基于改进蚁群算法的无人机路径规划

UAV Path Planning Based on Improved Ant Colony Algorithm

为了提高蚁群算法对于无人机的路径规划,提出了一种改进的蚁群算法,利用栅格地图法,改变蚁群算法的转移概率,加入死区判断,可以有效的减少“无效蚂蚁”。并对更新信息素进行改进,增强每一代蚂蚁的最优路径的信息素,其他保持不变。同时舍去每一代蚂蚁的最长路径,用历史最短路径代替。此外,为避免在蚁群算法中陷于局部最优,再通过蚂蚁行走过程中的前后节点分别对目的地进行距离判断,利用正态分布函数对更新后的信息素进行增益。实验结果表明,论文提出改进的蚁群算法可以寻找到更短的路径,同时加快了算法的收敛速度。对比传统的算法,该算法在寻找最优路线更加的稳定。

In order to improve the path planning of the ant colony algorithm for UAV,an improved ant colony algorithm is proposed.By using the grid map method,changing the transfer probability of the ant colony algorithm and adding the dead zone judgment,the"invalid ants"can be effectively reduced.The pheromone of the optimal path of each generation of ants is enhanced by improving the pheromone of the updated pheromone,while the others remain unchanged.At the same time,the longest path of each generation of ants is eliminated and replaced by the shortest path in history.In addition,in order to avoid being trapped in the local optimum in the ant colony algorithm,the distance of the destination is judged by the front and back nodes in the process of ant walking,and the updated pheromone is gained by using the normal distribution function.The experimental results show that the improved ant colony algorithm can find a shorter path and accelerate the convergence speed of the algorithm.Compared with the traditional algorithm,the algorithm is more stable in finding the optimal route.