Adaptive finite state machine based visual autonomous navigation system

Sales, Daniel Oliva; Correa, D.O.; Fernandes, Leandro C.; Wolf, Denis F.; Osório, Fernando Santos

doi:10.1016/j.engappai.2013.12.006

Cited by 33 publications

(19 citation statements)

References 29 publications

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“…The first direction is automation (from the automation of certain operations to the introduction of automated control of technological processes). In particular, Petri nets [10], the methods of finite automata [11][12][13] and the assertion mechanisms are used for this purpose. The second direction is associated with the use of the methods of artificial intelligence and paying more attention not to simulating a control object, but rather to formalizing the driving activity of a person-operator [14][15][16].…”

Section: A G O R O B C H E N K Omentioning

confidence: 99%

Improving the process of driving a locomotive through the use of decision support systems

Tartakovskyi

Gorobchenko

Antonovych

2016

EEJET

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Розроблено структуру та архітектурну ієрархію локомотивних СППР. Алгоритм дій машиніста локомотива представлено нечітким імовірнісним графом. Вагові коефіцієнти переходів між вершинами завдані нечіткими числами, значення яких записані в матриці графа. Вибір варіанта рішення локомотивної СППР реалізовано за адитивним критерієм. Опис динамічної бази знань проведено з використанням продукційної моделі представлення знань Ключові слова: керування локомотивом, прийняття рішень, інтелектуальна система, база знань, нечіткий класифікатор Разработана структура и архитектурная иерархия локомотивных СППР. Алгоритм действий машиниста локомотива представлен нечетким вероятностным графом. Весовые коэффициенты переходов между вершинами заданы нечеткими числами, значения которых записаны в матрице графа. Выбор варианта решения локомотивной СППР реализован по аддитивному критерию. Описание динамической базы знаний проведено с использованием продукционной модели представления знаний Ключевые слова: управление локомотивом, принятие решений, интеллектуальная система, база знаний, нечеткий классификатор

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Section: A G O R O B C H E N K Omentioning

confidence: 99%

Improving the process of driving a locomotive through the use of decision support systems

Tartakovskyi

Gorobchenko

Antonovych

2016

EEJET

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“…Finite state machine (FSM) [3,4,5] is a mathematic model describing finite state, transition behavior between all the states and all the actions of the states. FSM can be shown by a directional figure, it can be defined as:…”

Section: Introductionmentioning

confidence: 99%

A kind of crane loading and unloading simulation based on finite-state Machine

Guang

Bai²,

Yu³

2015

Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015

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Aiming at the insufficiency of the actual crane, finite state machine technology was adopted for setting up an emulational training system. The system implemented a kind of crane loading and unloading operation. Therefor a sort of finite-state machine for the crane was set up. By the dynamics analysis for the crane, several states of the finite state machine were established during loading and unloading process. All the states were bewritten with some mathematic models. The models were simplified to adapt to the simulation system. Then the flow for establishing finite state machine was given, and the concrete methods in Unity3D engine for establishing finite state machine were described. Finally an instance of a kind of crane loading and unloading training system was implemented based on the aforementioned models and methods. The instance shows that the models are accurate and the finite-state machine states are logical.

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“…The Kinect provides RGB images (video stream) and also depth images, allowing to autonomously navigating using this sensor to avoid obstacles [20]. In previous works of our research group we have developed some applications of autonomous navigation [6,20,21] and gesture recognition [1] using the Kinect. In this work we adopt the Kinect to provide RGB images for face localization and tracking, and also, to provide depth information allowing obstacle avoidance, positioning and navigation.…”

Section: Related Workmentioning

confidence: 99%

From Tele-Operated Robots to Social Robots with Autonomous Behaviors

Berri

Wolf

Osório

2015

Communications in Computer and Information Science

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Abstract. This paper presents a perception/interface device for Telepresence Mobile Robots using a Kinect sensor. Firstly, using the Kinect RGB camera (Webcam) and image processing techniques, it is possible to detect a human face, allowing the robot to track the face, getting closer of a person, moving forward and rotating to get a better pose (position and orientation) to interact with him/her. Then it is possible to recognize hand gestures using the Kinect 3D sensor (Depth camera). The proposed gesture recognition method tracks the hands positions and movements, when moving it forward towards the robot, and then recognizing a set of predefined gestures/commands. Finally, the 3D perception provided by the Kinect also allows us to detect obstacles, avoiding collisions and safely moving the mobile robot base, also allowing to search for someone in the environment. Practical experiments are presented demonstrating the obtained results: (i) in the searching and tracking of human faces; (ii) in the robot positioning related to the user we want to interact with; and also (iii) in the human gesture recognition.

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Adaptive finite state machine based visual autonomous navigation system

Cited by 33 publications

References 29 publications

Improving the process of driving a locomotive through the use of decision support systems

Improving the process of driving a locomotive through the use of decision support systems

A kind of crane loading and unloading simulation based on finite-state Machine

From Tele-Operated Robots to Social Robots with Autonomous Behaviors

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