基于运动约束的泛化Field D~＊路径规划

Motion constrained generalized Field D path planning

为了解决基于栅格的路径规划算法因环境描述的离散化导致规划结果不能满足机器人运动约束,以及单一路径代价的局限致使算法无法适用于复杂环境的问题,提出一种基于运动约束的泛化Field D*算法.该算法的代价函数可同时考虑路程、行驶安全以及行驶时间等一个或多个行驶代价.根据机器人运动模型的特性,在路径点提取过程中结合机器人的最小转弯半径,进行满足运动约束的路径平滑.该算法在多组模拟的复杂环境栅格地图中进行测试,实验结果表明,算法对复杂环境有很好的适应性,同时有效提高路径的可执行性.

Grid-based path planning method can＇t meet the motion constraints due to discretization, and its cost function only considers an aspect of navigation costs, which limits it to relative simple environments. To solve these two problems, a motion constrained generalized Field .D algorithm is proposed. In this algorithm, the cost function was designed to involve one or several navigation costs, including distance, safety and time cost. Moreover, according to motion model of robot, the planned path was further smoothed regarding to the constraint of minimum turning radius. A group of simulated grid maps describing complicated environments had been tested. Experiments show that the proposed algorithm not only fits complicated environments but also improves performability of the results.