# 未知环境中 LTL 任务的并发语义搜索与任务执行

> 原文链接：https://robotics.hellobike.com/papers/2026-09-30-2609-39153
> 频道：papers | 更新日期：2026-09-30 | 标签：arxiv, cs.RO
> 核心摘要：针对未知环境中的 LTLf 任务规划，提出交织语义搜索与任务执行的自适应高层规划，并在逼真室内环境验证。

# 未知环境中 LTL 任务的并发语义搜索与任务执行

::: v-pre
> 原文链接：[Concurrent Semantic Search and Mission Execution for LTL Missions in Unknown Environments](https://arxiv.org/abs/2609.39153)（arXiv 2609.39153 · 发布日期：2026-09-30）

**Concurrent Semantic Search and Mission Execution for LTL Missions in Unknown Environments**

针对未知环境中的 LTLf 任务规划，提出交织语义搜索与任务执行的自适应高层规划，并在逼真室内环境验证。

## 核心要点

- 复杂未知环境任务要求机器人同时推理当前行动与尚需发现的信息。
- 方法在线构建 VLM 驱动的度量-语义场景图，并结合编码任务的 DFA。
- 规划器按任务剩余阶段评估航点价值，统一排序并随新信息重算高层计划。
- 在逼真室内环境五类任务中，比对比方法完成更多任务、覆盖更少、路径更短并遵守任务限制。

**验证范围与局限**：验证范围为逼真室内环境中的五类任务。

## 英文摘要 (Abstract)

Planning complex missions in unknown environments requires robots to reason simultaneously about what they should do and what they still need to discover. Existing approaches for solving LTLf missions typically assume a known environment, or separate the exploration of the environment from the execution of the mission, while semantic exploration methods look for one target at a time and ignore the mission being executed. To fill this gap, our main contribution is an adaptive high-level planning method that interleaves a task-driven semantic search with the execution of the mission, advancing both in a non-myopic manner. Our method leverages two representations built online, a metric-semantic scene graph, built with a Vision Language Model (VLM), that provides the evidence needed to locate the objects the mission refers to, and the deterministic finite automaton (DFA) encoding the mission, that indicates which of them matter at each mission state. At every planning stage, our planner selects the waypoints that are most valuable for both the semantic search and the advancement of the mission, valuing them over the remaining mission stages in order to avoid blocking states. The selected waypoints are then ordered in a single high-level plan, which is recomputed as new information arrives. In photorealistic indoor environments over five mission types, our method completes more missions than the compared approaches while having to cover less of the environment, and it does so with shorter paths and complying with the restrictions imposed by the mission.

作者：Fernando Salanova Gaspar, David Morilla-Cabello, Cristian Mahulea, Eduardo Montijano Muñoz
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## 原始参考来源 (Sources)

- [arXiv 2609.39153：Concurrent Semantic Search and Mission Execution for LTL Missions in Unknown Environments](https://arxiv.org/abs/2609.39153)