基于RRT算法的无人机路径规划应用研究

Research on UAV Path Planning Application Based on RRT Algorithm

经典的路径规划算法大都需要在全局已知空间中对环境进行建模,包括人工势场法、遗传算法、启发式算法、仿生学算法等。由于需要预先构建环境,因此这些方法并不适合解决在高维度空间中的路径规划问题。基于快速扩展随机树(RRT)的路径规划方式其优势在于可以避免对全局环境的构建,通过对状态空间进行随机采样,检测碰撞点,能够有效地解决在平面及三维状态空间下的复杂路径规划问题。通过与人工势场法和A*算法进行比对,确定了RRT算法在复杂环境中解决无人机路径规划问题的优势,在对相关参数进行优化后该方法是概率完备且存在最优解的,同时在固定翼智能集群飞行编队控制及协同项目中应用。

Traditional global path planning algorithms include artificial potential field method,genetic algorithm,intelligent bionics algorithm,heuristic algorithm and so on.However,these methods all need to model obstacles in the known global space,and are not suitable for solving the planning problem of multi-degree-of-freedom robots in complex environments.The path planning algorithm based on rapidly exploring random tree,through the collision monitoring of sampling points in the state space,avoids the modeling of the global space,and can effectively solve the path planning problems of high-dimensional space and complex constraints.By comparing with the artificial potential field method and the A*algorithm,the advantages of the RRT algorithm in solving the UAV path planning problem in a complex environment is determined in the paper.After optimizing the relevant parameters,the method is probabilistic and has an optimal solution,and applied in the Fixed-wing intelligent cluster flight formation control and coordination project at the same time.