DDDAMS-based crowd control via UAVs and UGVs

Zhenrui Wang, Mingyang Li, Amirreza M. Khaleghi, Dong Xu, Alfonso Lobos, Christopher Vo, Jyh Ming Lien, Jian Liu, Young Jun Son

Research output: Contribution to journalConference articlepeer-review

20 Scopus citations


Unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) collaboratively play central roles in intelligence gathering and control in urban/border surveillance and crowd control. In this paper, we first propose a comprehensive planning and control framework based on dynamic-data-driven, adaptive multi-scale simulation (DDDAMS). We then discuss proposed algorithms enabling DDDAMS capability based on several methods such as 1) Bayesian-based information aggregation/disaggregation, 2) dynamic information updating based on observation/simulation, 3) temporal and spatial data fusion for enhanced performance, 4) multi-resolution strategy in temporal tracking frequency, and 5) cached intelligent observers. Finally, preliminary results based on the proposed framework, algorithms, and testbeds are discussed.

Original languageEnglish (US)
Pages (from-to)2028-2035
Number of pages8
JournalProcedia Computer Science
StatePublished - 2013
Event13th Annual International Conference on Computational Science, ICCS 2013 - Barcelona, Spain
Duration: Jun 5 2013Jun 7 2013


  • Agent-based simulation
  • Crowd control
  • Multi-scale
  • UAV
  • UGV

ASJC Scopus subject areas

  • General Computer Science


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