  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.
