Network-Centric Supervisory Control of Multiple Unmanned Aerial Vehicles (UAV)

This proposal addresses the significant need for the supervisory monitoring and control of multiple Unmanned Aerial Vehicles (UAVs). A methodology and process to design an Application Web Service, Mission Capability Package decision agent, and toolset with advanced reasoning and processing technologies is detailed. The Phase 1 Work Plan employs the Rational Unified Process (RUP) and DoD Architectural Framework to ensure a focus on user needs and system goals. Examples of innovative proposed functionality include: agent input mechanisms, smart pull of data based on a UAV’s region of interest, label semantics, neural networks, dynamic ontology, and heuristic filters. Cognitive tasks and demands of the problem domain are analyzed with respect to workload, filtering of network-centric data, and presentation of information. An initial software framework is rationalized at the Fargo 119th ANG for user inputs to the decision agent design. Kutta details and captures the rationalized software design in a system & software design document. At the end of Phase 1, results of the research of are presented coupled with a proof of concept demonstration for a multiple UAV ingress scenario. Military and civilian market segments are defined for commercialization, including the DCGS, multiple UAV control stations and knowledge centric ERP markets. BENEFIT: In phase I Kutta designs an Application Web Service (AWS) and Mission Capability Package (MCP) decision agent that contains a functional toolset for supervisory monitoring and control of multiple Unmanned Aerial Vehicles (UAVs). This product: 1) automates the process of supervisory monitoring and control of heterogeneous UAVs, (2) coordinates multiple UAVs for Course of Actions (COA), (3) processes, correlates, and filters network-centric data from the Global Information Grid (GIG) in relation to a specific UAV, 4) provides input mechanisms to an agent based system interfacing to the GIG for dynamic updates of relevant changes to the battlefield, and (4) addresses the needs of semantic agent-based technologies in both civilian and military markets. Multiple DoD agencies benefit from the incorporation of a common intelligent decision agent solution for network-centric information collection, processing, and UAV C2. Operational UAV systems such as the Predator or Global Hawk also benefit from supervisory control through the optimization of the air asset for ingress or egress to a theater of interest. The utilization of the AWS, MCP decision agent and Human-Computer Interface (HCI) toolset by a UAV Operation Center connected to the Distributed Common Ground System (DCGS) enables the portrayal of the “right information at the right time” to the Warfighter in an integrated agent based solution. The Department of Homeland Security (DHS) benefits through the incorporation of supervisory multi-UAV control to assist in the persistent monitoring of America’s borders. In the civilian sector, a competitive advantage is obtained through the use of an multi-agent decision and semantic analysis system for market trends, creation of knowledge bases, and market forecasts for product development. Additionally, stovepipe legacy Enterprise Resource Planning (ERP) systems can benefit from adoption of semantic and decision agent reasoning technologies in the transition into a knowledge-centric environment.