A Recursive Algorithm for Indoor Positioning Using Pulse-Echo Ultrasonic Signals

The acoustic emission (AE) technique is one of the most widely used in the field of structural monitoring. Its popularity mainly stems from the fact that it belongs to the category of non-destructive techniques (NDT) and allows the passive monitoring of structures. The technique employs piezoelectric sensors to measure the elastic ultrasonic wave that propagates in the material as a result of the crack formation’s abrupt release of energy. The recorded signal can be investigated to obtain information about the source crack, its position, and its typology (Mode I, Mode II). Over the years, many techniques have been developed for the localization, characterization, and quantification of damage from the study of acoustic emission. The onset time of the signal is an essential information item to be derived from waveform analysis. This information combined with the use of the triangulation technique allows for the identification of the crack location. In the literature, it is possible to find many methods to identify, with increasing accuracy, the onset time of the P-wave. Indeed, the precision of the onset time detection affects the accuracy of identifying the location of the crack. In this paper, two techniques for the definition of the onset time of acoustic emission signals are presented. The first method is based on the Akaike Information Criterion (AIC) while the second one relies on the use of artificial intelligence (AI). A recurrent convolutional neural network (R-CNN) designed for sound event detection (SED) is trained on three different datasets composed of seismic signals and acoustic emission signals to be tested on a real-world acoustic emission dataset. The new method allows taking advantage of the similarities between acoustic emissions, seismic signals, and sound signals, enhancing the accuracy in determining the onset time.

Section: Sed For Acoustic Emissions Onset Time Detectionmentioning

confidence: 99%

Acoustic Emission and Artificial Intelligence Procedure for Crack Source Localization

Melchiorre

Bertetto

Rosso

et al. 2023

“…This approach is a map-matching-based algorithm, and it creates a smooth surface representation of the environment that is modeled by a set of local probability density functions. Another interesting approach to solving the indoor localization problem is using pulse-echo ultrasonic signals [ 20 ].…”

Section: State Of the Artmentioning

confidence: 99%

A Machine Learning Approach to Robot Localization Using Fiducial Markers in RobotAtFactory 4.0 Competition

Klein

Braun

Mendes

et al. 2023

Localization is a crucial skill in mobile robotics because the robot needs to make reasonable navigation decisions to complete its mission. Many approaches exist to implement localization, but artificial intelligence can be an interesting alternative to traditional localization techniques based on model calculations. This work proposes a machine learning approach to solve the localization problem in the RobotAtFactory 4.0 competition. The idea is to obtain the relative pose of an onboard camera with respect to fiducial markers (ArUcos) and then estimate the robot pose with machine learning. The approaches were validated in a simulation. Several algorithms were tested, and the best results were obtained by using Random Forest Regressor, with an error on the millimeter scale. The proposed solution presents results as high as the analytical approach for solving the localization problem in the RobotAtFactory 4.0 scenario, with the advantage of not requiring explicit knowledge of the exact positions of the fiducial markers, as in the analytical approach.

“…In [ 11 ], a study on a recursive algorithm for indoor positioning using pulse-echo ultrasonic signals was investigated. Ultrasounds are widely used for real-time applications in short-range communication systems and one of the parameters widely used is TOF, which can be evaluated by using different techniques.…”

Section: Relevant Contributionsmentioning

confidence: 99%

Advanced Sensors and Systems Technologies for Indoor Positioning

Carotenuto

Iero

Merenda

2022