A survey of 3D Space Path-Planning Methods and Algorithms

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2024-06-20 DOI:10.1145/3673896
Hakimeh mazaheri, salman goli, ali nourollah
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Abstract

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.

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三维空间路径规划方法和算法概览
无人驾驶飞行器(UAV)因其灵活性、成本效益和高机动性,吸引了研究人员和投资者的极大关注。路径规划是无人飞行器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一个区域。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,介绍了与无人飞行器启发式分解方法相关的最新技术和前沿挑战。此外,它还对路径规划算法的实用方法和表示技术进行了全面而新颖的分类。这取决于现实世界中的环境特征和最佳参数。第一类介绍了半精确分解方法的分类,这是最实用的分解方法,同时还介绍了这些做法的数据结构,并按阶段进行了分类。第二类介绍基于三维空间符号技术的路径规划过程。此外,它还根据路径质量的重要性和研究人员的关注度,对具有重要影响的方法进行了批判性分析,强调了这些方法的局限性和研究空白。此外,它还将为研究人员今后的工作提供最中肯的建议。研究表明,实验人员明显倾向于使用半精确蜂窝分解方法来改进三维路径规划。
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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