Localization System for Autonomous Mobile Robots Using Machine Learning Methods and Omnidirectional Sonar

Almeida, Jefferson S.; Marinho, Leandro Balby; Souza, João Wellington M.; Assis, Erika A; Gupta, Deepak

doi:10.1109/tla.2018.8327388

Cited by 19 publications

(7 citation statements)

References 12 publications

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“…The section analyses the state of the art of sensors used in mobile robots for terrain identification and classifications. Di Feng et al [19] [20] uses an unidirectional sonar for localization. The authors compared machine learning methods with the Bayesian method and found that the Optimum Forest method (OPF) has better performance than the conventional approach.…”

Section: Robot Perception Methods and Latest Developmentsmentioning

confidence: 99%

Recent developments in terrain identification, classification, parameter estimation for the navigation of autonomous robots

Nampoothiri

Sunny

et al. 2021

SN Appl. Sci.

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The work presents a review on ongoing researches in terrain-related challenges influencing the navigation of Autonomous Robots, specifically Unmanned Ground ones. The paper aims to highlight the recent developments in robot design and advanced computing techniques in terrain identification, classification, parameter estimation, and developing modern control strategies. The objective of our research is to familiarize the gaps and opportunities of the aforementioned areas to the researchers who are passionate to take up research in the field of autonomous robots. The paper brings recent works related to terrain strategies under a single platform focusing on the advancements in planetary rovers, rescue robots, military robots, agricultural robots, etc. Finally, this paper provides a comprehensive analysis of the related works which can bridge the AI techniques and advanced control strategies to improve navigation. The study focuses on various Deep Learning techniques and Fuzzy Logic Systems in detail. The work can be extended to develop new control schemes to improve multiple terrain navigation performance.

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Section: Robot Perception Methods and Latest Developmentsmentioning

confidence: 99%

Recent developments in terrain identification, classification, parameter estimation for the navigation of autonomous robots

Nampoothiri

Sunny

et al. 2021

SN Appl. Sci.

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“…Hence, the proposed scheme is liable to be faster than Zhang et al 24 Figure 11(a) suggests that the proposed method follows the shortest path as compared to Li et al 33 Learning-based approaches are presented to localize and navigate a mobile robot in refs. [25][26][27][28][29][30]. In contrast to these work, this paper provides a unique solution to achieve both estimation (all three body-to-camera parameters) and robot control in a single loop.…”

Section: Comparisonmentioning

confidence: 99%

“…Machine learning-based approaches for mobile robot localization are presented in refs. [25][26][27][28][29][30]. Marinho et al 26 present an approach to localize the mobile robot via a classifier with reject option.…”

Section: Introductionmentioning

confidence: 99%

Position-Based Visual Servoing of a Mobile Robot with an Automatic Extrinsic Calibration Scheme

Sharma¹,

Shukla²,

Behera³

et al. 2019

Robotica

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SUMMARYIn this paper, we present and implement a novel approach for position-based visual servoing. The challenge of controlling the mobile robot while simultaneously estimating the camera to mobile robot transformation is solved. This is achieved using gradient descent (GD)-based estimation and the sliding-mode approach. The GD approach allows online parameter estimation for controlling the robot to achieve a desired position and orientation. The adaptive nature of the parameters demonstrates the robustness of the system. In contrast to existing work, the proposed technique achieves both estimation and control tasks in a single experiment. Simulation and experimental results are provided to validate the performance of the proposed scheme.

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“…This robot moves by differential speed. Details of the sonar calibration procedure is described by [8], in which static objects were used in the environment, and was performed one hundred measurements at each preset fixed point.…”

Section: A Mobile Robotmentioning

confidence: 99%

A New Strategy for Mobile Robots Localization based on Omnidirectional Sonar Images and Machine Learning

Filho¹,

Silva²,

Ohata³

et al. 2019

Anais Estendidos Do XXXII Conference on Graphics, Patterns and Images (SIBRAPI Estendido 2019)

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In later years, research in mobile robotic areas have been experiencing a growth in interest due to its vast application area. In an unknown environment, the robot's location and movement are essential for its operation. In addition, machine learning techniques, along with signal or image processing, have been applied to map the environment, locate and move the mobile robot. This article proposes a low cost and efficient approach for mobile robot localization. It uses a omnidirectional sonar with machine learning and image processing. The feature extractors used in this paper were: Structural Co-occurrence Matrix (SCM), Statistical Moments, Central Moments, Hu Moments and Gray Level Co-occurrence Matrix (GLCM). The classifiers used in this study were: Bayes classifier, k-Nearest Neighbors (kNN), Multilayer Perceptron (MLP), Optimum Path Forest (OPF) and Support Vector Machines (SVM). The results showed that the best accuracy was achieved with Central Moments as feature extractor and OPF as classifier, achieving 96.61% and with a test time of 100us.

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Localization System for Autonomous Mobile Robots Using Machine Learning Methods and Omnidirectional Sonar

Cited by 19 publications

References 12 publications

Recent developments in terrain identification, classification, parameter estimation for the navigation of autonomous robots

Recent developments in terrain identification, classification, parameter estimation for the navigation of autonomous robots

Position-Based Visual Servoing of a Mobile Robot with an Automatic Extrinsic Calibration Scheme

A New Strategy for Mobile Robots Localization based on Omnidirectional Sonar Images and Machine Learning

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