Way portals: efficient multi-agent navigation with line-segment goals

Sean Curtis, J. Snape, Dinesh Manocha
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引用次数: 27

Abstract

It is a common artifact of multi-agent motion planning for groups of agents, following similar paths, to converge to a line. This occurs because the agents' immediate goals, also known as waypoints, are frequently a common point in space. Contention for the point goal causes agents to line up and generally interferes with agent motion. By extending the definition of an immediate point goal to a line segment, which we call a "way portal", we enable the agents to better utilize the space available to them in responding to dynamic constraints. We present a novel multi-agent navigation algorithm to efficiently compute the trajectories of autonomous agents using these way portals. We have incorporated the concept into a velocity obstacle-based local navigation model and present a new segment optimization algorithm that efficiently computes a new agent velocity with respect to the way portal. We show how way portal data is extracted from current global navigation data structures, such as navigation meshes. The algorithm is relatively simple to implement and has a small run-time cost (approximately 3 μs per agent.) We highlight its performance in different game-like scenarios and observe improved agent behavior and better utilization of free space.
路径入口:具有线段目标的高效多代理导航
在多智能体运动规划中,多智能体沿着相似的路径收敛到一条直线是一个常见的问题。这是因为代理的直接目标(也称为路径点)通常是空间中的公共点。对点目标的争夺导致代理排队,通常会干扰代理的运动。通过将即时点目标的定义扩展到线段,我们称之为“路径入口”,我们使代理能够更好地利用可用的空间来响应动态约束。我们提出了一种新的多智能体导航算法,利用这些路径入口有效地计算自主智能体的轨迹。我们将这一概念融入到基于速度障碍的局部导航模型中,并提出了一种新的分段优化算法,该算法可以有效地计算相对于入口方式的新代理速度。我们展示了如何从当前的全局导航数据结构(如导航网格)中提取门户数据。该算法实现起来相对简单,运行时成本也很小(每个代理大约3 μs)。我们强调了它在不同的类游戏场景中的表现,并观察到智能体行为的改进和自由空间的更好利用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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