LPWAN-Based Real-Time 2D SLAM and Object Localization for Teleoperation Robot Control

In this study, the teleoperation robot control on a mobile robot with 2D SLAM and object localization using LPWAN is proposed. The mobile robot is a technology gaining popularity due to flexibility and robustness in a variety of terrains. In search and rescue activities, the mobile robots can be used to perform some missions, assist and preserve human life. However, teleoperation control becomes a challenging problem for this implementation. The robust wireless communication not only allows the operator to stay away from dangerous area, but also increases the mobility of the mobile robot itself. Most of teleoperation mobile robots use Wi-Fi having high-bandwidth, yet short communication range. LoRa as LPWAN, on the other hand, has much longer range but low-bandwidth communication speed. Therefore, the combination of them complements each other’s weaknesses. The use of a two-LoRa configuration also enhances the teleoperation capabilities. All information from the mobile robot can be sent to the PC controller in relatively fast enough for real-time SLAM implementation. Furthermore, the mobile robot is also capable of real-time object detection, localization, and transmitting images. Another problem of LoRa communication is a timeout. We apply timeout recovery algorithms to handle this issue, resulting in more stable data. All data have been confirmed by real-time trials and the proposed method can approach the Wi-Fi performance with a low waiting time or delay.

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Thermal Modulation of a High-Bandwidth Gas Sensor Array in Real-Time for Application on a Mobile Robot

Proceedings ◽

10.3390/proceedings2130858 ◽

2018 ◽

Vol 2 (13) ◽

pp. 858 ◽

Cited By ~ 1

Author(s):

Timothy A. Vincent ◽

Yuxin Xing ◽

Marina Cole ◽

Julian W. Gardner

Keyword(s):

Signal Processing ◽

Mobile Robot ◽

Real Time ◽

Low Cost ◽

Processing Technique ◽

Signal Processing Technique ◽

Baseline Drift ◽

Gas Sensor Array ◽

Sensor Module ◽

High Bandwidth

A new signal processing technique has been developed for resistive metal oxide (MOX) gas sensors to enable high-bandwidth measurements and enhanced selectivity at PPM levels (<50 PPM VOCs). An embedded micro-heater is thermally pulsed from 225 to 350 °C, which enables the chemical reactions in the sensor film (e.g., SnO2, WO3, NiO) to be extracted using a fast Fourier transform. Signal processing is performed in real-time using a low-cost microcontroller integrated into a sensor module. The approach enables the remove of baseline drift and is resilient to environmental temperature changes. Bench-top experimental results are presented for 50 to 200 ppm of ethanol and CO, which demonstrate our sensor system can be used within a mobile robot.

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Design and Implementation of a Fuzzy Path Optimization System for Omnidirectional Autonomous Mobile Robot Control in Real-Time

Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications - Studies in Computational Intelligence ◽

10.1007/978-3-319-71008-2_19 ◽

2018 ◽

pp. 241-252 ◽

Cited By ~ 2

Author(s):

Felizardo Cuevas ◽

Oscar Castillo

Keyword(s):

Mobile Robot ◽

Real Time ◽

Robot Control ◽

Path Optimization ◽

Autonomous Mobile Robot ◽

Optimization System ◽

Design And Implementation ◽

Mobile Robot Control

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Navigation of a wheeled mobile robotic agent using modified grey wolf optimization controller

International Journal of Intelligent Unmanned Systems ◽

10.1108/ijius-06-2020-0023 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Chittaranjan Paital ◽

Saroj Kumar ◽

Manoj Kumar Muni ◽

Dayal R. Parhi ◽

Prasant Ranjan Dhal

Keyword(s):

Path Planning ◽

Mobile Robot ◽

Mobile Robots ◽

Real Time ◽

Autonomous Navigation ◽

Simulation Software ◽

Grey Wolf ◽

Grey Wolf Optimization ◽

Content Type ◽

Cluttered Environment

PurposeSmooth and autonomous navigation of mobile robot in a cluttered environment is the main purpose of proposed technique. That includes localization and path planning of mobile robot. These are important aspects of the mobile robot during autonomous navigation in any workspace. Navigation of mobile robots includes reaching the target from the start point by avoiding obstacles in a static or dynamic environment. Several techniques have already been proposed by the researchers concerning navigational problems of the mobile robot still no one confirms the navigating path is optimal.Design/methodology/approachTherefore, the modified grey wolf optimization (GWO) controller is designed for autonomous navigation, which is one of the intelligent techniques for autonomous navigation of wheeled mobile robot (WMR). GWO is a nature-inspired algorithm, which mainly mimics the social hierarchy and hunting behavior of wolf in nature. It is modified to define the optimal positions and better control over the robot. The motion from the source to target in the highly cluttered environment by negotiating obstacles. The controller is authenticated by the approach of V-REP simulation software platform coupled with real-time experiment in the laboratory by using Khepera-III robot.FindingsDuring experiments, it is observed that the proposed technique is much efficient in motion control and path planning as the robot reaches its target position without any collision during its movement. Further the simulation through V-REP and real-time experimental results are recorded and compared against each corresponding results, and it can be seen that the results have good agreement as the deviation in the results is approximately 5% which is an acceptable range of deviation in motion planning. Both the results such as path length and time taken to reach the target is recorded and shown in respective tables.Originality/valueAfter literature survey, it may be said that most of the approach is implemented on either mathematical convergence or in mobile robot, but real-time experimental authentication is not obtained. With a lack of clear evidence regarding use of MGWO (modified grey wolf optimization) controller for navigation of mobile robots in both the environment, such as in simulation platform and real-time experimental platforms, this work would serve as a guiding link for use of similar approaches in other forms of robots.

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Adaptive ground segmentation method for real-time mobile robot control

International Journal of Advanced Robotic Systems ◽

10.1177/1729881417748135 ◽

2017 ◽

Vol 14 (6) ◽

pp. 172988141774813 ◽

Cited By ~ 2

Author(s):

Hoang Vu ◽

Hieu Trong Nguyen ◽

Phuong Minh Chu ◽

Weiqiang Zhang ◽

Seoungjae Cho ◽

...

Keyword(s):

Mobile Robot ◽

Real Time ◽

Robot Control ◽

Segmentation Method ◽

Ground Segmentation ◽

Mobile Robot Control

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1A1-D21 Real time mobile robot control with a multiresolution map representation

The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) ◽

10.1299/jsmermd.2006._1a1-d21_1 ◽

2006 ◽

Vol 2006 (0) ◽

pp. _1A1-D21_1-_1A1-D21_4

Author(s):

Katsuya Iwata ◽

Shinkichi Inagaki ◽

Yusuke Nara ◽

Tatsuya Suzuki

Keyword(s):

Mobile Robot ◽

Real Time ◽

Robot Control ◽

Mobile Robot Control

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Real-Time Neural Network Based Camera Localization and its Extension to Mobile Robot Control

International Journal of Neural Systems ◽

10.1142/s012906579700029x ◽

1997 ◽

Vol 08 (03) ◽

pp. 279-293 ◽

Cited By ~ 3

Author(s):

Doo-Hyun Choi ◽

Se-Young Oh

Keyword(s):

Neural Network ◽

Neural Networks ◽

Mobile Robot ◽

Real Time ◽

Robot Control ◽

Imaging System ◽

System Modeling ◽

Hybrid Approach ◽

Camera Localization ◽

Mobile Robot Control

The feasibility of using neural networks for camera localization and mobile robot control is investigated here. This approach has the advantages of eliminating the laborious and error-prone process of imaging system modeling and calibration procedures. Basically, two different approaches of using neural networks are introduced of which one is a hybrid approach combining neural networks and the pinhole-based analytic solution while the other is purely neural network based. These techniques have been tested and compared through both simulation and real-time experiments and are shown to yield more precise localization than analytic approaches. Furthermore, this neural localization method is also shown to be directly applicable to the navigation control of an experimental mobile robot along the hallway purely guided by a dark wall strip. It also facilitates multi-sensor fusion through the use of multiple sensors of different types for control due to the network's capability of learning without models.

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