优化有向路标图：基于随机梯度下降的多智能体路径规划
Optimized Directed Roadmap Graph for Multi-Agent Path Finding Using Stochastic Gradient Descent
AI总结：
提出ODRM方法，通过随机梯度下降优化有向路标图顶点与边方向，减少多智能体碰撞，提升集中式和分散式规划性能。
摘要：
我们提出了一种名为优化有向路标图（ODRM）的新方法。这是一种构建有向路标图的方法，可在多机器人导航中实现碰撞避免。这是一个高度相关的问题，例如在工业自动导引车中。ODRM的核心思想是，有向路标图可以编码环境的固有属性，当智能体在同一环境中需要相互避让时，这些属性非常有用。与概率路标图（PRMs）类似，ODRM的第一步是从C空间生成样本。在第二步中，ODRM通过随机梯度下降（SGD）优化顶点位置和边方向。这导致了诸如边与墙壁平行以及类似双车道街道或环形交叉口的模式等涌现特性。智能体随后可以在此图上独立搜索路径，并在运行时解决发生的智能体间碰撞，从而进行导航。与未优化的图相比，使用ODRM生成的图可显著减少智能体间碰撞的发生。我们使用集中式和分散式规划器评估了我们的路标图。实验表明，使用ODRM，即使是简单的集中式规划器也能解决其他多智能体规划器无法解决的高数量智能体问题。此外，我们使用带有分散式规划器和在线碰撞避免的模拟机器人，展示了智能体在我们的路标图上比在标准网格地图上快得多。
英文摘要：
We present a novel approach called Optimized Directed Roadmap Graph (ODRM). It is a method to build a directed roadmap graph that allows for collision avoidance in multi-robot navigation. This is a highly relevant problem, for example for industrial autonomous guided vehicles. The core idea of ODRM is, that a directed roadmap can encode inherent properties of the environment which are useful when agents have to avoid each other in that same environment. Like Probabilistic Roadmaps (PRMs), ODRM's first step is generating samples from C-space. In a second step, ODRM optimizes vertex positions and edge directions by Stochastic Gradient Descent (SGD). This leads to emergent properties like edges parallel to walls and patterns similar to two-lane streets or roundabouts. Agents can then navigate on this graph by searching their path independently and solving occurring agent-agent collisions at run-time. Using the graphs generated by ODRM compared to a non-optimized graph significantly fewer agent-agent collisions happen. We evaluate our roadmap with both, centralized and decentralized planners. Our experiments show that with ODRM even a simple centralized planner can solve problems with high numbers of agents that other multi-agent planners can not solve. Additionally, we use simulated robots with decentralized planners and online collision avoidance to show how agents are a lot faster on our roadmap than on standard grid maps.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
