Online Gas Mapping in Outdoor Environment using Solar-Powered Mobile Robot

Watiasih, Richa; Rivai, Muhammad; Penangsang, Ontoseno; Budiman, Fajar; Tukadi,; Izza, Yusril

doi:10.1109/cenim.2018.8711409

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…In the last decade, cooperative source seeking based on multi-agents (or multirobots) has been drawing more and more attention and widely used in many fields, such as oil exploration [1][2][3], odor source search [4], environmental monitoring [5,6], pollution detection [7], and first-response search and rescue (SAR) tasks [8][9][10], etc. The target signal source can generally be an electromagnetic signal, an acoustic signal, or a chemical or biological signal.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Source Seeking in Scalar Field: A Virtual Structure-Based Spatial-Temporal Method

Chen

Duan

et al. 2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Source seeking problem has been faced in many fields, especially in search and rescue applications such as first-response rescue, gas leak search, etc. We proposed a virtual structure based spatial-temporal method to realize cooperative source seeking using multi-agents. Spatially, a circular formation is considered to gather collaborative information and estimate the gradient direction of the formation center. In terms of temporal information, we make use of the formation positions in time sequence to construct a virtual structure sequence. Then, we fuse the sequential gradient as a whole. A control strategy with minimum movement cost is proposed. This strategy rotates the target formation by a certain angle to make the robot team achieve the minimum moving distance value when the circular team moves to the next position. Experimental results show that, compared with state-of-the-art, the proposed method can quickly find the source in as few distances as possible, so that the formation can minimize the movement distance during the moving process, and increase the efficiency of source seeking. Numerical simulations confirm the efficiency of the scheme put forth. Compared with state-of-the-art source seeking methods, the iterative steps of our proposed method is reduced by 20%, indicating that the method can find the signal source with higher efficiency and lower energy consumption, as well as better robustness.

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Section: Introductionmentioning

confidence: 99%

Cooperative Source Seeking in Scalar Field: A Virtual Structure-Based Spatial-Temporal Method

Chen

Duan

et al. 2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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“…Thus, this robot is expected to be able to provide information about the map of the room in real-time. Mobile robot can be used as a sensor node and moves to map the desired area [14]. Autonomous vehicle navigation has become an important research fields in a variety of applications including navigation, localization, and mapping [15,16].…”

Section: Introductionmentioning

confidence: 99%

2D mapping using omni-directional mobile robot equipped with LiDAR

2020

Self Cite

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A room map in a robot environment is needed because it can facilitate localization, automatic navigation, and also object searching. In addition, when a room is difficult to reach, maps can provide information that is helpful to humans. In this study, an omni-directional mobile robot equipped with a LiDAR sensor has been developed for 2D mapping a room. The YDLiDAR X4 sensor is used as an indoor scanner. Raspberry Pi 3 B single board computer (SBC) is used to access LiDAR data and then send it to a computer wirelessly for processing into a map. This computer and SBC are integrated in robot operating system (ROS). The movement of the robot can use manual control or automatic navigation to explore the room. The Hector SLAM algorithm determines the position of the robot based on scan matching of the LiDAR data. The LiDAR data will be used to determine the obstacles encountered by the robot. These obstacles will be represented in occupancy grid mapping. The experimental results show that the robot is able to follow the wall using PID control. The robot can move automatically to construct maps of the actual room with an error rate of 4.59%.

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“…A method of collision avoidance based on velocity control has been proposed in [6]. Localization in outdoor environment often employs Global Positioning Systems (GPS) such as mobile robot for gas level mapping [7]. A web-camera to replace GPS is employed in wheel chair as a mobile robot for outdoor localization [8].…”

Section: Introductionmentioning

confidence: 99%

CNN-Based Self Localization Using Visual Modelling of a Gyrocompass Line Mark and Omni-Vision Image for a Wheeled Soccer Robot Application

Dikairono¹,

Setiawardhana²,

Purwanto³

et al. 2020

IJIES

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The Convolutional Neural Network (CNN) is an object classification method that has been widely used in recent research. In this paper, we propose CNN for use in the self-localization of wheeled soccer robots on a soccer field. If the soccer field is divided into equally sized quadrants with imaginary vertical and horizontal lines intersecting in the middle of the field, then the soccer field has an identical shape for each quadrant. Every quadrant is a reflection of the other quadrants. Superficially similar images appearing in different positions may result in positioning mistakes. This paper proposes a solution to this problem by using a visual modelling of the gyrocompass line mark and omni-vision image for the CNN-based self-localization system. A gyrocompass is used to obtain the angle of the robot on the soccer field. A 360° omni-vision camera is used to capture images that cover all parts of the soccer field wherever the robot is located. The angle of the robot is added to the omni-vision image using the visual modelling method. The implementation of self-localization without visual modelling gives accuracy rates of 0.3262, and this result is increased to 0.6827 with the proposed methods. The experiment was carried out in the robotics laboratory of the Institut Teknologi Sepuluh Nopember (ITS) with the ITS Robot with Intelligent System (IRIS) robot.

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Online Gas Mapping in Outdoor Environment using Solar-Powered Mobile Robot

Cited by 6 publications

References 10 publications

Cooperative Source Seeking in Scalar Field: A Virtual Structure-Based Spatial-Temporal Method

Cooperative Source Seeking in Scalar Field: A Virtual Structure-Based Spatial-Temporal Method

2D mapping using omni-directional mobile robot equipped with LiDAR

CNN-Based Self Localization Using Visual Modelling of a Gyrocompass Line Mark and Omni-Vision Image for a Wheeled Soccer Robot Application

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