Ceilbot: A Ceiling-Mounted Robot for Fast and Accurate Localization of Off-the-Shelf RFIDs via Wideband SAR-Based Reinforcement Learning

We present the design, implementation, and evaluation of Ceilbot, a ceiling-mounted robot for efficient and accurate RFID localization. Unlike previous robotic RFID localization systems, which focused primarily on localization accuracy, Ceilbot learns to jointly optimize both the accuracy and speed of localization. To achieve this, we introduce a reinforcement-learning-based (RL) trajectory optimization network that determines the most effective trajectory for a robot-mounted reader antenna. Our algorithm integrates aperture length, estimated tag locations, and location confidence (using a wideband synthetic-aperture-radar formulation) into the state observations to learn the optimal trajectory. We developed an end-to-end prototype of Ceilbot and evaluated it in a practical stockroom-like environment. The prototype includes a standard RFID reader with our custom hardware extension (to enable wideband localization of off-the-shelf RFIDs) and a ceiling robot that moves on a 2D track. In our evaluation, Ceilbot demonstrated a median 3D localization accuracy of 0.17 meters and located over 50 RFID tags $12.5\times $ faster than the state-of-the-art baseline. Our results highlight the potential for RL-based RFID localization to significantly enhance the efficiency of RFID inventory processes across sectors such as manufacturing, retail, and logistics.