复杂动态环境下的移动机器人路径规划

Mohamed Lamine Tazir, O. Azouaoui, Mohamed Hazerchi, M. Brahimi
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引用次数: 8

摘要

近年来,移动机器人路径规划的研究主要集中在动态环境中。在本课题中提出的大多数方法都需要重新规划机器人的剩余路径,每次都有新的信息进入,这大大增加了计算时间,使得这些方法很难实时实现,有时甚至不可能实现。该方法由静态和动态两个不同的模块组成,结合两种导航方法来利用全局静态地图的先验信息和来自传感器的局部信息。机器人利用周围环境的全局信息,利用遗传算法结合Dijkstra算法,通过静态障碍物规划出最优路径。动态阶段是在机器人运动时完成的。该算法通过等待/加速概念(W/ a C)或使用Dijkstra算法生成新的最优局部路径来避开移动障碍物。该工作的一个特别新颖的方面是利用关于环境的先验信息高效地搜索全局路径,并在离线和在线阶段结合Dijkstra算法,从而大大减少了计算时间,并提高了全局轨迹的准确性。加速动作的引入也是一个新的方面。仿真结果验证了该方法在复杂环境下的有效性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mobile robot path planning for complex dynamic environments
The last few years, research in the area of path planning for mobile robots has been focusing on dynamic environments. Most of methods proposed in this topic need to re-plan the remaining path of the robot, every time when new information come in which significantly increase the computation time and make real-time implementation of these methods difficult and sometimes impossible. The proposed approach consists of two different modules (static and dynamic) that combine two navigation methods to exploit prior information of global static map and local information coming from sensors. The robot uses global information about his environment, and plans the optimal path using genetic algorithms (GA) combined with Dijkstra algorithm through static obstacles. The dynamic phase is done while the robot is moving. The algorithm is able to avoid a moving obstacle by wait/Accelerate concept (W/A C), or by producing a new optimal local path using Dijkstra algorithm. A particularly new aspect of the work is to use the prior information about the environment for searching a global path efficiently and the incorporation of Dijkstra algorithm in both off-line and online phases, which leads to a significantly computation time reducing, and increases the accuracy of the global trajectory. The introduction of the accelerate action is also a new aspect. The simulation results confirm the efficiency and effectiveness of this approach in complex environments.
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