无人机3D航迹规划及动态避障算法研究

Research on UAV 3D flight track planning and dynamic obstacle avoidance algorithm

规划一条高时效且低代价的三维(3D)航行轨迹,成为目前无人机广泛应用亟须解决的问题。针对蚁群算法在航迹规划中出现的航迹长度和平滑性不足问题,通过改进蚁群系统中的节点移动规则、构造多重启发信息并结合粒子群优化算法的全局搜索能力,提出了蚁群粒子群融合算法。同时,就飞行航迹中出现的动态避障问题和目标点变化问题,提出了改进生物启发神经动力学模型算法,该算法针对3D静态最优航迹中出现的障碍物和目标点变化,实现了局部在线航迹调整。实验仿真结果表明,蚁群粒子群融合算法能在3D静态环境中规划出一条期望航迹。同时,改进生物启发神经动力学模型算法不仅能对突发障碍动态避障,还能对动态目标点变化实时跟踪。

Planning a high-efficiency and low-cost three-dimensional(3 D)flight track has become an urgent problem to be solved for UAV extensive application.Aiming at the problems of track length and lack of smoothness of ant colony algorithm in the flight track planning,this paper proposes the ant colony particle swarm fusion algorithm,which improves the node movement rules in ant colony system,constructs multiple heuristic information and combines the global search ability of particle swarm optimization algorithm.Meanwhile,to solve the problems of dynamic obstacle avoidance and target point change in the flight track,an improved bio-inspired neural dynamics model algorithm is proposed,which realizes local online flight track adjustment for the obstacles and target point change in the 3 D static optimal flight track.Experiment simulation results show that the ant colony particle swarm fusion algorithm can plan an expected track in 3 D static environment.At the same time,the improved bio-inspired neural dynamics model algorithm can not only dynamically avoid sudden obstacles,but also track the changes of dynamic target points in real time.