Next-Best-Sense: A Multi-Criteria Robotic Exploration Strategy for RFID Tags Discovery

Abstract

Automated exploration is one of the most relevant applications for autonomous robots. In this paper, 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. The code developed is publicly available on the authors' repository 1 .

Navigate-and-Seek: A Robotics Framework for People Localization in Agricultural Environments

“…In this paper, we present a robotic framework for localizing and tracking people in a structured environment. It combines an improved formulation of the topological particle filter (TPF) with a multi-criteria optimization path planner such as Next-Best-Sense (NBS) [2]. NBS is used to identify new robot positions which can lead to obtaining meaningful observations for improving the TPF estimate on the people's location.…”

“…To further improve the estimate of the TPF, we adopted a decision-making framework for planning the next robot position to be the closest to the estimated picker location or to allow meaningful readings. With this in mind, we chose to use Next-Best-Sense [2], an iterative exploration algorithm that executes sensing actions and updates a belief map with information from the most recent observation. Full details on the approach are reported in the original publication, we try now to summarize the most important bits of information.…”

“…Finding the best set of weights for the criteria is out of the scope of this paper. Based on the study shown in [2], we identified a combination of weights working for the current application and we kept it fixed across all the experiments reported in the following section.…”

“…The initial work [1] solved the problem employing a "Topological Particle Filter" (TPF) and focused entirely on exploiting such sparsely linked topological graphs to compensate for the noise and inaccuracies of deployed cheap GNSS IoT sensors or mobile phones carried by human workers, to allow them to summon a robot to their locations as part of a robotic in-field logistics solution. This paper takes this idea and the TPF approach further, making the following tangible contributions: (i) Generalization of the TPF framework to support a larger variety of information sources to be fused, i.e., complementing noise GNSS information with detections of humans from a robot's onboard sensing such as LIDAR-based and RFID identification; (ii) complementing the opportunistic and passive sensing approach proposed in [1] with an intelligent decision-making active sensing approach known as Next-Best-Sense (NBS) [2] to utilize the onboard sensors of the robotic platforms to further reduce the uncertainty of humans' location in the field, i.e., to actively move robots around the environment to systematically reduce sensing uncertainty; (iii) a comprehensive evaluation of the proposed integrated approach in a digital twin of the real-world logistics scenario, in which a fleet of robots is supporting human fruit pickers in soft-fruit production, by allowing them to focus on the picking of fruits themselves, while the robotic fleet automates the transportation of picked fruits from the human picker to a local storage facility.…”

Design and Analysis of UHF RFID Tag using MATLAB

“…Polvara R., et al [7] Suggested a framework online coverage algorithm called Next-Best-Sense (NBS), an extension of the Exploration Algorithms Next-Best-View class which optimises the exploration task by balancing multiple criteria. The issue of finding all Radio Frequency Identification tags with a mobile robot is solved with NBS.…”