Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Constrained animation of flocks
Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation
Robot and Sensor Networks for First Responders
IEEE Pervasive Computing
Coordination without communication: the case of the flocking problem
Discrete Applied Mathematics - Fun with algorithms 2 (FUN 2001)
Cooperative Observation of Multiple Moving Targets: an algorithm and its formalization
International Journal of Robotics Research
A Decentralized and Adaptive Flocking Algorithm for Autonomous Mobile Robots
GPC-WORKSHOPS '08 Proceedings of the 2008 The 3rd International Conference on Grid and Pervasive Computing - Workshops
ROBIO '09 Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics
Motion planning for multitarget surveillance with mobile sensor agents
IEEE Transactions on Robotics
IEEE Communications Magazine
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This paper presents novel approaches to (1) the problem of flocking control of a mobile sensor network to track and observe a moving target and (2) the problem of sensor splitting/merging to track and observe multiple targets in a dynamic fashion. First, to deal with complex environments when the mobile sensor network has to pass through a narrow space among obstacles, we propose an adaptive flocking control algorithm in which each sensor can cooperatively learn the network's parameters to decide the network size in a decentralized fashion so that the connectivity, tracking performance and formation can be improved. Second, for multiple dynamic target tracking, a seed growing graph partition (SGGP) algorithm is proposed to solve the splitting/merging problem. To validate the adaptive flocking control we tested it and compared it with the regular flocking control algorithm. For multiple dynamic target tracking, to demonstrate the benefit of the SGGP algorithm in terms of total energy and time consumption when sensors split, we compared it with the random selection (RS) algorithm. Several experimental tests validate our theoretical results.