Automated exploration is one of the most relevant applications for autonomous robots. In this letter, we propose a novel online coverage algorithm called Next-Best-Sense (NBS), an extension of the Next-Best-View class of exploration algorithms which optimizes the exploration task balancing multiple criteria. NBS is applied to the problem of localizing all Radio Frequency Identification (RFID) tags with a mobile robot. We cast this problem as a coverage planning problem by defining a basic sensing operation - a scan with the RFID reader - as the field of “view” of the sensor. NBS evaluates candidate locations with a global utility function which combines utility values for travel distance, information gain, sensing time, battery status and RFID information gain, generalizing the use of Multi-Criteria Decision Making. We developed an RFID reader and tag model in the Gazebo simulator for validation. Experiments performed both in simulation and with a robot suggest that our NBS approach can successfully localize all the RFID tags while minimizing navigation metrics, such sensing operations, total traveling distance and battery consumption.
