Dynamic Channel: A Planning Framework for Crowd Navigation
This addresses the problem of efficient and optimal navigation for robots in crowded settings, though it appears incremental as it builds on existing planning methods.
The paper tackles real-time navigation in dense human environments by introducing Dynamic Channels, a planning framework that combines high-level topological path planning with low-level motion planning to efficiently account for pedestrian dynamics, achieving improved performance compared to a state-of-the-art algorithm on public datasets.
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.