Dynamic Data-Driven UAV Network for Plume Characterization
Abstract
Targeted, intelligent sensor networks have important applications in a tremendous range of situations, including toxic plume characterization, Intelligence, Surveillance and Reconnaissance (ISR), environmental monitoring, weather forecasting, and disaster management and response. Data driven operation of a mobile sensor network enables asset allocation to regions with highest impact on the mission success. We studied a dynamic data driven (DDD) approach to operation of a heterogeneous team of unmanned aerial vehicles (UAVs) or micro/miniature aerial vehicles (MAVs) for toxic plume characterization or similar ISR missions in complex domains. The proposed approach consists of two DDD loops. These are the DDD simulation loop and DDD sensor placement loop. The integrated feedback loops connect simulations and data analysis techniques with mobile sensor data collection where simulations and measurements become a symbiotic feedback control system where simulations inform measurement locations and the measured data augments simulations. We have developed several model reduction strategies to reduce the computational complexity of the simulation loop.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 23, 2016
- Accession Number
- AD1010620
Entities
People
- Kamran Mohseni
Organizations
- University of Florida