无人机在多目标工程侦察中的路径优化问题研究

Research on Path Optimization of Unmanned Aerial Vehicles in Multi-Target Engineering Reconnaissance

摘要: 使用四旋翼无人机实施工程侦察，已经成为指挥所构筑与伪装行动中选址侦察的重要手段，无人机对多个工程作业目标实施侦察时的路径优化问题，是旅行商问题(Travelling Salesman problem, TSP)的典型应用，属于组合优化中的非确定性多项式完全(Non-deterministic Polynomial Complete, NPC)问题之一。头脑风暴优化算法(Brain Storm Optimization Algorithm, BSO)、模拟退火算法(Simulated Annealing Algorithm, SA)和遗传算法(Generation Algorithm, GA)都属于解决此问题的启发式智能优化算法，本文通过问题描述、数学建模和算法原理逐步介绍求解思路与方法，并将求解结果进行对比分析，数据表明，头脑风暴优化算法求解速度快、收敛度好、结果更优，能够更好地解决当前部队面临的无人机多目标侦察任务中的路径优化问题。

Abstract: The use of four-rotor unmanned aerial vehicles (UAVs) in engineering reconnaissance has become an important means of location reconnaissance in command post construction and camouflage operations. The path optimization problem of UAVs for reconnaissance of multiple engineering targets is a typical traveling salesman problem. It’s one of the nondeterministic polynomial complete problems in combinatorial optimization. Brain Storm Optimization Algorithm (BSO), Simulated Annealing Algorithm (SA) and Genetic Algorithm (GA) are heuristic optimization algorithms to solve this problem. In this paper, through problem description, mathematical modeling and algorithm principle, the solution ideas and methods are introduced step by step, and the results are compared and analyzed. The Brain Storming Optimization Algorithm has the advantages of high speed, good convergence and better results, which can better solve the path optimization problem of UAV multi-target reconnaissance mission.