Open-world navigation requires a robot to locate a user-specified target in an indoor scene under open-vocabulary queries. Realistic indoor environments are semi-static: walls and furniture largely persist while smaller objects move, relocate, or vanish between visits. Prior-map systems answer queries from object poses recorded at reconstruction time, which expire as soon as objects move, whereas online methods re-derive where to look from current observations every few steps, often through expensive calls to a vision--language model. Assuming that walls and large furniture do not move, we keep their map to plan paths, but use a target--furniture co-occurrence prior to decide where to search. We present PreNav, an ObjectNav framework on semi-static environments. Rather than invoking a vision-language model at each step, PreNav generates a single global search reference before the robot moves: an LLM converts target--furniture co-occurrence into a spatial prior, a conditional diffusion model turns that prior and the persistent structure into an ordered centerline with a band around it, and the robot approaches the target once it is detected online. Experiments on a Gibson ObjectNav benchmark show that PreNav outperforms a strong online baseline under matched structural priors, using a generator trained only on room-level ScanNet++ scenes and applied to floor-level Gibson without fine-tuning. Real-world experiments under object relocation and open-vocabulary queries further validate the approach.

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