基于云模型蜂群算法的无人机航迹规划

Path Planning for Unmanned Air Vehicles Using Improved Artificial Bee Colony Algorithm

针对无人机(UAV)在复杂战场环境下的生存问题,提出了一种基于云模型的人工蜂群算法的航迹规划。在算法中引入一维正态云模型,利用云模型随机性和稳定性的特点来提高传统人工蜂群算法(ABC)的鲁棒性并避免陷入局部最优,同时引入一个新的概率选择策略来保证种群的多样性。采用改进算法来处理UAV的航迹规划问题时,首先将航迹规划问题通过建模转换成一个多维函数优化问题,然后结合云模型和ABC算法的优势,最后用UAV航迹规划任务对新算法进行测试。仿真实验验证了改进算法在解决UAV航迹规划上的可行性和优越性。

Aiming at the survival problem of unmanned air vehicles （UAV） in the complex combat field, a novel algorithm-artificial bee colony（ABC） algorithm based on cloud model was proposed. Considering the stochastic and the stability of the cloud model, we used the one-dimension normal cloud model to improve the robustness of the ABC algorithm and avoid the local optima. In order to maintain diversity, a new selection strategy was introduced. When the proposed ABC algorithm is applied to solve the above problem, firstly, the UAV path planning problem is transformed into a multi-dimensional optimization problem through environmental modeling. Then the advantages of the ABC algorithm and cloud model are combined. Lastly, the proposed algorithm is tested through the path planning task. The experimental results show that the improved algorithm is feasible and superior in solving UAV path planning.