Real-Time Scalable Motion Planning for Crowds

Abstract

Real-time crowd motion planning requires fast, realistic methods for path planning as well as obstacle avoidance. The difficulty to find a satisfying trade-off between efficiency and believability is particularly challenging, and prior techniques tend to focus on a single approach. In this paper, we present a hybrid architecture to handle the path planning of thousands of pedestrians in real time, while ensuring dynamic collision avoidance. The scalability of our approach allows to interactively create and distribute regions of varied interest, where motion planning is ruled by different algorithms. Practically, regions of high interest are governed by a long-term potential field-based approach, while other zones exploit a graph of the environment and short-term avoidance techniques. Our method also ensures pedestrian motion continuity when switching between motion planning algorithms. Tests and comparisons show that our architecture is able to realistically plan motion for many groups of characters, for a total of several thousands of people in real time, and in varied environments.