Mechatronic design of NAO humanoid

Gouaillier, David; Hugel, Vincent; Blazevic, Pierre; Kilner, Chris; Monceaux, Jérôme; Lafourcade, Pascal; Marnier, Brice; Serre, Julien; Maisonnier, Bruno

doi:10.1109/robot.2009.5152516

Cited by 443 publications

(233 citation statements)

References 18 publications

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“…In this paper, we propose a fast online background subtraction algorithm with contiguous foreground prior for background subtraction, and we implement the proposed algorithm in a Nao humanoid robot [24,25] based moni-105 toring system to examine its performance in real applications 1 . The algorithm includes a background model and a foreground model.…”

Section: Introductionmentioning

confidence: 99%

“…Nao [24,25] is the product of Aldebaran Robotics -SAS (Limited Company). It has a medium size with a height of 57.3cm, a width of 27.3cm and a weight less than 4.3kg.…”

Section: Introduction Of Nao Humanoid Robotmentioning

confidence: 99%

“…In this subsection, we introduce some key modules of Nao related to our implementation. We refer the readers with interest to [24,25].…”

Section: Introduction Of Nao Humanoid Robotmentioning

confidence: 99%

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An online background subtraction algorithm deployed on a NAO humanoid robot based monitoring system

Sirlantzis

Howells

et al. 2016

Robotics and Autonomous Systems

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Please cite this article as: Y. Hu, et al., An online background subtraction algorithm deployed on a NAO humanoid robot based monitoring system, Robotics and Autonomous Systems (2016), http://dx.doi.org/10. 1016/j.robot.2016.08.013 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.An online background subtraction algorithm deployed on a NAO humanoid robot based monitoring system AbstractIn this paper, we design a fast background subtraction algorithm and deploy this algorithm on a monitoring system based on NAO humanoid robot. The proposed algorithm detects a contiguous foreground via a contiguously weighted linear regression (CWLR) model. It consists of a background model and a foreground model. The background model is a regression based low rank model. It seeks a low rank background subspace and represents the background as the linear combination of the basis spanning the subspace. The foreground model promotes the contiguity in the foreground detection. It encourages the foreground to be detected as whole regions rather than separated pixels. We formulate the background and foreground model into a contiguously weighted linear regression problem. This problem can be solved efficiently via an alternating optimization approach which includes continuous and discrete variables. Given an image sequence, we use the first few frames to incrementally initialize the background subspace, and we determine the background and foreground in the following frames in an online scheme using the proposed CWLR model, with the background subspace continuously updated using the detected background information. The proposed algorithm is implemented by Python on a NAO humanoid robot based monitoring system. This system consists of a control station and a Nao robot. The Nao robot acts as a mobile probe. It captures image sequence and sends it to the control station. The control station serves as a control terminal. It sends commands to control the behaviour of Nao robot, and it processes the image data sent by Nao. This system can be used for living environment monitoring and form the basis for many vision-based applications like fall detection and scene understanding. The experimental comparisons with most recent algorithms on both benchmark dataset and NAO captures demonstrate the high effectiveness of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%

“…Nao [24,25] is the product of Aldebaran Robotics -SAS (Limited Company). It has a medium size with a height of 57.3cm, a width of 27.3cm and a weight less than 4.3kg.…”

Section: Introduction Of Nao Humanoid Robotmentioning

confidence: 99%

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An online background subtraction algorithm deployed on a NAO humanoid robot based monitoring system

Sirlantzis

Howells

et al. 2016

Robotics and Autonomous Systems

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“…The NAO humanoid robot [1] is widely used in the robotics community to design omnidirectional walking gaits [2,3], kicking moves [4], and stabilizers [5,6]. In the NAO robot the yaw joints at the hips are coupled and 45 [deg] inclined with respect to the vertical [1] (Fig.…”

Section: Introductionmentioning

confidence: 99%

“…In the NAO robot the yaw joints at the hips are coupled and 45 [deg] inclined with respect to the vertical [1] (Fig. 1).…”

Section: Introductionmentioning

confidence: 99%

Analytical Solution for Joint Coupling in NAO Humanoid Hips

Hugel

Jouandeau

2015

RoboCup 2014: Robot World Cup XVIII

Self Cite

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Abstract. Usually the legs of humanoids capable of omnidirectional walking are not underactuated. In other words each one of the six degrees of freedom of the torso can be commanded independently from the leg joint angles. However the NAO humanoid robot has a coupled joint at the hips, which makes 11 degrees of freedom instead of 12 for the locomotor apparatus. As a consequence the trunk of the robot has only 5 independent degrees of freedom when the positions of both feet are xed, and each leg cannot be commanded independently to execute walking steps. Up to now only bypass solutions have been proposed, where the coupled joint angles are not calculated exactly. This paper describes an analytical solution to determine the exact angle to be applied to the coupled joint. The method uses the positions of both foot ankles in the trunk reference frame and the angle between footprints as inputs, and calculates the yaw angle of the trunk. The solution was demonstrated in a dynamics simulator using the NAO model.

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Identifying safety issues from energy conservation requirements

Madala¹,

Tenbergen

2022

J Software Evolu Process

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In cyber-physical systems such as robots and automated vehicles that rely heavily on batteries, safety, and energy conservation can result conflicting requirements when not considered together. In systems engineering, the development begins at a concept phase where we have high-level information of different components of the system. During the concept phase, we perform hazard analysis and risk assessment, define safety goals, and derive safety requirements. This means requirement engineering occurs at the end of the concept phase. However, energy conservation recommendations are not taken into consideration until the detailed design with specific hardware and software is known. Hence, it is possible to recommend energy conservation behaviors that can compromise system's safety. If we perform a trade-off analysis between safety and energy conservation at a concept phase, we can propose various design alternatives and choose the best one that offers a safe and energy saving architecture. To achieve this goal, we propose an approach for identifying safety issues that can be caused by energy conservation recommendations. To evaluate the effectiveness of our approach, we performed an empirical study on four robotic systems. Our results show that we can find energy conservation recommendations that can compromise safety at a concept phase.

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Mechatronic design of NAO humanoid

Cited by 443 publications

References 18 publications

An online background subtraction algorithm deployed on a NAO humanoid robot based monitoring system

An online background subtraction algorithm deployed on a NAO humanoid robot based monitoring system

Analytical Solution for Joint Coupling in NAO Humanoid Hips

Identifying safety issues from energy conservation requirements

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