Toward Crowd-Sensitive Path Planning
This addresses the challenge of efficient and safe robot path planning in dynamic crowds, though it appears incremental as it builds on existing navigation architectures.
The paper tackles the problem of robot navigation in crowded environments by predicting average crowd densities in unseen areas, resulting in faster target arrival, reduced travel distance, and fewer collisions compared to traditional A* planning in simulations.
If a robot can predict crowds in parts of its environment that are inaccessible to its sensors, then it can plan to avoid them. This paper proposes a fast, online algorithm that learns average crowd densities in different areas. It also describes how these densities can be incorporated into existing navigation architectures. In simulation across multiple challenging crowd scenarios, the robot reaches its target faster, travels less, and risks fewer collisions than if it were to plan with the traditional A* algorithm.