基于采样的代价图瓶颈寻路算法
Efficient sampling-based bottleneck pathfinding over cost maps
摘要：
我们介绍了一个简单而有效的抽样为基础的规划，是专为瓶颈寻路：给定一个隐式定义的成本地图$\mathcal{M}:\mathbb{R}^d\rightarrow \mathbb{R}$，它分配给空间中的每一个点一个真正的价值，我们希望找到一条路径连接两个给定的点，最大限度地减少相对于~ $\mathcal{M}$的最大值。我们证明了我们的算法，我们称之为瓶颈树（BTT），在几个具有挑战性的情况下，涉及多个代理的问题，它优于国家的最先进的成本地图规划技术T-RRT* 的能力。在理论方面，我们研究了我们的方法的渐近性质，并考虑特殊的设置，计算的轨迹必须是单调的所有坐标。这种约束出现在的情况下，其中的问题涉及的多个代理，被限制为沿预定义的路径向前运动的协调。
英文摘要：
We introduce a simple yet effective sampling-based planner that is tailored for bottleneck pathfinding: Given an implicitly-defined cost map $\mathcal{M}:\mathbb{R}^d\rightarrow \mathbb{R}$, which assigns to every point in space a real value, we wish to find a path connecting two given points, that minimizes the maximal value with respect to~$\mathcal{M}$. We demonstrate the capabilities of our algorithm, which we call bottleneck tree (BTT), on several challenging instances of the problem involving multiple agents, where it outperforms the state-of-the-art cost-map planning technique T-RRT*. On the theoretical side, we study the asymptotic properties of our method and consider the special setting where the computed trajectories must be monotone in all coordinates. This constraint arises in cases where the problem involves the coordination of multiple agents that are restricted to forward motions along predefined paths.
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