电光与控制, 2023, 30 (3): 63, 网络出版: 2023-04-03  

改进蚁群算法的无人机三维路径规划

Three-Dimensional Path Planning of UAVs Based on Improved Ant Colony Algorithm
作者单位
1 太原理工大学,a.信息与计算机学院, 山西 晋中 030000
2 太原理工大学,b.机械与运载工程学院, 太原 030000
摘要
针对蚁群算法在无人机三维路径规划问题中存在收敛速度慢和容易陷入局部最优的问题, 提出了一种融合改进人工势场的蚁群算法。构造重力势能场, 将改进人工势场的合力作为系数对预搜索可行区域内的信息素进行初始化, 提出一种随机性信息素挥发因子更新机制, 改进蚁群算法的启发函数和信息素更新规则, 引入重力势能来模拟无人机高空飞行, 并将其应用于信息素的更新。最后设置两组对比实验对比4种算法。结果表明, 所提算法有效地解决了蚁群算法存在的问题, 提高了算法搜索路径的效率和能力, 能在不同的环境下最快地收敛到最优值, 证明了该算法的适应性和有效性。
Abstract
Aiming at the problems of slow convergence and easy to fall into local optimum in three-dimensional path planning of Unmanned Aerial Vehicles (UAVs), this paper propeses an ant colony algorithm with improved artificial potential field, constructs a gravitational potential field, and initializes pheromones in the preliminary searchable area with the resultant force of improved artificial potential field as a coefficient.A random pheromone volatilization factor updating mechanism is proposed, which improves the heuristic function and pheromone updating rules of ant colony algorithm, introduces gravitational potential energy to simulate UAV flying at high altitude, and applies it to pheromone updating.Finally, two groups of comparative experiments are set up to compare the four algorithms.The comparison results show that the improved algorithm can effectively solve the problems existing in the ant colony algorithm, improve the efficiency and ability of the algorithm to search the path, and converge to the optimal value in different environments, which proves the adaptability and effectiveness of the algorithm.

孔维立, 王峰, 周平华, 王鹤峰. 改进蚁群算法的无人机三维路径规划[J]. 电光与控制, 2023, 30(3): 63. KONG Weili, WANG Feng, ZHOU Pinghua, WANG Hefeng. Three-Dimensional Path Planning of UAVs Based on Improved Ant Colony Algorithm[J]. Electronics Optics & Control, 2023, 30(3): 63.

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