面向飞机蒙皮检测任务的空-地异构机器人协同覆盖路径规划  

Cooperative coverage path planning for aerial-ground heterogeneous robots in aircraft skin inspection tasks

作　　者：朴敏楠 罗佳 李海丰[1] 周雨晗 Piao Minnan;Luo Jia;Li Haifeng;Zhou Yuhan(College of Computer Science&Technology,Civil Aviation University of China,Tianjin 300300,China)

机构地区：[1]中国民航大学计算机科学与技术学院,天津300300

出　　处：《计算机应用研究》2025年第4期1044-1049,共6页Application Research of Computers

基　　金：国家自然科学基金资助项目(62203450,62373365);航空科学基金资助项目(2022Z034067004);天津市自然科学基金多元投入青年项目(24JCQNJ00070);中央高校基本科研业务费资助项目(3122022QD09,3122023PT16,3122024PT08);天津市“一带一路”联合实验室项目(24PTLYHZ00230)。

摘　　要：飞机蒙皮检测对于保证飞机飞行安全至关重要。采用移动机器人自主检测方式能够大大提高检测效率以及降低安全风险。但由于飞机结构复杂,仅使用单一种类机器人作业难以实现飞机蒙皮全覆盖。所以,提出了一种空-地异构机器人协同覆盖路径规划方法(AG-CCPP)。首先引入无人机(UAV)、无人车(UGV)异构机器人系统,分析规划过程中必要的约束条件,包括作业空间约束、续航时间约束等,采用整数线性规划方法建立优化模型。其次,提出一种基于贪婪分配策略的多精英种群双染色体遗传算法进行任务分配与路径规划联合求解,增加分配染色体实现任务分配与路径规划联合求解,实现全局优化;基于续航约束进行贪婪分配,充分利用异构机器人优点;多层次精英种群设计,减少低效交叉种群数量,提升算法运行效率。最后,通过波音737-300的仿真实验进行对比分析,结果表明所提方法在机器人协同覆盖完成时间与程序执行时间方面均优于现有算法。The inspection of aircraft skin is critical to ensuring the safety of aircraft flights.Employing autonomous mobile robots for inspection can significantly enhance inspection efficiency and reduce safety risks.However,achieving full coverage of aircraft skin with a single type of robot is challenging due to the complexity of aircraft structures.Therefore,this paper proposed an aerial-ground cooperative coverage path planning(AG-CCPP)method for heterogeneous robots.Firstly,this paper introduced a heterogeneous robot system consisting of unmanned aerial vehicles(UAV)and unmanned ground vehicles(UGV),analyzed the necessary constraints in the planning process,including workspace constraints,endurance constraints,and so on,and established an optimization model using integer linear programming.Subsequently,this paper proposed a dual-chromosome genetic algorithm with a multi-elite population based on a greedy allocation strategy for jointly solving task allocation and path planning.By incorporating an allocation chromosome,it achieved joint solution for task allocation and path planning,enabling global optimization.It conducted greedy allocation based on endurance constraints,fully leveraging the advantages of heterogeneous robots.It adopted a multi-level elite population design to reduce the number of inefficient crossover populations and enhance the algorithm’s operational efficiency.Finally,simulation experiments and comparative analysis conducted on a Boeing 737-300 demonstrate that the proposed method outperforms existing algorithms in terms of both the completion time for cooperative coverage by robots and the program execution time.

关 键 词：飞机蒙皮检测 异构机器人协同 覆盖路径规划 遗传算法 任务分配 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]