College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China.
Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation, Shenzhen University, Shenzhen 518060, China.
Sensors (Basel). 2022 Jul 12;22(14):5217. doi: 10.3390/s22145217.
Path planning for wheeled mobile robots on partially known uneven terrain is an open challenge since robot motions can be strongly influenced by terrain with incomplete environmental information such as locally detected obstacles and impassable terrain areas. This paper proposes a hierarchical path planning approach for a wheeled robot to move in a partially known uneven terrain. We first model the partially known uneven terrain environment respecting the terrain features, including the slope, step, and unevenness. Second, facilitated by the terrain model, we use A⋆ algorithm to plan a global path for the robot based on the partially known map. Finally, the -learning method is employed for local path planning to avoid locally detected obstacles in close range as well as impassable terrain areas when the robot tracks the global path. The simulation and experimental results show that the designed path planning approach provides satisfying paths that avoid locally detected obstacles and impassable areas in a partially known uneven terrain compared with the classical A⋆ algorithm and the artificial potential field method.
轮式移动机器人在部分已知非均匀地形上的路径规划是一个开放性挑战，因为机器人的运动可能会受到环境信息不完全的地形的强烈影响，例如局部检测到的障碍物和无法通行的地形区域。本文提出了一种轮式机器人在部分已知非均匀地形上移动的分层路径规划方法。我们首先根据地形特征（包括坡度、台阶和不平整）来建模部分已知的非均匀地形环境。其次，借助地形模型，我们使用 A⋆算法根据部分已知地图为机器人规划全局路径。最后，使用 - 学习方法进行局部路径规划，以在机器人跟踪全局路径时避免近距离局部检测到的障碍物和无法通行的地形区域。仿真和实验结果表明，与经典的 A⋆算法和人工势场方法相比，所设计的路径规划方法在部分已知非均匀地形中提供了满意的路径，可以避免局部检测到的障碍物和无法通行的区域。
