基于搜索规则和交叉熵优化的无人机路径规划方法

针对快速扩展随机树(RRP)算法计算效率低、不具备渐进最优性等问题，该文提出一种基于搜索规则和交叉熵优化的改进RRT(IRRT)算法。在路径搜索过程中根据当前节点位置及搜索规则，调整搜索步长及搜索方向，实现高效、快速的初始路径规划。然后，利用交叉熵理论优化初始路径，使得路径具备渐进最优性。仿真实验1结果表明所提方法的有效性和收敛性，仿真实验2将该文所提算法与多种变体RRT算法进行比较，结果表明所提算法能够保证计算效率，同时使得路径具备渐进最优性。

Unmanned Aerial Vehicle Path Planning Method Based on Search Rule and Cross Entropy Optimization

The Rapidly-exploring Random Tree (RRT) algorithm has some shortcomings, including low computation efficiency and non-asymptotic optimality. An Improved RRT (IRRT) algorithm based on search rules and cross entropy optimization is presented in this paper. In the path search process, according to the current node position and search rules, the search step size and search direction are adjusted to achieve efficient and rapid initial path planning. Then, the cross entropy theory is applied to optimize the initial path, so that the path has the characteristic of asymptotic optimality. The simulation results of experiment 1 show the effectiveness and convergence of the proposed method, in the second simulation experiment, the proposed algorithm is compared with several variant RRT algorithms, and the results show that the proposed algorithm can ensure the computational efficiency and make the path has the characteristic of asymptotic optimality.