基于改进元胞蚁群算法的无人机路径规划

UAV Path Planning Based on Improved Cellular Ant Colony Algorithm

针对传统方法在解决复杂环境下无人机路径规划问题中仿真时长不一致、易陷入局部最优等问题,在栅格化地图的基础上,提出了改进元胞蚁群算法。首先,为统一仿真时间步长,采用六边形栅格地图对飞行空域进行建模;然后,提出一种改进元胞蚁群算法进行路径规划,算法引入势场概念对启发函数进行修正、采用差别搜索策略以引导蚁群快速向目标搜索,并设计一种自适应信息素更新方式以选出优质路线。实验结果表明,所提模型和算法解决了矩形栅格地图中仿真时长不统一的问题,并有效提升了路径寻优速度和全局搜索能力,避免算法陷入局部最优。

Aiming at the problems that traditional methods are inconsistent in simulation time and easy to fall into local optimization in solving UAV path planning problems in complex environment,an improved cellular ant colony algorithm is proposed on the basis of grid map.Firstly,in order to unify the simulation time step,the hexagonal grid map is used to model the flight space;Then,an improved cellular ant colony algorithm is proposed for path planning.The algorithm introduces the concept of potential field to modify the heuristic function,adopts the differential search strategy to guide the ant colony to search for the target quickly,and designs an adaptive pheromone update method to select the optimal route.The experimental results show that the model and algorithm proposed in this paper solve the problem of non-uniform simulation time in rectangular grid map,effectively improve the speed of path optimization and global search ability,and avoid the algorithm falling into local optimization.