Introducing the Turtlebot3

Pyo, Yoonseok; Shibata, Yoshihiro; Jung, Ryuwoon; Lim, Tae‐Hoon

doi:10.36288/roscon2016-900770

Cited by 3 publications

(9 citation statements)

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“…ROS is a meta-operating system for robots [37]. As mentioned in Section 1, this is an open-source platform which functions are equivalent to what we expect from an operating system.…”

Section: Mes and Aivsmentioning

confidence: 99%

“…These nodes register as Publisher or Subscriber to a program called 'ROS Master'. This is necessary because Publisher and Subscriber nodes do not communicate directly with each other but through the 'ROS Master' [37]. The communication is simplified by publishing or subscribing messages to ROS topics.…”

Section: Mes and Aivsmentioning

confidence: 99%

“…The communication is simplified by publishing or subscribing messages to ROS topics. ROS related terms like node, topic, message, ROS Master, etc., are explained in Reference [37].…”

Section: Mes and Aivsmentioning

confidence: 99%

“…Similarly, a Publisher is generated which publishes a topic (topic subscribed by LabVIEW Subscriber) to send an acknowledgment to LabVIEW. To take coordinates of the physical location, a map is generated using SLAM (Simultaneous Localization And Mapping) [37] technique. To generate the map, the AIV is taken round the workplace and allowed to scan the surrounding area with its main LiDAR sensor.…”

Section: Ros Packagementioning

confidence: 99%

“…Here, hard codded coordinates of physical locations are used. The coordinates are taken using amcl (adaptive Monte Carlo Localization) node [37], which takes laser scans, Figure 6. Front panel of 'WebserviceA' webservice (after edits) (sfcList data is not displayed because of its specific company product details).…”

Section: Ros Packagementioning

confidence: 99%

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Integration of an MES and AIV Using a LabVIEW Middleware Scheduler Suitable for Use in Industry 4.0 Applications

et al. 2020

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Industry 4.0 uses the analysis of real-time data, artificial intelligence, automation, and the interconnection of components of the production lines to improve manufacturing efficiency and quality. Manufacturing Execution Systems (MESs) and Autonomous Intelligent Vehicles (AIVs) are key elements of Industry 4.0 implementations. An MES connects, monitors, and controls data flows on the factory floor, while automation is achieved by using AIVs. The Robot Operating System (ROS) built AIVs are targeted here. To facilitate MES and AIV interactions, there is a need to integrate the MES and the AIVs to help in building an automated and interconnected manufacturing environment. This integration needs middleware, which understands both MES and AIVs. To address this issue, a LabVIEW-based scheduler is proposed here as the middleware. LabVIEW communicates with the MES through webservices and has support for ROS. The main task of the scheduler is to control the AIV based on MES requests. The scheduler developed was tested in a real factory environment using the SAP MES and a Robotnik ‘RB-1′ robot. The scheduler interface provides real-time information about the current status of the MES, AIV, and the current stage of scheduler processing. The proposed scheduler provides an efficient automated product delivery system that transports the product from process cell to process cell using the AIV, based on the production sequences defined by the MES. In addition, using the proposed scheduler, integration of an MES is possible with any low-cost ROS-built AIV.

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“…ROS is a meta-operating system for robots [37]. As mentioned in Section 1, this is an open-source platform which functions are equivalent to what we expect from an operating system.…”

Section: Mes and Aivsmentioning

confidence: 99%

Section: Mes and Aivsmentioning

confidence: 99%

“…The communication is simplified by publishing or subscribing messages to ROS topics. ROS related terms like node, topic, message, ROS Master, etc., are explained in Reference [37].…”

Section: Mes and Aivsmentioning

confidence: 99%

Section: Ros Packagementioning

confidence: 99%

Section: Ros Packagementioning

confidence: 99%

See 3 more Smart Citations

Integration of an MES and AIV Using a LabVIEW Middleware Scheduler Suitable for Use in Industry 4.0 Applications

et al. 2020

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Examining of the Effect of Geometric Objects on SLAM Performance Using ROS and Gazebo

Aydemir¹,

Tekerek

GÖK

2021

El-Cezeri Fen Ve Mühendislik Dergisi

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An autonomous mobile robot needs a map of the environment and location information relative to the map. Simultaneous Localization and Mapping (SLAM) is a prediction process in which the autonomous mobile robot can use this map to determine its position while building a consistent map. The purpose of this study is to examine the effect of geometric objects on SLAM performance. In this direction, three different experimental areas including equilateral triangular prisms, square prisms and cylinders are designed in Gazebo. The fourth experiment area includes all three geometric objects used in the study. When the mapping times of the four experimental areas were compared, it was seen that the fastest scenario is achieved within triangular-only objects (9 min 55 sec) and the slowest within square (10 min 43 sec). In terms of measures, the generated map including the triangular prisms is the closest to the actual measures of the simulated area. Accordingly, the mapping error was calculated as 0.171 m 2 per 1 m 2 in an interior made of triangular prisms, and 0.682 m 2 in an interior made of square prisms. The obtained results show that the shapes of the geometric objects directly affect the performance of SLAM.

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A 2D-Lidar-Equipped Unmanned Robot-Based Approach for Indoor Human Activity Detection

Bouazizi

Mora

Ohtsuki

2023

Sensors

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Monitoring the activities of elderly people living alone is of great importance since it allows for the detection of when hazardous events such as falling occur. In this context, the use of 2D light detection and ranging (LIDAR) has been explored, among others, as a way to identify such events. Typically, a 2D LIDAR is placed near the ground and collects measurements continuously, and a computational device classifies these measurements. However, in a realistic environment with home furniture, it is hard for such a device to operate as it requires a direct line of sight (LOS) with its target. Furniture will block the infrared (IR) rays from reaching the monitored person thus limiting the effectiveness of such sensors. Nonetheless, due to their fixed location, if a fall is not detected when it happens, it cannot be detected afterwards. In this context, cleaning robots present a much better alternative given their autonomy. In this paper, we propose to use a 2D LIDAR mounted on top of a cleaning robot. Through continuous movement, the robot is able to collect distance information continuously. Despite having the same drawback, by roaming in the room, the robot can identify if a person is laying on the ground after falling, even after a certain period from the fall event. To achieve such a goal, the measurements captured by the moving LIDAR are transformed, interpolated, and compared to a reference state of the surroundings. A convolutional long short-term memory (LSTM) neural network is trained to classify the processed measurements and identify if a fall event occurs or has occurred. Through simulations, we show that such a system can achieve an accuracy equal to 81.2% in fall detection and 99% in the detection of lying bodies. Compared to the conventional method, which uses a static LIDAR, the accuracy reaches for the same tasks 69.4% and 88.6%, respectively.

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Introducing the Turtlebot3

Cited by 3 publications

References 0 publications

Integration of an MES and AIV Using a LabVIEW Middleware Scheduler Suitable for Use in Industry 4.0 Applications

Integration of an MES and AIV Using a LabVIEW Middleware Scheduler Suitable for Use in Industry 4.0 Applications

Examining of the Effect of Geometric Objects on SLAM Performance Using ROS and Gazebo

A 2D-Lidar-Equipped Unmanned Robot-Based Approach for Indoor Human Activity Detection

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