多智能体导航融合语义地图与预测控制，高效避障且防死锁。
Hybrid Decision Making for Scalable Multi-Agent Navigation: Integrating Semantic Maps, Discrete Coordination, and Model Predictive Control
- 用共享语义地图和申请策略协调智能体访问区域
- 无需考虑智能体间碰撞，计算效率更高
- 能预判并避免死锁，适合复杂动态场景
本文提出一种在结构化但动态环境中进行多智能体导航的框架，整合三个核心组件：共享语义地图（编码度量与语义环境知识）、用于协调环境区域访问的申请策略，以及满足环境与协调约束的模型预测控制器。该方法的主要优势包括：(i) 强制执行由特定任务需求导出的区域占用约束；(ii) 通过消除智能体间的碰撞规避约束，提升计算可扩展性；(iii) 能够预见并避免智能体之间的死锁。论文包含仿真与实物实验，在多种典型场景中验证了该框架的有效性。
原文摘要 · Abstract (English)
This paper presents a framework for multi-agent navigation in structured but dynamic environments, integrating three key components: a shared semantic map encoding metric and semantic environmental knowledge, a claim policy for coordinating access to areas within the environment, and a Model Predictive Controller for generating motion trajectories that respect environmental and coordination constraints. The main advantages of this approach include: (i) enforcing area occupancy constraints derived from specific task requirements; (ii) enhancing computational scalability by eliminating the need for collision avoidance constraints between robotic agents; and (iii) the ability to anticipate and avoid deadlocks between agents. The paper includes both simulations and physical experiments demonstrating the framework's effectiveness in various representative scenarios.
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