Division of Graduate Studies

One of the current autonomous systems research area in civilian and military applications is finding robust methods to track a ground area of interest (AOI) or airborne toxic substance. In case that the AOI is a spreading phenomenon (e.g., a wild forest fire), it is clear that a cooperative strategy for searching and monitoring with multiple UAV (Unmanned Aerial Vehicle) is beneficial compared to one vehicle. Multi-UAV Systems have the ability to share tasks and therefore accomplish the mission objectives with greater efficiency. The main purpose of this study is to suggest an estimation approach that is carried out using statistical methods of quantized observations for multi-UAVs' path re-planning to minimize the uncertainty of the monitored area. The UAVs share their observations and estimate the disaster area periphery with associated mapping for the level of confidence. The study presents a thorough analysis of the monitoring algorithm which operates as an uncertainty-oriented guidance system.