Energy-efficient mid-term strategies for collision avoidance in crowd simulation

Abstract

When navigating in crowds, humans are able to move efficiently between people. They look ahead to know which path would reduce the complexity of their interactions with others. Current navigation systems for virtual agents consider the long-term planning to find a path in the static environment and the short term reaction to avoid collision with close obstacles. Recently some mid-term considerations have been added to avoid high density areas. However, there is no mid-term planning among static and dynamic obstacles that would enable the agent to look ahead and avoid difficult paths or find easy ones as human do. In this paper we present a system for such mid-term planning. This system is added to the navigation process between the path finding and the local avoidance to improve the navigation of virtual agents. We show the capacities of such system on several case studies. Finally we use an energy criterion to compare trajectories computed with and without the mid-term planning.