Autonomous UAV Path Planning for Search and Rescue Missions in Post Natural Disaster Assessment Based on Novel G-BFOA Algorithm

Nowadays, unmanned aerial vehicles (UAV), as the mobile multi-sensor platforms, are a necessary instrument for immediately spatial information gathering after occurrence of natural hazards. UAVs have a substantial role in improvement of search and rescue missions during natural hazards with fast and systematic monitoring of dangerous areas those are not accessible to relief workers. On the other hand, UAV mission planning is a fundamental stage to achieve the autonomous navigation. In this domain, computing optimal trajectories considering mission requirements and environmental conditions is a required step in path planning of autonomous UAVs. In this paper, after analyzing the role of UAV in upgrading the search and rescue missions, an improved bacterial foraging algorithm is proposed and implemented for UAV path planning. For performance evaluations, the algorithm is compared with other robust methods. Finally, results of UAV search and rescue mission simulations and research outcomes are presented.