DDDAMS-based Crowd Control via UAVs and UGVs | Zendy

Zhenrui Wang | Zendy; Mingyang Li | Zendy; Amirreza M. Khaleghi | Zendy; Dong Xu | Zendy; Alfonso Lobos | Zendy; Christopher Vo | Zendy; Jyh-Ming Lien | Zendy; Jian Liu | Zendy; YoungJun Son | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

DDDAMS-based Crowd Control via UAVs and UGVs

Author(s) -

Zhenrui Wang,

Mingyang Li,

Amirreza M. Khaleghi,

Dong Xu,

Alfonso Lobos,

Christopher Vo,

Jyh-Ming Lien,

Jian Liu,

YoungJun Son

Publication year - 2013

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2013.05.372

Subject(s) - computer science , control (management) , crowd simulation , real time computing , sensor fusion , artificial intelligence , dynamic bayesian network , bayesian probability , data mining , distributed computing , computer security , crowds

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

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research