New algorithm for behaviour-based mobile robot navigation in cluttered environment using neural network architecture

Purpose This study concerns an on-line path planning technique for a behaviour-based wheeled mobile robot local navigation in an unknown environment with hurdles, using the feedforward back-propagation neural network sensor-actuator control technique. The purpose of this study is to find the non-collision path for the mobile robot moving towards the goal in a cluttered environment. Design/methodology/approach Neural network architecture input layers are the different hurdle distance information, which are acquired by an array of equipped sensors, and the output layer is the turning angle (motor control). In this way, the mobile robot is effectively being trained to move autonomously in the environment. Findings Computer simulation and real-time experimental results show that the proposed neural network controller can improve navigation performance in cluttered and unknown environments. Originality/value The proposed neural network controller gives better results (in terms of path length) as compared to previously developed models, which verifies the effectiveness of the proposed architecture.

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Bioinspired control design using cerebellar model articulation controller network for omnidirectional mobile robots

Advances in Mechanical Engineering ◽

10.1177/1687814018794349 ◽

2018 ◽

Vol 10 (8) ◽

pp. 168781401879434 ◽

Cited By ~ 5

Author(s):

Yiming Jiang ◽

Chenguang Yang ◽

Min Wang ◽

Ning Wang ◽

Xiaofeng Liu

Keyword(s):

Neural Network ◽

Mobile Robot ◽

Complex Dynamics ◽

Learning Ability ◽

Control Technique ◽

Robot Dynamics ◽

Cerebellar Model Articulation Controller ◽

Omnidirectional Mobile Robot ◽

Neural Network Controller ◽

Network Controller

As a learning mechanism that emulates the structure of the cerebellum, cerebellar model articulation controllers have been widely adopted in the control of robotic systems because of the fast learning ability and simple computational structure. In this article, a cerebellar model articulation controller–based neural network controller is developed for an omnidirectional mobile robot. With the powerful learning ability of cerebellar model articulation controller, a cerebellar model articulation controller neural network is constructed to learn the complex dynamics of the omnidirectional mobile robot such that the robot is controlled without a priori knowledge of the robot dynamics. In addition, to overcome the limitation of the neural network controller, a global control technique with a group of smooth switching functions is designed such that the global ultimately uniformly boundedness of cerebellar model articulation controller is achieved instead of conventional semi-global ultimately uniformly boundedness. Moreover, smooth decreasing boundary functions are synthesized into the controller to guarantee the transient control performance. Based on an omnidirectional mobile robot, numerical experiments have been conducted to demonstrate the effectiveness of the proposed cerebellar model articulation controller controller.

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A data-driven neural network architecture for sentiment analysis

Data Technologies and Applications ◽

10.1108/dta-03-2018-0017 ◽

2019 ◽

Vol 53 (1) ◽

pp. 2-19 ◽

Cited By ~ 1

Author(s):

Erion Çano ◽

Maurizio Morisio

Keyword(s):

Neural Network ◽

Sentiment Analysis ◽

Network Architecture ◽

Network Models ◽

Data Sets ◽

Feature Maps ◽

Neural Network Architecture ◽

Neural Network Models ◽

Content Type ◽

Max Pooling

Purpose The fabulous results of convolution neural networks in image-related tasks attracted attention of text mining, sentiment analysis and other text analysis researchers. It is, however, difficult to find enough data for feeding such networks, optimize their parameters, and make the right design choices when constructing network architectures. The purpose of this paper is to present the creation steps of two big data sets of song emotions. The authors also explore usage of convolution and max-pooling neural layers on song lyrics, product and movie review text data sets. Three variants of a simple and flexible neural network architecture are also compared. Design/methodology/approach The intention was to spot any important patterns that can serve as guidelines for parameter optimization of similar models. The authors also wanted to identify architecture design choices which lead to high performing sentiment analysis models. To this end, the authors conducted a series of experiments with neural architectures of various configurations. Findings The results indicate that parallel convolutions of filter lengths up to 3 are usually enough for capturing relevant text features. Also, max-pooling region size should be adapted to the length of text documents for producing the best feature maps. Originality/value Top results the authors got are obtained with feature maps of lengths 6–18. An improvement on future neural network models for sentiment analysis could be generating sentiment polarity prediction of documents using aggregation of predictions on smaller excerpt of the entire text.

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