Fuzzy-logic-assisted interacting multiple model (FLAIMM) for mobile robot localization

Lee, Hyoung-Ki; Jung, Jongdae; Choi, Kiwan; Park, Jiyoung; Myung, Hyun

doi:10.1016/j.robot.2012.09.018

Cited by 29 publications

(12 citation statements)

References 20 publications

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“…It also reduces the complexity and thus improves performance. A fuzzy-logic-assisted interacting multiple model (FLAIMM) technique is introduced in [3]. Authors designed two extended Kalman filters (EKF) to solve the problem of the dynamics of mobile robots whereas an adaptive neurofuzzy inference system (ANFIS) was used for predicting the slip.…”

Section: Related Workmentioning

confidence: 99%

Development of efficient obstacle avoidance and line following mobile robot with the integration of fuzzy logic system in static and dynamic environments

Almasri

Elleithy

Alajlan

2016

2016 IEEE Long Island Systems, Applications and Technology Conference (LISAT)

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Section: Related Workmentioning

confidence: 99%

Development of efficient obstacle avoidance and line following mobile robot with the integration of fuzzy logic system in static and dynamic environments

Almasri

Elleithy

Alajlan

2016

2016 IEEE Long Island Systems, Applications and Technology Conference (LISAT)

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“…Because odometric error diverges, it cannot in practice be used for long-term localization. However, proprioceptive sensors have the advantages of low cost, low energy consumption, high updating rate, and insensibility to environmental changes, which shows that odometry can be an irreplaceable localization method in scenarios where the performance of exteroceptive sensors is seriously affected by the working environment [9][10][11]. Different from relative localization, absolute localization means the ability to directly determine the robot's location with respect to a given frame of reference by using exteroceptive sensors, and therefore, it suffers no cumulative errors.…”

Section: Introductionmentioning

confidence: 99%

FVO: floor vision aided odometry

Kang

Qin

2018

Sci. China Inf. Sci.

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In many indoor scenarios, such as restaurants, laboratories, and supermarkets, the planar floors are covered with rectangular tiles. We realized that the abundant parallel lines and crossing points formed by tile joints can be used as natural features to assist indoor localization, and thus we propose a novel indoor localization method for mobile robots by fusing odometry and monocular vision. The method comprises three steps. First, the heading and location of the mobile robot are approximately estimated by odometry based on incremental encoders. Second, with the aid of a camera, the lens of which points vertically toward the floor, the odometric heading estimation can be corrected by detecting the relative angle between the robot's heading and the tile joints. Third, the odometric location estimation is corrected by detecting the perpendicular distance between the image center and the tile joints. As compared with the existing indoor localization methods, the proposed method, called floor vision aided odometry, is not only relatively low in economic cost and computational complexity, but also relatively high in accuracy and robustness. The effectiveness of this method is verified by a real-world experiment based on a differential-drive wheeled mobile robot.

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“…However, when the robot is kidnapped (i.e., the robot is moved by an intentional or unintentional user) or suffers from locomotion failure (due to large slip and falling), the robot will inevitably lose its current position [6], [7]. In this case, immediate recovery of the robot position is essential for seamless operation.…”

Section: Introductionmentioning

confidence: 99%

“…In this case, immediate recovery of the robot position is essential for seamless operation. Several works have been carried out to solve related problems either by fault diagnosis using adaptive filtering methods [8], [9] or structural adaptation using multiple models [6], [10]- [12]. However, the adaptation approaches cannot guarantee error convergence in severe conditions and in that case, reinitialization of the robot state is required.…”

Section: Introductionmentioning

confidence: 99%

Indoor Mobile Robot Localization and Mapping Based on Ambient Magnetic Fields and Aiding Radio Sources

Jung

Lee

Myung

2015

IEEE Trans. Instrum. Meas.

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In robotics, the problem of concurrently addressing the localization and mapping is well defined as simultaneous localization and mapping (SLAM) problem. Since the SLAM procedure is usually recursive, maintaining a certain error bound on the current position estimate is a critical issue. However, when the robot is kidnapped (i.e., the robot is moved by an intentional or unintentional user) or suffers from locomotion failure (due to large slip and falling), the robot will inevitably lose its current position. In this case, immediate recovery of the robot position is essential for seamless operation. In this paper, we present a method of solving both SLAM and relocation problems by employing ambient magnetic and radio measurements. The proposed SLAM is realized in the Rao-Blackwellized particle filter-and grid-based SLAM frameworks, where we exploit the local heading corrections from the magnetic measurements. For the relocation, we design the location signatures using the magnetic and radio measurements, and examine each of the Monte Carlo localization-based and multilayer perceptron-based relocation methods with real-world data. We implement the proposed SLAM and relocation algorithms in an embedded system and verify the feasibility of the proposed methods as an online robot navigation system.

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Fuzzy-logic-assisted interacting multiple model (FLAIMM) for mobile robot localization

Cited by 29 publications

References 20 publications

Development of efficient obstacle avoidance and line following mobile robot with the integration of fuzzy logic system in static and dynamic environments

Development of efficient obstacle avoidance and line following mobile robot with the integration of fuzzy logic system in static and dynamic environments

FVO: floor vision aided odometry

Indoor Mobile Robot Localization and Mapping Based on Ambient Magnetic Fields and Aiding Radio Sources

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