Port traffic changes on a time scale that is poorly served by fixed sensors and precomputed routes. We therefore formulate vessel guidance as a repeatedly updated planning problem and use an unmanned aerial vehicle (UAV) to supply local observations when conventional sources are delayed or incomplete. Electronic navigational chart (ENC) constraints, automatic identification system (AIS) reports, and UAV detections are registered in a time-indexed representation of the navigation area. This representation drives an ECNA-based collision-risk term within the route optimizer, while a feedback loop revises only those route segments affected by new observations. In the simulation case, the resulting route is 0.57% longer than the geometric shortest path but is smoother and remains computable at millisecond scale. Adding UAV observations raises detection coverage from 72.0% to 96.4% and lowers the reported collision-risk index from 0.38 to 0.12. These results indicate that mobile aerial sensing can make port guidance more responsive without sacrificing online computational feasibility.
Kai-Nan Ma, Wei Pan· Scientific Journal of Intell...· 0 citations
A tourist may describe a desired day as 'relaxed, coastal, suitable for parents, and not too crowded,' whereas a route optimizer requires numerical attributes and explicit constraints. This paper connects these two representations without asking a large language model (LLM) to draw the route itself. The LLM parses a natural-language request into a preference profile; a semantic-spatial network then links that profile to attraction attributes, travel connections, visit durations, and congestion information. Route selection is performed by a multi-criteria model that evaluates preference fit together with distance and time costs. The framework is examined using six attractions in Dalian and four traveler profiles. Compared with the shortest-path baseline, the LLM-assisted method increases the reported preference-matching degree by about 29.1%, although it does not always return the minimum-distance itinerary. The result suggests a practical division of labor: language modeling handles ambiguous user intent, while an explicit optimizer remains responsible for spatial feasibility and resource limits.
Kai-Nan Ma, Wei Pan· Frontiers in Computing and I...· 0 citations
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