基于遗传算法的多无人机协同逆推式路径规划

综合了多无人机群的任务和任务区域的特点,在优化过程中考虑了机群综合获利效益、无人机(UAV)的的最大转弯角限制和多无人机成员间防碰和威胁区域的生存概率,合理构建了UAV任务的目标函数和约束条件。将协同逆推预测控制(CRH)方法引入无人机机群路径规划方法的研究中,并采用遗传算法求出了满足目标和约束要求的控制量。仿真算例表明采用介绍的协同逆推式路径规划算法,任务空间中无人机能够自动地分别访问不同的目标以得到最大的获利值,证明了算法的有效性。

Cooperative Receding Horizon Path Planning of Multiple UAVs by Genetic Algorithm

A setting where multiple Unmanned Aerial Vehicles(UAVs) cooperatively visiting multiple targets to get rewards associated with the importance of the target is discussed.Cooperative Receding Horizon Control Strategy(CRH) and Genetic Algorithm are applied to solving the optimal control law to maximize an objective function and meet the needs of constraints.Integrating the feature of the mission and mission region of the multiple UAVs,a reasonable objective function and constraints are formulated by the total rewards of the group of UAVs,the maximum turning angle constraint,the survival probability of the threatening area and the collision avoidance between UAVs.Actually,Cooperative Receding Horizon Control Strategy(CRH) is a very effective control method to solve cooperative path planning problem for multiple UAV team,but its procedure of planning which needs to solve some optimal problems is very slow.In order to get the cooperative paths for multiple UAV team more rapidly,Genetic Algorithm is used.Numerical simulation illustrates that every target in the mission region is assigned to UAVs to visit to obtain maximum rewards,and it proves the validity of the proposed CRH path planning method.