Calibration of ultrasonic sensors of a mobile robot

Paunović, Ivan; Todorovic, Darko; Božić, Miroslav; Djordjevic, S Goran

doi:10.2298/sjee0903427p

Cited by 4 publications

(2 citation statements)

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“…In the case of ultrasonic sensors, the waves travel in the air at the speed of light and bounce back after striking the landmarks with the same speed. An object's distance can be measured by calculating the time of the signal from emission to the echo reception [49]. Usually, a sonar sensor actuates in the underwater environment at low frequency [50], and ultrasonic sensors operate at high frequency in the air.…”

Section: Sensors Approach Implementedmentioning

confidence: 99%

Investigation of Widely Used SLAM Sensors Using Analytical Hierarchy Process

et al. 2022

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Applications of mobile robots are continuously capturing the importance in numerous areas such as agriculture, surveillance, defense, and planetary exploration to name a few. Accurate navigation of a mobile robot is highly significant for its uninterrupted operation. Simultaneous localization and mapping (SLAM) is one of the widely used techniques in mobile robots for localization and navigation. SLAM consists of front- and back-end processes, wherein the front-end includes SLAM sensors. These sensors play a significant role in acquiring accurate environmental information for further processing and mapping. Therefore, understanding the operational limits of the available SLAM sensors and data collection techniques from a single sensor or multisensors is noteworthy. In this article, a detailed literature review of widely used SLAM sensors such as acoustic sensor, RADAR, camera, Light Detection and Ranging (LiDAR), and RGB-D is provided. The performance of SLAM sensors is compared using an analytical hierarchy process (AHP) based on various key indicators such as accuracy, range, cost, working environment, and computational cost.

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Section: Sensors Approach Implementedmentioning

confidence: 99%

Investigation of Widely Used SLAM Sensors Using Analytical Hierarchy Process

et al. 2022

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“…If the sensors are included with the mobile robot, the manufacturer usually gives a model to convert raw data into distance, however such model provides an average performance of the distance estimation since this built-in model is not tuned for each sensor. Thus, in order to increase the accuracy of the distance estimation, the traditional method of calibration for a suitable distance model, turns frequently into a manual and time-consuming process [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

A Neural Network Approach for Building An Obstacle Detection Model by Fusion of Proximity Sensors Data

Farías

Fábregas

Peralta

et al. 2018

Sensors

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Proximity sensors are broadly used in mobile robots for obstacle detection. The traditional calibration process of this kind of sensor could be a time-consuming task because it is usually done by identification in a manual and repetitive way. The resulting obstacles detection models are usually nonlinear functions that can be different for each proximity sensor attached to the robot. In addition, the model is highly dependent on the type of sensor (e.g., ultrasonic or infrared), on changes in light intensity, and on the properties of the obstacle such as shape, colour, and surface texture, among others. That is why in some situations it could be useful to gather all the measurements provided by different kinds of sensor in order to build a unique model that estimates the distances to the obstacles around the robot. This paper presents a novel approach to get an obstacles detection model based on the fusion of sensors data and automatic calibration by using artificial neural networks.

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