Abstract
Pathfinding has been one of major research areas in video games for many years. It is a key problem that most video games are confronted with. Search algorithm such as Dijkstra’s algorithm and A* algorithm are representing only half of the picture. The underlying map representations such as regular grid, visibility graph and navigation mesh also have significant impact on the performance. This paper reviews the current widely used solutions for pathfinding and proposes a new method which is expected to generate a higher quality path using less time and memory than other existing solutions. The deployment of methodology and techniques is described in detail. The aim and significance of the proposed method in future video games is addressed and the conclusion is given at the end.
Highlights
The video game business is a multi-billion-dollar industry and still growing
This paper proposes a new method called Direction Oriented Pathfinding (DOP) which can provide a high quality solution without sacrificing speed
The main objective of this research is to propose a new pathfinding method called Direction Oriented Pathfinding (DOP) which can find the shortest path on an n-sided-poly-based NavMesh more efficiently in terms of running time and space consumption
Summary
The video game business is a multi-billion-dollar industry and still growing. In the past, better graphics have been one of the main driving forces of sales; this is no longer the case and good graphics alone is not sufficient to fuel sales. As a fundamental AI problem, pathfinding has an unneglectable indirect impact on gaming experience. It determines how a game character can move to a desired destination. Pathfinding generally refers to find an optimal path between any two locations. The general solution is to overlay another map with cost information on the actual game world as shown in Figure 1b and adopt a search algorithm on it to find the least cost path. Current solutions for pathfinding, in the context of video games, either provide a high speed search by sacrificing accuracy or produce an optimal path but using more time and resources [1]. How to find an optimal path more efficiently is still an area of study
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