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.

翻译：本文提出MOON（多目标优化驱动的物体目标导航），这是一种专为大规模复杂室内环境中高效导航而设计的新型框架。现有方法通常依赖局部启发式策略，在广阔区域中往往难以处理竞争目标之间的战略权衡。为克服这一局限，我们将该任务形式化为多目标优化问题，在基于前沿的探索与已观测地标利用之间进行平衡。我们的原型系统整合了三大核心支柱：（1）采用QOM [IROS05]实现判别性地标编码；（2）利用StructNav [RSS23]增强导航流程；（3）构建可变视野集合定向问题（SOP）公式以实现全局连贯规划。为提升框架可扩展性，我们为预算约束的SOP公式及数据驱动的模式切换策略提供了详细的理论基础，该策略支持长视野资源分配。此外，我们提出了一种高速神经规划器，将专家求解器蒸馏至基于Transformer的模型中，在保持高质量规划的同时将决策延迟降低近10倍。