论文部分内容阅读
栅格法的强适应性和简单直观性使其成为寻找最优耗费路径的流行算法。然而,在实时路径规划中如果单元格划分的精度很高就会使得计算的时间复杂度高,从而不能适应实时运行环境。在动态环境中,路径规划的前面一部分(即里机器人较近的一段路径)是对移动机器人最有用。因此,文章提出将环境不等大小划分,离机器人距离越近计算精度越高而越远计算精度越低,并结合动态信息模型对障碍物运动信息的有效反应得到一种新的路径规划算法。通过仿真实验与在RoboCup中型组机器人上的测试表明了该方法的有效性。
The strong adaptability and simple intuition of grid method make it become a popular algorithm to find the optimal path. However, if the precision of cell division in real-time path planning is high, the computational time complexity will be high, so that it will not be able to adapt to the real-time operating environment. In a dynamic environment, the first part of the path planning (that is, a path closer to the robot) is most useful for mobile robots. Therefore, the paper proposes to divide the environment into different sizes. The closer the robot is to the robot, the higher the calculation accuracy and the farther away from the robot. The lower the accuracy is, and a new path planning algorithm is obtained based on the effective response of the dynamic information model to the obstacle motion information. The effectiveness of this method is demonstrated by simulation experiments and tests on RoboCup medium-sized robots.