无人靶车是军事训练中必不可少的一部分.为了测试各类精准打击武器,需要无人靶车自主移动到目标点.由于测试武器的场地环境比较复杂,所以需要无人靶车躲避移动过程中突如其来的障碍物.基于贪心初始化算法的动态避障算法(Greedy Initialization Dynamic Obstacle Avoidance,GIDOA)可以求解无人靶车动态避障路径规划问题(Dynamic Obstacle Avoidance Routing for Unmanned Target Vehicle,DOARUTV).该算法在传统的D*算法(Digital Smart Technologies for Amateur Radio)的基础上进行改进,结合贪心初始化算法,有效地探索了初始解,加快了算法迭代速度.为了验证该算法的可行性和有效性,使用Python编程语言将GIDOA和迪克斯特拉算法(Dijkstra)、A*算法(A-star Algorithm)进行对比.实验结果表明:GIDOA能够解决具有动态避障功能的无人靶车问题,对比Dijkstra算法和A*算法更适合DOARUTV的求解.
Research on the Dynamic Obstacle Avoidance of Unmanned Target Vehicles
The unmanned target vehicle is an indispensable part in military training.In order to test various types of precision strike weapons,unmanned target vehicles are required to move autonomously to target points.Due to the complex environment of the sites for testing weapons,it is necessary for the unmanned target vehicle to avoid sud-den obstacles during moving.The greedy initialization dynamic obstacle avoidance(GIDOA)algorithm can solve the dynamic obstacle avoidance routing for unmanned target vehicles(DOARUTV).The algorithm is improved on the basis of the traditional D*(Digital Smart Technologies for Amateur Radio)algorithm and is combined with the greedy initialization algorithm,and it effectively explores the initial solution and accelerates the iteration speed of the algorithm.In order to verify the feasibility and effectiveness of the algorithm,GIDOA is compared with the Dijkstra algorithm and the a-star algorithm by using Python programming language.Experimental results show that GIDOA can solve the problem of unmanned target vehicles with the function of dynamic obstacle avoidance,and that it is more suitable for the solution of DOARUTV than the Dijkstra algorithm and the A* algorithm.
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