With　the　proliferation　of　smartphones　and　the　rapid　development　of　the　Internet　of　Things　(IoT),　Location-Based　Services　(LBS)　have　garnered　increased　attention,　particularly　in　the　context　of　Indoor　Positioning　Systems　(IPS).　Mainstream　positioning　technologies　perform　poorly　indoors　due　to　obstructions,　necessitating　specialized　IPS.　A　significant　advantage　of　WiFi-based　positioning　is　its　ability　to　operate　without　additional　hardware,　leveraging　existing　mobile　devices.　However,　WiFi-based　positioning　faces　challenges,　including　the　labor-intensive　and　costly　process　of　manual　fingerprint　data　collection　and　the　limitations　of　traditional　feature　extraction　methods　in　meeting　accuracy　demands.　This　paper　proposes　a　novel　deep　learning-based　indoor　positioning　method　utilizing　robotic　WiFi　fingerprint　data　collection.　By　employing　a　robotic　platform　for　automated　data　collection　and　a　Deep　Long　ShortTerm　Memory　(DLSTM)　model　to　extract　complex　temporal　features　of　WiFi　data,　it　enhances　both　the　efficiency　and　accuracy　of　indoor　positioning.　Experimental　results　demonstrate　significant　improvements　in　positioning　accuracy　compared　to　traditional　methods.　©　2024　IEEE.