Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
A particle swarm model for swarm-based networked sensor systems
Proceedings of the 2002 ACM symposium on Applied computing
Autonomous Robots
Sensor replacement using mobile robots
Computer Communications
RTCSA '07 Proceedings of the 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications
Communication and Coordination in Wireless Sensor and Actor Networks
IEEE Transactions on Mobile Computing
The I-SWARM project: intelligent small world autonomous robots for micro-manipulation
SAB'04 Proceedings of the 2004 international conference on Swarm Robotics
Autonomic mobile sensor network with self-coordinated task allocation and execution
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Testbeds for ubiquitous robotics: A survey
Robotics and Autonomous Systems
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A swarm is a ''complex adaptive system'', which is decentralized and self-organized and whose individuals are simple, homogeneous and autonomous. Swarm intelligence is defined to describe its emergent behaviors. Both wireless sensor networks and mobile multi-robots demonstrate swarm features. This paper first discusses the challenges of combining wireless sensor networks and mobile multi-robots, and then proposes a layered dual-swarm framework with three communication channels that can inherit traditional swarm technology while building an efficient interaction channel for both swarms to cooperate. In order to improve the system controllability, a new type of numerical entity called ''virtual entity'' and related control strategies are introduced. Finally, proof-of-concept implementations are presented and illustrated with simulation scenarios and a physical testbed. The experimental results show that the WSN-MMR swarm system can emerge successfully and robustly from swarm intelligence.