基于改进A*算法与天牛须搜索算法的农业机器人路径规划方法

为解决复杂环境下,农业机器人路径规划存在的局部路径欠优、收敛速度慢、折点较多的问题。为解决此问题,本文提出一种基于天牛须搜索算法和A*算法相结合的BACA*全局规划方法。首先,基于A*算法,采用曼哈顿距离作为启发函数进行全局规划；其次,通过适当调整步长的天牛须搜索算法对路径进行优化,缩短了路径长度,降低了转折点数量；最后,采用贝塞尔曲线对路径进行圆滑处理,使机器人在现实场景中能平稳前进。仿真结果表明：与传统A*算法相比,该算法的路径更加平滑,折点数更少；与天牛须搜索算法相比,能保证生成路径的效率性、全局最优性。在缩短路径长度和降低累计转折点数量方面验证了所提方法的有效性。

Research on Path Planning method of Agricultural Robot based on improved A * algorithm and Beetle Antennae search algorithm

In order to solve the problems of poor local path, slow convergence rate and many broken points in the path planning of agricultural robot in complex environment, there are many problems in the path planning of agricultural robot. In order to solve this problem, this paper proposes a BACA* global planning method based on the combination of the Beetle Antennae Search algorithm and the A * algorithm. Firstly, based on the A * algorithm, Manhattan distance is used as the heuristic function for global planning; secondly, the path is optimized by adjusting the step size of Beetle Antennae Search algorithm, which shortens the path length and reduces the number of turning points. Finally, Bezier curve is used to smooth the path so that the robot can advance smoothly in the real world. The simulation results show that compared with the traditional A * algorithm, the path of the algorithm is smoother and the number of broken points is less, and the efficiency and global optimality of the