Low-Cost Reduced Navigation System for Mobile Robot in Indoor/Outdoor Environments

This paper presents a low-cost based approach for solving the navigation problem of wheeled mobile robots to perform required tasks within indoor and outdoor environments. The presented solution is based on probabilistic approaches for multiple sensor fusion utilizing low-cost visual/inertial sensor...

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Bibliographic Details
Published inIEEE access Vol. 8; pp. 25014 - 25026
Main Authors Al Khatib, Ehab I., Jaradat, Mohammad Abdel Kareem, Abdel-Hafez, Mamoun F.
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Coders
Extended Kalman filter
Feedback linearization
Global Positioning System
Global positioning systems
GPS
Hybrid systems
Indoor environments
Inertial sensing devices
kinect depth sensor
Low cost
low-cost navigation
mobile robot
Mobile robots
Navigation systems
Robot control
Robot kinematics
Robot sensing systems
Robots
sensor fusion
Sensors
State feedback
Wheels
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Summary:This paper presents a low-cost based approach for solving the navigation problem of wheeled mobile robots to perform required tasks within indoor and outdoor environments. The presented solution is based on probabilistic approaches for multiple sensor fusion utilizing low-cost visual/inertial sensors. For the outdoor environment, the Extended Kalman Filter (EKF) is used to estimate the robot position and orientation, the system consists of wheel encoders, a reduced inertial sensor system (RISS), and a Global Positioning System (GPS). For the indoor environment, where GPS signals are blocked, another EKF algorithm is proposed using low cost depth sensor (Microsoft Kinect stream). EKF indoor localization is based on landmarks extracted from the depth measurements. A hybrid low-cost reduced navigation system (HLRNS) for indoor and outdoor environments is proposed and validated in both simulation and experimental environments. Additionally, an input-output state feedback linearization (I-O SFL) technique is used to control the robot to track the desired trajectory in such an environment. According to the conducted validation simulation and experimental testing, the proposed HLRNS provides an acceptable performance to be deployed for real-time applications.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2971169