Development of search strategy for single / multiagent UAVs in cooperative environment for Search and Rescue (S&R) / Area Surveillance

Abstract

The first decade of the current millennium witnessed limited use of Unmanned Aerial Vehicles (UAVs) technology mostly by the defense organizations. Rising accessibility of UAV technology to civilian especially commercial sectors; mostly, dangerous and high-paid jobs are being rapidly replaced by the UAVs. Off late, UAVs technology has outperformed human operators in search & rescue, fire protection and area surveillance due to technological advancement in the field of micro-electromechanical systems, collision-avoidance algorithms, precision and accuracies in sensor technologies. Many different sorts of SAR missions are dependent on the environment, the survivor's location (land or sea), and search techniques. In this thesis, different search strategies (Expanding-Square Exploration, Creeping-Line Exploration, Parallel-Track Exploration etc.) expounded by “International Aeronautical and Maritime Search and Rescue (IAMSAR) Manual” have been explored. I have proposed a hybrid solution based on Ant Colony Optimization (ACO) Algorithm for single / multi agents UAV system for SAR missions in a cooperative environment. The suggested Algorithm allows UAVs to examine the disaster region, gather data about the probable survivors, and transmit their positions to the ground station.