AI 中文总结
该文献综述聚焦无人机环境监测的自主路径规划，遵循PRISMA 2020框架搜索相关研究。介绍筛选流程与初步分析结果，指出研究集中在特定方面，多依赖模拟验证，近期对强化学习等兴趣上升，凸显研究活跃但分散，需结构化综合以明确技术与差距。
AI 中文摘要
无人机环境监测需要路径规划方法，在处理能量限制、操作约束和几何复杂性的同时最大化覆盖面积。本文报告了一项正在进行的关于面向覆盖的环境监测无人机自主路径规划的系统文献综述的方案和初步结果。该综述遵循PRISMA 2020框架，在Scopus和Web of Science中搜索2015年至2026年发表的研究。方案聚焦于路径规划等，强调算法家族等方面。目前已识别562条记录，去除161条重复记录，筛选出401条独特记录，247项研究进入全文评估。初步分析表明研究集中在特定方面，多数依赖基于模拟的验证，近期对强化学习等兴趣增加。这些早期发现表明研究活跃但分散，支持进行结构化综合以识别成熟技术和未解决差距的需求。
英文摘要
Environmental monitoring with unmanned aerial vehicles (UAVs) requires route planning methods that maximize covered area while handling energy limits, operational constraints, and geometric complexity. This paper reports the protocol and preliminary results of an ongoing systematic literature review (SLR) on autonomous UAV route planning for coverage-oriented environmental monitoring. The review follows the PRISMA 2020 framework and searches Scopus and Web of Science for studies published between 2015 and 2026. The protocol focuses on path planning, coverage path planning, and informative path planning, with emphasis on algorithmic families, coverage and energy metrics, obstacle handling, geometric environment representations, and environmental constraints. At the current stage, 562 records have been identified, 161 duplicates have been removed, and 401 unique records have been screened by title, abstract, and keywords. From these, 247 studies were retained for full-text eligibility assessment (235 eligible and 12 borderline records to be resolved during full-text review). A preliminary analysis of the retained studies suggests strong concentration on coverage-oriented formulations, multi-UAV coordination, and energy-aware optimization, while fewer studies explicitly address weather, uncertainty, or obstacle-rich environments. Most retained studies rely on simulation-based validation, highlighting a potential simulation-to-reality gap, and recent publications show increasing interest in reinforcement learning, hybrid optimization, and geometry-aware planning. These early findings indicate an active but fragmented research landscape and support the need for a structured synthesis to identify mature techniques and unresolved gaps for realistic environmental monitoring missions.
CommentsAccepted in CLEI 2026
Journal refCLEI 2026
