Abstract The use of Unmanned Aerial Vehicles (UAVs) has rapidly expanded across various applications in recent years. The autonomy requirements for UAVs include obstacle recognition, obstacle avoidance, and Safe Landing Zone (SLZ) detection. A fundamental challenge common to these vehicles is path planning, which determines where they should navigate and how they should find their routes. Although advancements in technologies utilizing computer vision have accelerated developmental progress, achieving a fully autonomous system necessitates solving multiple complex problems. During flight, UAVs must be capable of recognizing and assessing unexpected situations, generating a new path to continue operation, and ultimately completing the return journey and landing safely. This review provides a comprehensive perspective on the applicability of methods used to address autonomy-related challenges in UAVs and examines trends in recent studies. A total of 211 studies conducted between 2000 and 2025 were analyzed, revealing a three-phase growth pattern: 2000–2007 exhibited a low number of publications and a stagnant period; 2007–2014 showed a moderate increase in publications with rising awareness; and 2014–2025 experienced a significant surge in published articles. The results indicate that SLZ detection is the most frequently addressed issue, while visual-based control techniques are the most commonly employed methods. This study enables an evaluation of which methods are predominantly used in different application areas, the types of analyses conducted to validate proposed solutions, and which UAV types are more suitable for specific applications.
Küçükerdem et al. (Thu,) studied this question.