Due to their agility, cost-effectiveness, and high maneuverability, Unmanned Aerial Vehicles (UAVs) have attracted considerable attention from researchers and investors alike. Path planning is one of the practical subsets of motion planning for UAVs. It prevents collisions and ensures complete coverage of an area. This study provides a structured review of applicable algorithms and coverage path planning solutions in Three-Dimensional (3D) space, presenting state-of-the-art technologies related to heuristic decomposition approaches for UAVs and the forefront challenges. Additionally, it introduces a comprehensive and novel classification of practical methods and representational techniques for path-planning algorithms. This depends on environmental characteristics and optimal parameters in the real world. The first category presents a classification of semi-accurate decomposition approaches as the most practical decomposition method, along with the data structure of these practices, categorized by phases. The second category illustrates path-planning processes based on symbolic techniques in 3D space. Additionally, it provides a critical analysis of crucial influential approaches based on their importance in path quality and researchers' attention, highlighting their limitations and research gaps. Furthermore, it will provide the most pertinent recommendations for future work for researchers. The studies demonstrate an apparent inclination among experimenters towards using the semi-accurate cellular decomposition approach to improve 3D path planning.

由于其敏捷性、成本效益和高机动性，无人机（UAV）吸引了研究人员和投资者的广泛关注。路径规划是无人机运动规划的实用子集之一。它可以防止碰撞并确保完全覆盖某个区域。本研究对三维 (3D) 空间中的适用算法和覆盖路径规划解决方案进行了结构化回顾，介绍了与无人机启发式分解方法相关的最先进技术和前沿挑战。此外，它还介绍了路径规划算法的实用方法和表示技术的全面且新颖的分类。这取决于现实世界中的环境特征和最佳参数。第一类提出了作为最实用的分解方法的半精确分解方法的分类，以及这些实践的数据结构，按阶段分类。第二类说明了基于 3D 空间中的符号技术的路径规划过程。此外，它还根据关键影响方法对路径质量的重要性和研究人员的关注程度，对它们进行了批判性分析，强调了它们的局限性和研究差距。此外，它还将为研究人员未来的工作提供最相关的建议。研究表明，实验者明显倾向于使用半精确细胞分解方法来改进 3D 路径规划。