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• Advanced UAV task Planning : The study focuses on enhancing UAV task planning systems, particularly in 3D trajectory planning, to optimize flight paths in complex, dynamic environments. • Bio-Inspired AI Pathfinding : Utilization of bio-inspired artificial intelligence technologies to determine optimal, feasible paths for UAVs, surpassing conventional vehicle path planning methods. • Mixed integer programming Model : Development of a mixed integer programming mathematical model to optimize UAV routes based on flight task parameters such as origin, destination, and departure time. • Emergency response Efficiency : AI techniques are employed to improve safety and efficiency in emergency response scenarios, reducing risks and costs through intelligent analysis and real-time data acquisition. • Validation of model Performance : The efficacy of the proposed model is validated with performance metrics (accuracy, recall, precision, F-measure, Fb-measure) all exceeding 0.88 and 0.89, demonstrating high reliability and performance. • Pragmatic UAV Features : The UAV model reduces search time for missing persons, increases tracking accuracy, minimizes human effort, and is easy to control via remote sensing. Its small size makes it suitable for difficult environments. • Cost-Effectiveness and data Collection : The UAV model is cost-effective compared to traditional aircraft, facilitating affordable real-time data collection and transmission in various formats (brackets, images, videos) to smartphones or computers. Unmanned aerial vehicles (UAVs) have become indispensable in replacing human pilots for high-risk, high-intensity operations, including search-and-rescue missions, infrastructure inspection, and environmental monitoring. As a key enabler of these operations, UAV task planning systems require advanced trajectory optimization to operate effectively in dynamic, complex environments. This paper presents an innovative model integrating fully fuzzy linear fractional transportation programming (FTMP) with Internet of Things (IoT) technologies to enhance UAV performance. Unlike conventional path-planning methods, the proposed model leverages bio-inspired artificial intelligence (AI) algorithms and mixed-integer programming to solve multi-objective optimization problems under uncertainty. A critical feature of this platform is its ability to continuously adjust flight altitude based on real-time data, accounting for varying terrain morphology, fuel constraints, flight restrictions, and threats, thus ensuring optimal 3D trajectory planning. This makes the platform particularly effective in emergency scenarios such as natural disasters, explosions, or fires, where it supports rapid and accurate search-and-rescue efforts. The UAV system not only minimizes human involvement but also reduces the response time by transmitting real-time video, audio, and GPS data to responders over long distances. The proposed solution offers several advantages over traditional aerial platforms, including: ( a) Precision tracking and path optimization with a demonstrated accuracy exceeding 88 % across recall, precision, and F-measure metrics. (b) Cost-effectiveness : The UAV is compact, lightweight, and affordable, significantly reducing operational expenses compared to helicopters. ( c) Adaptability : Remote sensing allows for seamless control in rugged and obstructed environments. (d) Scalability : IoT integration supports multi-drone coordination for continuous real-time data collection and transmission to smartphones or command centers. This pragmatic UAV model offers transformative potential for emergency response, environmental monitoring, and smart infrastructure management, validating its reliability and efficiency through comprehensive performance metrics.
Al-Janabi et al. (Thu,) studied this question.