基于遗传算法的输电线路无人机巡检任务智能分配协同控制系统设计

Design of cooperative control system for intelligent assignment of transmission line UAV inspection task based on genetic algorithm

输电线路无人机(Unmanned Aerial Vehicle,UAV)巡检质量对于线路安全和工作效率等都具有重要的影响,巡检质量的提升和保障尤为重要。基于此,研究结合模拟退火算法(Simulated Annealin, SA)和遗传算法(Genetic Algorithm)构建了GAISA算法,并基于GAISA设计了一个输电线路无人机巡检任务智能分配协同控制系统。实验结果表明,通过求解任务分配算例时,在迭代次数接近20后,相比遗传和模拟退火分别低于400和250左右。从实验结果中可以看出,GAISA从不同算法优化结果和求解不同规模的MTSP问题的结果均体现出一定的优越性,体现出其一定的有效性与优势性。研究提出的算法引入了协同控制机制,并针对输电线路特点进行优化,由此该方法有望解决结合人工智能和数据分析技术,实现线路巡检的高效、准确和安全。

The quality of unmanned aerial vehicle inspection on transmission lines has an important impact on line safety and work efficiency,and the improvement and guarantee of inspection quality are particularly important.Based on this,the study combined Simulated Annealing(SA)and Genetic Algorithm to construct the GAISA algorithm,and designed an intelligent allocation and collaborative control system for unmanned aerial vehicle inspection tasks on transmission lines based on GAISA.The experimental results show that when solving the task allocation example,after the number of iterations approaches 20,compared to genetic algorithm and simulated annealing,it is around 400 and 250,respectively.From the experimental results,it can be seen that GAISA exhibits certain advantages in optimizing results from different algorithms and solving MTSP problems of different scales,demonstrating its effectiveness and advantages.The algorithm proposed in the study introduces a collaborative control mechanism and is optimized based on the characteristics of transmission lines.Therefore,this method is expected to solve the problem of combining artificial intelligence and data analysis technology to achieve efficient,accurate,and safe line inspection.