  Real-time navigation in dense human environments is a challenging problem in
robotics. Most existing path planners fail to account for the dynamics of
pedestrians because introducing time as an additional dimension in search space
is computationally prohibitive. Alternatively, most local motion planners only
address imminent collision avoidance and fail to offer long-term optimality. In
this work, we present an approach, called Dynamic Channels, to solve this
global to local quandary. Our method combines the high-level topological path
planning with low-level motion planning into a complete pipeline. By
formulating the path planning problem as graph searching in the triangulation
space, our planner is able to explicitly reason about the obstacle dynamics and
capture the environmental change efficiently. We evaluate efficiency and
performance of our approach on public pedestrian datasets and compare it to a
state-of-the-art planning algorithm for dynamic obstacle avoidance.
