Abstract: The Rapidly-exploring Random Tree（RRT） algorithm is an efficient approach to solve the path planning problem with nonholonomic constraints of wheeled robot. Robot dynamic constraints can be imported to the path planning process of RRT. However the efficiency of the RRT algorithm will be reduced when a lot of obstacles exist in the environment. On the other hand, the path planned with RRT algorithm is non-optimal, which limit the application of RRT in path planning for wheeled robot. Aiming to overcome the above shortages of RRT algorithm, a hybrid strategy based path planning method is presented. A multi-RRT structure is obtained according to artificial-guided points and then traversable areas can be found quickly using the RRT local exploration and merge features; A heuristic search algorithm is used to quickly find a robot trajectory which meets the dynamic constraints in traversable areas. The experiments show that this method can quickly and efficiently solve the problem of wheeled robot path planning in complex obstacle environment.

Key words: hybrid strategy, path planning, Rapidly-Exploring Random Tree（RRT）

摘要： 快速扩展随机树方法（RRT）是解决具有非完整性约束的轮式机器人路径规划问题的一种有效途径。RRT能够在规划过程中引入机器人动力学约束，但是当环境中存在大量障碍物时，RRT算法的路径搜索效率将会降低。另一方面，RRT算法不具有最优性，限制了其在轮式机器人路径规划中的应用。针对经典RRT算法的不足，提出一种混合的路径规划策略，首先通过路径导引点扩展多树RRT结构，利用多树RRT的局部探索与合并特性快速寻找可通行的区域范围，利用启发式搜索算法在可通行区域内快速寻找动力学可行的机器人运动轨迹。仿真与实车实验表明，该方法能够快速有效地解决复杂障碍物环境下的机器人路径规划问题。

关键词: 混合策略, 路径规划, 快速搜索随机树