Unmanned aerial vehicles (UAVs) have gained widespread applications in areas such as logistics, surveillance, precision agriculture, disaster response, and smart city infrastructure. Efficient and autonomous path planning is a critical challenge in UAV operations, requiring optimized trajectory generation to enhance navigation, obstacle avoidance, energy efficiency, and multi-agent coordination. To address these challenges, researchers have increasingly adopted metaheuristic algorithms, particularly ant colony optimization (ACO), along with hybrid AI-driven approaches such as genetic algorithms (GA), particle swarm optimization (PSO), and reinforcement learning. This study conducts a bibliometric analysis using Scopus data and VOSviewer visualization techniques to explore global research contributions in UAV path planning and optimization. The analysis identifies key research clusters, influential authors, institutional collaborations, and regional research distributions, providing insights into emerging trends and methodologies in UAV navigation. The findings reveal a strong emphasis on bio-inspired algorithms, AI integration, and multi-agent UAV coordination, with China leading in publication contributions. Additionally, hybrid optimization models are gaining traction for real-time decision-making and adaptive route planning in dynamic environments. The results of this study offer a comprehensive overview of the research landscape, highlighting current advancements, challenges, and opportunities in UAV path planning. Future research is expected to focus on fully autonomous UAV systems, swarm intelligence, secure UAV communication networks, and energy-efficient navigation strategies. This bibliometric study serves as a valuable resource for researchers and industry stakeholders aiming to enhance UAV autonomy through cutting-edge optimization and AI-driven solutions.
