计算机科学
任务(项目管理)
互补性(分子生物学)
运动规划
工作量
分布式计算
稳健性(进化)
选择(遗传算法)
优先次序
路径(计算)
自适应系统
任务分析
工程类
杠杆(统计)
适应性
解算器
工作(物理)
意外事件
夹持器
支柱
控制工程
计算
作者
Yonghao Zhao,Jianjun Ni,Jie Liu,Yang Gu,Simon X. Yang
标识
DOI:10.1109/tase.2025.3637392
摘要
Autonomous exploration in unknown environments is a critical capability for multi-UAV systems. However, existing methods often suffer from unbalanced task allocation, low exploration efficiency, and unstable paths, especially in large-scale and complex scenarios. To address these challenges, this paper presents an adaptive coordination exploration approach for multi-UAV systems. In the proposed approach, dynamic region allocation, entropy-guided local planning, and direction-consistent frontier selection are integrated to achieve efficient and collaborative exploration. The system first partitions the environment adaptively based on workload and regional complexity. It then prioritizes high-information-value areas for exploration. Directional constraints are further applied to improve path continuity and reduce turning redundancy. Extensive experiments in indoor maze and pillar environments, and outdoor forest and urban environments demonstrate that the proposed approach outperforms state-of-the-art baselines. Furthermore, ablation studies validate the necessity and complementarity of each module. This work provides a practical and efficient solution for multi-UAV exploration in structured and unstructured environments.
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