Intelligent path planning algorithms for UAVs: Classification, complexity analysis, hybrid ablation insights, and future directions

As unmanned aerial vehicle (UAV) technology has evolved, these systems are being increasingly utilized across diverse industries. However, controlling UAVs faces significant problems owing to several environmental circumstances and obstacles, making path planning a critical initial step for UAV operation. This paper offers an overview of UAV path planning research founded on intelligent algorithms, which are divided into three categories: computational intelligence (CI), machine learning (ML), and hybrid methods. Each category has been analyzed in depth to show its strengths, limits, and where it may be applied to UAV-related problems. The methodology includes a comparative analysis based on multiple performance metrics such as path length, flight time, collision avoidance, complexity, and environmental adaptability. Furthermore, the research covers the latest publications that deal with solving essential challenges of UAV path planning by using new hybrid algorithms and enhanced optimization methods. The results indicate that although each strategy offers specific strengths suited to particular scenarios, hybrid strategies are more likely to deliver greater flexibility and robustness, particularly in uncertain, and dynamic environments. These findings are significant for guiding future research in adaptive path planning and for supporting practical UAV applications such as autonomous delivery, aerial surveillance, disaster response, and environmental monitoring.