基于改进蚁群算法的多旋翼无人机航迹规划研究

Research on path planning of multi-rotor UAV based on improved ant colony algorithm

航迹规划需要无人机快速到达目标点来减少航程,同时要躲避障碍物减小威胁。针对传统的蚁群算法在无人机航迹规划中易出现极值、收敛速度慢等缺陷,文章提出了一种改进的蚁群算法。对环境地图进行坐标转换来避免蚂蚁在最后节点可能横跨多个单元;利用起始点与目标点位置来初始化信息素分布,信息素挥发因子采用时间和空间的自适应更新策略,增强了算法的全局搜索能力和效率;设计了方向和角度最优的启发信息,并构造相应的综合评价函数;最后对航迹采用三阶B样条曲线平滑处理。仿真结果表明,改进后的蚁群算法能够快速收敛于最优航迹,并能很好地适应无人机的飞行要求。

Path planning requires unmanned aerial vehicle(UAV)to reach the target point quickly to reduce the range,while avoiding obstacles to reduce the threat.In view of the shortcomings of traditional ant colony algorithm such as extreme value and slow convergence speed in UAV path planning,an improved ant colony algorithm is proposed.Coordinate transformation of environmental map is carried out to overcome the defect that ants may span multiple units at the last node.The pheromone distribution is initialized by the position of the starting point and the target point.The pheromone volatile factor adopts the adaptive update strategy of time and space,which enhances the global search ability and efficiency of the algorithm.The heuristic information of the optimal direction and angle is designed,and the corresponding comprehensive evaluation function is constructed.Finally,the track is smoothed by cubic B-spline curve.The simulation results show that the improved ant colony algorithm can quickly converge to the optimal trajectory,and can well meet the flight requirements of the rotor UAV.