用多目标优化解决复杂室内导航的全局规划难题
MOON: Multi-Objective Optimization-Driven Object-Goal Navigation Using a Variable-Horizon Set-Orienteering Planner
- 将导航建模为多目标优化问题，平衡探索与地标利用
- 采用变窗长集合定向问题规划，实现全局一致路径决策
- 适合需要长距离高效导航的机器人系统研究者
本文提出MOON（多目标优化驱动的对象目标导航）框架，用于在大规模复杂室内环境中实现高效导航。现有方法多依赖局部启发式策略，难以处理广阔区域中多个目标间的权衡问题。为此，我们将任务建模为多目标优化问题（MOO），在前沿探索与已观测地标利用之间取得平衡。原型系统融合三项核心技术：(1) 使用QOM [IROS05]进行显著地标编码；(2) 借助StructNav [RSS23]提升导航流程；(3) 采用变窗长集合定向问题（SOP）进行全局连贯规划。为支持框架可扩展性，我们提供了预算约束下SOP形式化的理论基础，以及数据驱动的模式切换策略，实现长程资源分配。此外，引入高速神经规划器，通过Transformer模型蒸馏专家求解器，使决策延迟降低近10倍，同时保持高规划质量。
原文摘要 · Abstract (English)
This paper proposes MOON (Multi-Objective Optimization-driven Object-goal Navigation), a novel framework designed for efficient navigation in large-scale, complex indoor environments. While existing methods often rely on local heuristics, they frequently fail to address the strategic trade-offs between competing objectives in vast areas. To overcome this, we formulate the task as a multi-objective optimization problem (MOO) that balances frontier-based exploration with the exploitation of observed landmarks. Our prototype integrates three key pillars: (1) QOM [IROS05] for discriminative landmark encoding; (2) StructNav [RSS23] to enhance the navigation pipeline; and (3) a variable-horizon Set Orienteering Problem (SOP) formulation for globally coherent planning. To further support the framework's scalability, we provide a detailed theoretical foundation for the budget-constrained SOP formulation and the data-driven mode-switching strategy that enables long-horizon resource allocation. Additionally, we introduce a high-speed neural planner that distills the expert solver into a transformer-based model, reducing decision latency by a factor of nearly 10 while maintaining high planning quality.
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