UC Irvine

We develop a Drone-assisted Monitoring system, DOME, that gathers real-time data for situational awareness in emergent and evolving events. The driving use case for this work is a prescribed burn event (Rx fire), o ften u sed t o r educe h azardous f uels i n forests. DOME coordinates the use of multiple heterogeneous drone platforms to support the observation of emergent physical phenomena (e.g., fire spread) by leveraging domain expert input and physics-based modeling/simulation methods. We propose an executable rule-based system for drone task generation; here, a high-level mission specification utilizes physics-based models for fire spread prediction and automatically generates monitoring instructions with locations, periods, and frequency for drones. DOME integrates algorithms for task allocation (mapping tasks to drones) and flight path planning while considering trade-offs between sensing coverage and accuracy. In addition, DOME will guide in-flight drones to store and upload data under challenged communication settings (out of transmission range, external signal blocking by trees). We evaluate the performance of DOME in real events (with expert-developed burn plans). We test the applicability of the DOME system using simulated Rx burns at the Blodgett Forest Research Station and evaluate our proposed algorithms by comparing their performance with multiple baseline algorithms. Our experiments show the effectiveness of the composite mechanisms in DOME that outperforms other approaches with higher rewards (capturing data of higher quality) and coverage (reduction of missed tasks).