海上无人机多机协同巡检航迹规划方法

Path Planning for Multi-UAV Collaborative Inspections in Sea Environment

随着无人机技术应用的不断深入,如何提高无人机编队的协同能力及在复杂动态环境的自适应性,已成为“集群智能”的1个重要研究方向。文章对海上无人机多机协同航迹规划整体流程进行了分析,分别阐释了静态和动态场景下的多机协同规划方法。静态场景下主要采用分支定界法建立静态问题模型,为每架无人机划分作业区域、生成作业路径,使得整个巡检作业的航迹长度代价与作业时间代价最小;动态场景下主要针对气象变化、连续跟监、海域变化3种突发场景,侧重于协同决策和路径规划设计对应的目标函数,采用启发式算法为整个巡检作业进行自适应航迹规划,以确保安全性和效率性。实验结果显示,无人机协同路径规划能够根据环境变化和任务需求动态调整多无人机的巡检路径,快速给出不同突发情况下的最佳动态调整方案,以应对复杂的海上环境并动态规避障碍物。

With the increasing utilization of UAVs,enhancing the cooperative capability and adaptability of the UAV formation in complex and dynamic environments has become a crucial research focus in"Cluster Intelligence".The overall process of collaborative trajectory planning for multi-UAV on the sea is analyzed,and the methods for collaborative trajectory planning in both static and dynamic situations are presented respectively.In the static condition,the planning algorithm based on branch-and-bound algorithm is primarily used to establish a static problem model,which divides the operational area and generates the operation path for each UAV,so as to minimize the cost of trajectory length and operation effective time for the entire inspection operation.In the dynamic condition,three unexpected situations,such as meteorological changes,continuous monitoring and sea area alterations,are mainly targeted,focusing on collaborative decision-making and path planning,designing the corresponding objective functions.Additionally,a heuristic algorithm is employed to conduct adaptive trajectory planning for the entire inspection operation,ensuring both safety and efficiency.The results demonstrate that the collaborative trajectory planning of multi-UAV could dynamically adjust the inspection path according to the environmental changes and mission requirements,and it also could swiftly provide the optimal dynamic adjustment scheme for different unexpected situations,effectively dealing with the complex maritime environment and dynamic obstacles.