資訊軌跡規劃（例如，物件搜尋、地圖探索）是機器人技術的關鍵技術。由於大型語言模型技術的進步，可以利用更多語意資訊來提升規劃效能。本研究提出了一種語意認知的資訊軌跡規劃(SA-IPP)演算法偵測率假說(HPD)。由於HPD的目標函數具有自適應次模性，因此它提供了理論保證。實驗表明，該演算法的性能優於基準方法。;Informative path planning (e.g., object search, map exploration) is a key technology in robotics. Due to advances in large language model technology, more semantic information can be leveraged to boost the planning performance. This research proposes a semantic-aware informative path planning (SA-IPP) algorithm, hypothesis probability of detection (HPD). Since the objective function of HPD has adaptive submodularity, it provides theoretical guarantees. The experiments demonstrate that the proposed algorithm outperforms benchmark approaches.
