Stealthy path planning against dynamic observers

Abstract

In virtual environments, research into the problem of stealthy or covert path planning has either assumed fixed and static motion of observers or has used relatively simple probabilistic models that statically summarize potential behavior. In this paper, we introduce a method that dynamically estimates enemy motion in order to plan covert paths in a prototype game environment. We compare our results to other baseline pathfinding methods and conduct an extensive exploration of the many parameters and design choices involved to better understand the impact of different settings on the success of covert path planning in virtual environments. Our design provides a more flexible approach to covert pathfinding problems, and our analysis provides useful insights into the relative weighting of the different factors that can improve design choices in building stealth scenarios.