vehicle dynamics
Autonomous system
Peer to peer
swarms
Moving robot
Similarity
Lyapunov method
Potential function
Fragmentation
dynamic graphs
Self organization
User interface
Swarm intelligence
networked autonomous vehicles
mobile sensor networks
Collective process
Consensus theory
self-organizing systems
Computer animation
Knowledge extraction
Consensus
Lattice
Multiagent system
self-assembly of networks
Distributed algorithm
Distributed control
Wireless network
Sensor array
Mobile computing
Lyapunov function
ISSN:0018-9286, 1558-2523
Online Access:Get full text
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Summary:In this paper, we present a theoretical framework for design and analysis of distributed flocking algorithms. Two cases of flocking in free-space and presence of multiple obstacles are considered. We present three flocking algorithms: two for free-flocking and one for constrained flocking. A comprehensive analysis of the first two algorithms is provided. We demonstrate the first algorithm embodies all three rules of Reynolds. This is a formal approach to extraction of interaction rules that lead to the emergence of collective behavior. We show that the first algorithm generically leads to regular fragmentation, whereas the second and third algorithms both lead to flocking. A systematic method is provided for construction of cost functions (or collective potentials) for flocking. These collective potentials penalize deviation from a class of lattice-shape objects called /spl alpha/-lattices. We use a multi-species framework for construction of collective potentials that consist of flock-members, or /spl alpha/-agents, and virtual agents associated with /spl alpha/-agents called /spl beta/- and /spl gamma/-agents. We show that migration of flocks can be performed using a peer-to-peer network of agents, i.e., "flocks need no leaders." A "universal" definition of flocking for particle systems with similarities to Lyapunov stability is given. Several simulation results are provided that demonstrate performing 2-D and 3-D flocking, split/rejoin maneuver, and squeezing maneuver for hundreds of agents using the proposed algorithms.
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ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2005.864190