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

Filho, Pedro Pedrosa Rebouças; Silva, Suane Pires Pinheiro da; Ohata, Elene Firmeza; Almeida, Jefferson S.; Sousa, Pedro Henrique Feijó de; Nascimento, Navar Medeiros M. e; Silva, Francisco Hercules dos Santos

doi:10.5753/sibgrapi.est.2019.8321

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…Many authors claim that they achieve very satisfactory results applying ML methods in mobile robot problems. For example, in the work of [13], the best results for mobile robots in machine learning were obtained using Central Moments as a feature extractor and Optimum Path Forest as a classifier with an accuracy of 96.61%. The authors of [9] stated that the developed algorithm for autonomous mobile robots in industrial areas enabled the execution of work orders with a 100% accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Machine Learning Approaches for Mobile Robot Control

Rybczak,

Popowniak,

Lazarowska

2024

Robotics

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Machine learning (ML) is a branch of artificial intelligence that has been developing at a dynamic pace in recent years. ML is also linked with Big Data, which are huge datasets that need special tools and approaches to process them. ML algorithms make use of data to learn how to perform specific tasks or make appropriate decisions. This paper presents a comprehensive survey of recent ML approaches that have been applied to the task of mobile robot control, and they are divided into the following: supervised learning, unsupervised learning, and reinforcement learning. The distinction of ML methods applied to wheeled mobile robots and to walking robots is also presented in the paper. The strengths and weaknesses of the compared methods are formulated, and future prospects are proposed. The results of the carried out literature review enable one to state the ML methods that have been applied to different tasks, such as the following: position estimation, environment mapping, SLAM, terrain classification, obstacle avoidance, path following, learning to walk, and multirobot coordination. The survey allowed us to associate the most commonly used ML algorithms with mobile robotic tasks. There still exist many open questions and challenges such as the following: complex ML algorithms and limited computational resources on board a mobile robot; decision making and motion control in real time; the adaptability of the algorithms to changing environments; the acquisition of large volumes of valuable data; and the assurance of safety and reliability of a robot’s operation. The development of ML algorithms for nature-inspired walking robots also seems to be a challenging research issue as there exists a very limited amount of such solutions in the recent literature.

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Section: Introductionmentioning

confidence: 99%

A Survey of Machine Learning Approaches for Mobile Robot Control

Rybczak,

Popowniak,

Lazarowska

2024

Robotics

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Environment modeling and localization from datasets of omnidirectional scenes using machine learning techniques

Cebollada

Payá

Peidró

et al. 2023

Neural Comput & Applic

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This work presents a framework to create a visual model of the environment which can be used to estimate the position of a mobile robot by means of artificial intelligence techniques. The proposed framework retrieves the structure of the environment from a dataset composed of omnidirectional images captured along it. These images are described by means of global-appearance approaches. The information is arranged in two layers, with different levels of granularity. The first layer is obtained by means of classifiers and the second layer is composed of a set of data fitting neural networks. Subsequently, the model is used to estimate the position of the robot, in a hierarchical fashion, by comparing the image captured from the unknown position with the information in the model. Throughout this work, five classifiers are evaluated (Naïve Bayes, SVM, random forest, linear discriminant classifier and a classifier based on a shallow neural network) along with three different global-appearance descriptors (HOG, gist, and a descriptor calculated from an intermediate layer of a pre-trained CNN). The experiments have been tackled with some publicly available datasets of omnidirectional images captured indoors with the presence of dynamic changes. Several parameters are used to assess the efficiency of the proposal: the ability of the algorithm to estimate coarsely the position (hit ratio), the average error (cm) and the necessary computing time. The results prove the efficiency of the framework to model the environment and localize the robot from the knowledge extracted from a set of omnidirectional images with the proposed artificial intelligence techniques.

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

Cited by 2 publications

References 7 publications

A Survey of Machine Learning Approaches for Mobile Robot Control

A Survey of Machine Learning Approaches for Mobile Robot Control

Environment modeling and localization from datasets of omnidirectional scenes using machine learning techniques

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