Comprehensive Review of Path Planning Techniques for Unmanned Aerial Vehicles (UAVs)

Abstract

Unmanned Aerial Vehicles (UAVs) have gained significant attention in recent years for their potential applications in surveillance, monitoring, search and rescue, and mapping. However, efficient and optimal path planning remains a key challenge for UAV navigation. This survey paper reviews various UAV path planning algorithms, encompassing Sampling-Based techniques, Potential Field methods, Bio-Inspired algorithms, and Artificial Intelligence-based approaches. We explore key factors affecting path planning, including environmental constraints, objectives, and uncertainties. We explore vital factors affecting path planning, including environmental constraints, objectives, and uncertainties. A comparative analysis of these techniques focuses on their strengths, weaknesses, and applicability in different UAV scenarios, including heuristic, mathematical, Bio-Inspired, and machine-learning methods. Critical parameters like path length, flight time, number of UAVs and targets, environmental dynamics, obstacle management, algorithmic approaches, real-time execution, and collision avoidance are examined. This survey aims to inform researchers, practitioners, and engineers in UAV path planning, offering insights into these techniques' challenges, limitations, and future research directions. By presenting a comprehensive overview of state-of-the-art methods and trends, our survey provides a clear understanding of the diverse path-planning strategies, their merits and demerits, and highlights key research challenges and unresolved issues in the field.