EACS: Effective Avoidance Combination Strategy

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 long-term planning to find a path in the static environment and short term reactions to avoid collisions 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 humans do. In this paper we present a system for such mid-term planning. This system is added to the navigation process between pathfinding and local avoidance to improve the navigation of virtual agents. We show the capacities of such a system using several case studies. Finally we use an energy criterion to compare trajectories computed with and without the mid-term planning.