In real-world applications, the demand for unmanned, autonomous platforms rises; yet these robot platforms are often deployed in highly dynamic environments and are operated by personnel with little to no expertise in formal planning, for whom plan validation must be simple and follow a human-in-the-loop (``four-eyes'') principle. To address this challenge, we propose a two-stage pipeline for automatic goal and constraint extraction for problem instance acquisition, where extracted goals complement the problem instance and constraints guide execution in open-world mobile robot mission planning. Our Large Language Model (LLM)-based goal and constraint extraction (GCE) module parses the operator-defined natural language mission instruction into a structured planner-independent goal and constraint representation while being robust to slightly misphrased mission instructions. In a second stage, a planner-specific projection layer maps these general goals and constraints to the syntax of any downstream planner. In a first validation step, on a fixed-domain benchmark of operator-authored mission instructions, our LLM-based extractor achieves higher exact-match goal identification and constraint coverage than a strong rule-based baseline, and we situate these results within current work. In a second step, we integrate the full pipeline into AUSPEX and showcase the planner-independent to planner-specific projection functionality based on different planners for a sample Search and Rescue (SAR) scenario with UAVs.     «

In real-world applications, the demand for unmanned, autonomous platforms rises; yet these robot platforms are often deployed in highly dynamic environments and are operated by personnel with little to no expertise in formal planning, for whom plan validation must be simple and follow a human-in-the-loop (``four-eyes'') principle. To address this challenge, we propose a two-stage pipeline for automatic goal and constraint extraction for problem instance acquisition, where extracted goals comple...     »