DDDAMS-based Urban Surveillance and Crowd Control via UAVs and UGVs

Investigating algorithmic approaches to create scalable, robust, multi-scale, and effective urban surveillance and crowd control strategies using UAVs and UGVs.

Air Force Research Laboratory, Arlington, Virginia

A comprehensive planning and control framework was designed and developed based on dynamic-data-driven, adaptive multi-scale simulation (DDDAMS) (see illustration) where dynamic data is incorporated into simulation, simulation steers the measurement process for data update and system control, and an appropriate level of simulation fidelity is selected based on the time constraints for evaluating alternative control policies using simulation.

The illustration shows an overview of the proposed DDDAMS-based planning and control framework for surveillance and crowd control via UAVs and UGVs that was developed and refined in this project. The major components of the framework include: 1) real system (UAVs, UGVs, human crowd, and environment); 2) integrated planner; 3) integrated controller; and 4) decision module for DDDAMS. The proposed frame work was aimed to enhance the surveillance and crowd control capability of UAVs and UGVs in terms of their performance on crowd detection, tracking, and motion planning. In particular, the crowd coverage percentage was considered as the measure of effectiveness (MOE) in this work. An overview of different components is provided in the following paragraphs.