Search results

The purpose of this paper is to address the use of passive RFID technology for the development of an autonomous surveillance robot. Passive RFID tags can be used for labelling both valued objects and goal‐positions that the robot has to reach in order to inspect the surroundings. In addition, the robot can use RFID tags for navigational purposes, such as to keep track of its pose in the environment. Automatic tag position estimation is, therefore, a fundamental task in this context.

The paper proposes a supervised fuzzy inference system to learn the RFID sensor model; Then the obtained model is used by the tag localization algorithm. Each tag position is estimated as the most likely among a set of candidate locations.

The paper proves the feasibility of RFID technology in a mobile robotics context. The development of a RFID sensor model is first required in order to provide a functional relationship between the spatial attitude of the device and its responses. Then, the RFID device provided with this model can be successfully integrated in mobile robotics applications such as navigation, mapping and surveillance, just to mention a few.

The paper presents a novel approach to RFID sensor modelling using adaptive neuro‐fuzzy inference. The model uses both Received Signal Strength Indication (RSSI) and tag detection event in order to achieve better accuracy. In addition, a method for global tag localization is proposed. Experimental results prove the robustness and reliability of the proposed approach.

Aims to make a mobile robot able to build accurate 2D and 3D models of its environment while navigating autonomously.

2D map building is performed using a laser range scanner. The map is used by the robot to both localize itself and recognize places already explored. This is the well‐known simultaneous localization and mapping (SLAM) problem. 3D model reconstruction, instead, uses computer vision techniques based on feature extraction and matching.

The experimental results illustrate the validity and accuracy of the reconstructed maps of the environment and enable the robot to navigate autonomously in indoor environments, such as museums, hospitals, airports, offices and so on. Such a robot can play a major role in different tasks such as surveillance, image‐based rendering, remote fruition of hardly accessible sites, monitoring and maintenance applications, reverse engineering in construction. In these areas accurate 3D models in addition to 2D maps can convey a lot of very useful information.

The main contribution of the paper is an interesting integration of different algorithms in an experimental platform that performs 2D map building using a laser range scanner, autonomous navigation and 3D reconstruction of the areas of particular interest.

The purpose of this paper is to investigate the use of passive radio frequency identification (RFID) technology for environment mapping and surveillance by an autonomous mobile robot.

Proposes a fuzzy inference method to localize RFID tags in the environment.

Demonstrates that RFID technology can be successfully integrated in mobile robot systems to support navigation and provide the robot with mapping and surveillance capabilities.

Use of fuzzy reasoning to learn the model of the RFID device and localize the tags, enhancing the capability of the system to recognize and monitor the environment.