Abstract We present a novel topology-driven method for enhancing the navigation behavior of agents in virtual environments and crowds. In agent-based crowd simulations, each agent combines multiple navigation algorithms for path planning, collision avoidance, and more. This may lead to undesired motion whenever the algorithms disagree on how an agent should pass an obstacle or another agent. In this paper, we argue that all navigation algorithms yield a strategy: a set of decisions to pass obstacles and agents along the left or right. We show how to extract such a strategy from a (global) path and from a (local) velocity. Next, we propose a general way for an agent to resolve conflicts between the strategies of its algorithms. For example, an agent may re-plan its global path when collision avoidance suggests a detour. As such, we bridge conceptual gaps between algorithms, and we synchronize their results in a fundamentally new way. Experiments with an example implementation show that our strategy concept can improve the behavior of agents while preserving real-time performance. It can be applied to many agent-based simulations, regardless of their specific navigation algorithms. The concept is also suitable for explicitly sending agents in particular directions, e.g. to simulate signage.

中文翻译：

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通过拓扑策略同步人群模拟的导航算法

摘要 我们提出了一种新的拓扑驱动方法，用于增强虚拟环境和人群中代理的导航行为。在基于代理的人群模拟中，每个代理都结合了多种导航算法，用于路径规划、避免碰撞等。每当算法在代理应如何通过障碍物或另一个代理上存在分歧时，这可能会导致不希望的运动。在本文中，我们认为所有导航算法都会产生一种策略：一组决策，用于沿左侧或右侧通过障碍物和代理。我们展示了如何从（全局）路径和（局部）速度中提取这样的策略。接下来，我们提出了一种代理解决其算法策略之间冲突的一般方法。例如，当碰撞避免建议绕行时，代理可能会重新规划其全局路径。因此，我们弥合了算法之间的概念差距，并以一种全新的方式同步它们的结果。示例实现的实验表明，我们的策略概念可以在保持实时性能的同时改善代理的行为。它可以应用于许多基于代理的模拟，无论其特定的导航算法如何。该概念也适用于在特定方向上明确发送代理，例如模拟标牌。