自适应斥力系数的无人机路径规划

使用人工势场法进行无人机路径规划时,往往存在目标不可达、运动轨迹迂回反复和路径长度过长等问题.传统的人工势场法不能根据环境具体信息对斥力系数进行调整,而现有的改进方法不能在自适应调整斥力系数的同时兼顾规划效果和规划时长.针对以上问题,提出了一种基于深度学习的无人机自适应斥力系数路径规划方法.首先通过融合遗传算法与人工势场法找出在特定环境下最合适的斥力系数样本集,其次利用该样本集训练残差神经网络,最后通过残差神经网络计算适应环境的斥力系数,进而使用人工势场法进行路径规划.仿真实验表明,该方法在一定程度上解决了人工势场法规划中目标不可达、运动轨迹迂回反复和路径长度过长等问题,规划效果和规划时长方面均有优异表现,能很好地满足无人机路径规划中对当前环境的自适应要求和快速规划的要求.

Path Planning of UAV Based on Adaptive Repulsion Coefficient

When the artificial potential field method is employed for unmanned aerial vehicle (UAV) path planning, there are often some problems, such as unreachable targets, repeated motion trajectories, and large path lengths. The traditional artificial potential field method fails to adjust the repulsion coefficient according to the specific information of the environment, while the existing improved methods cannot take into account the planning effect and planning time while adaptively adjusting the repulsion coefficient. To solve the above problems, this study proposes a UAV path planning method based on the adaptive repulsion coefficient with the help of deep learning. Firstly, the most suitable repulsion coefficient sample set in a specific environment is found by integrating a genetic algorithm and the artificial potential field method. Secondly, a residual neural network is trained with the sample set. Finally, the repulsion coefficient adapted to the environment is calculated by the residual neural network, and then the artificial potential field method is used for path planning. Simulation experiments show that the proposed method solves the abovementioned problems in path planning with the artificial potential field method to a certain extent. It has excellent performance in planning effect and planning time and can well meet the requirements for current environment adaptation and rapid planning in UAV path planning.