Optimizing Motion-Constrained Pathfinding

Abstract

High-quality motion planning can be quite expensive to perform, yet is critical for many video games. As a result, many approximations are employed to reduce costs. Common approximations include pathfinding on a grid or on a navigation mesh instead of in a real-valued world. As a result, the paths that are found are not immediately appropriate for use, and often require post-processing, such as smoothing. An alternate approach is to incorporate pathfinding constraints directly into A* search. This approach is expensive and so it hasn't been widely applied in games. In this paper we analyze the complexity of planning with complex motion constraints and suggest optimizations which make such planning feasible for use in modern games. We propose the use of two ideas, abstract perimeter heuristics and intermediate search truncation, which can reduce the cost of search by an order of magnitude or more.