There are many challenges for robot navigation in densely populated dynamic environments. This paper presents a survey of the path planning methods for robot navigation in dense environments. Particularly, the path planning in the navigation framework of mobile robots is composed of global path planning and local path planning, with regard to the planning scope and the executability. Within this framework, the recent progress of the path planning methods is presented in the paper, while examining their strengths and weaknesses. Notably, the recently developed Velocity Obstacle method and its variants that serve as the local planner are analyzed comprehensively. Moreover, as a model-free method that is widely used in current robot applications, the reinforcement learning-based path planning algorithms are detailed in this paper.
翻译:在人口稠密的动态环境中,机器人导航面临许多挑战。本文件对在密集环境中机器人导航的路径规划方法进行了调查。特别是,移动机器人导航框架的路径规划由全球路径规划和地方路径规划组成,涉及规划范围和可执行性。在此框架内,路径规划方法的最新进展在文件中作了介绍,同时审查了其优缺点。值得注意的是,对最近开发的“速度障碍”方法及其作为地方规划员的变量进行了全面分析。此外,作为目前机器人应用中广泛使用的无模式方法,本文件详细介绍了强化学习路径规划算法。