Path finding for a NAO humanoid robot by fusing visual and proximity sensors

Mao, Xiaoqian; He, Huidong; Li, Wei

doi:10.1109/wcica.2016.7578365

Cited by 3 publications

(4 citation statements)

References 14 publications

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“…Once an object of interest is identified, the NAO is able to find a path to approach the object and to conduct the operation [34]. Nevertheless, the current IFCE algorithm aims at extracting an object with “single” similar color.…”

Section: Discussionmentioning

confidence: 99%

“…Due to the robustness and precision of interesting-object extraction, the exact color and shape information can be revealed vividly, which provides an effective means for automatically identifying an object of interest by matching a property with the object via P300 brain signals. Once an object of interest is identified, the NAO is able to find a path to approach the object and to conduct the operation [ 34 ]. Nevertheless, the current IFCE algorithm aims at extracting an object with “single” similar color.…”

Section: Discussionmentioning

confidence: 99%

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Object Extraction in Cluttered Environments via a P300-Based IFCE

Mao

et al. 2017

Computational Intelligence and Neuroscience

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One of the fundamental issues for robot navigation is to extract an object of interest from an image. The biggest challenges for extracting objects of interest are how to use a machine to model the objects in which a human is interested and extract them quickly and reliably under varying illumination conditions. This article develops a novel method for segmenting an object of interest in a cluttered environment by combining a P300-based brain computer interface (BCI) and an improved fuzzy color extractor (IFCE). The induced P300 potential identifies the corresponding region of interest and obtains the target of interest for the IFCE. The classification results not only represent the human mind but also deliver the associated seed pixel and fuzzy parameters to extract the specific objects in which the human is interested. Then, the IFCE is used to extract the corresponding objects. The results show that the IFCE delivers better performance than the BP network or the traditional FCE. The use of a P300-based IFCE provides a reliable solution for assisting a computer in identifying an object of interest within images taken under varying illumination intensities.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Object Extraction in Cluttered Environments via a P300-Based IFCE

Mao

et al. 2017

Computational Intelligence and Neuroscience

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“…Nao features an inertial measurement unit with accelerometer, gyrometer, and four ultrasonic sensors and force sensors. 54 There are many other humanoid robots in academic institutions worldwide (REEM-C, OceanOne, Titan, QRIO, etc.). Online learning, autonomous motion adaptation, grasp planning, exoskeletons, affective human-robot interaction, and speech communication are the latest research lines and challenges in humanoid robots.…”

Section: Locomotionmentioning

confidence: 99%

“…Nao features an inertial measurement unit with accelerometer, gyrometer, and four ultrasonic sensors and force sensors. 54…”

Section: Locomotionmentioning

confidence: 99%

A review of mobile robots: Concepts, methods, theoretical framework, and applications

Rubio

Chuliá

Llopis-Albert

2019

International Journal of Advanced Robotic Systems

507

178

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Humanoid robots, unmanned rovers, entertainment pets, drones, and so on are great examples of mobile robots. They can be distinguished from other robots by their ability to move autonomously, with enough intelligence to react and make decisions based on the perception they receive from the environment. Mobile robots must have some source of input data, some way of decoding that input, and a way of taking actions (including its own motion) to respond to a changing world. The need to sense and adapt to an unknown environment requires a powerful cognition system. Nowadays, there are mobile robots that can walk, run, jump, and so on like their biological counterparts. Several fields of robotics have arisen, such as wheeled mobile robots, legged robots, flying robots, robot vision, artificial intelligence, and so on, which involve different technological areas such as mechanics, electronics, and computer science. In this article, the world of mobile robots is explored including the new trends. These new trends are led by artificial intelligence, autonomous driving, network communication, cooperative work, nanorobotics, friendly human-robot interfaces, safe human-robot interaction, and emotion expression and perception. Furthermore, these news trends are applied to different fields such as medicine, health care, sports, ergonomics, industry, distribution of goods, and service robotics. These tendencies will keep going their evolution in the coming years.

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