摘要： 航路规划是提高无人机生存能力的有效途径,可使其安全、快速到达目的地。为在云计算环境中分布式并 行地求解航路规划问题,应用云计算技术提出基于MapReduce 和多目标蚁群算法的航路规划算法(RPMA)。设计 多目标蚁群算法,并采用多种优化策略对传统算法进行改进。RPMA 能预先规划出多条航迹,可根据不同的飞行 任务选择不同的航路,并在飞行过程中根据不同需要临时确定合适的飞行航路。仿真实验结果表明,RPMA 求解 航路问题是可行、有效的,具有较好的收敛性和扩展性,以及对大规模数据的处理能力。

关键词: 云计算, MapReduce 分布式编程, 蚁群优化, 航路规划, 无人机, Hadoop 分布式文件系统

Abstract: Route planning is an effective way to improve the ability to survive of Unmanned Aerial Vehicle(UAV),for which can make the UAV reach the destination safely and fast. In this paper,the route planning algorithm based on MapReduce and multi-objective Ant Colony Optimization ( ACO) is put forward, which named RPMA. The multiobjective ACO algorithm is designed in the RPMA and different varieties of optimization strategies are used to improve the RPMA. The RPMA uses cloud computing technology and makes it solve the route planning problems in distributed cloud computing environment and parallel technology. A number of paths are planned in advance. The RPMA is able to make the UAV choose different routes according to different missions or choose the appropriate route according to different temporary needs. The preferable result is got in the simulation experiment,which indicates that the RPMA is an efficient way to solve the route planning problems and has the qualities of convergence and scalability. In addition,the RPMA has the handling abilities of large-scale data.

Key words: cloud computing, MapReduce distributed programming, Ant Colony Optimization(ACO), route planning, Unmanned Aerial Vehicle(UAV), Hadoop Distributed File System(HDFS)

中图分类号: 

• TP301