This paper combines pathfinding and influence fields (or influence maps) in a way that the influence costs replace the traditional distance costs. By placing repulsors and attractors in the game world, we are mixing their influence fields into an overall game influence field, which determines the influence costs in finding a path between a start and a goal node. For that purpose, we introduce an automated method to place such attractors and repulsors, which builds upon the concept of thinning graph (and its dual). This thinning graph is a sort of medial axis of the set of passable tiles of the game map. As a result, we obtain bounded-search pathfinders that avoid obstacles (repulsors) and follows check points (attractors) in an intuitive manner. Moreover, the well-known problem underlying the influence fields, called local extremum trap (i.e., an agent may enter into a place from where there is no escape away), is solved in an elegant way. Also, our influence-driven bounded-search pathfinders are comparable to other influence-based pathfinders in terms of memory expenditure and time performance.

中文翻译：

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基于吸引子和排斥子生成的影响图的有界搜索探路者

本文以影响成本取代传统距离成本的方式结合了寻路和影响场（或影响图）。通过在游戏世界中放置排斥子和吸引子，我们将它们的影响场混合到一个整体的游戏影响场中，这决定了在开始和目标节点之间寻找路径的影响成本。为此，我们引入了一种自动化方法来放置此类吸引子和排斥子，该方法基于细化图（及其对偶）的概念。这个细化图是游戏地图的一组可通过图块的一种中轴。结果，我们获得了有界搜索探路者，以直观的方式避开障碍物（排斥物）并跟随检查点（吸引物）。此外，影响场背后的众所周知的问题，称为局部极值陷阱（即，代理可能会进入一个无法逃脱的地方），以优雅的方式解决。此外，我们的影响驱动的有界搜索探路者在内存消耗和时间性能方面与其他基于影响的探路者相当。