Lower-body control of humanoid robot NAO via Kinect

Wang, Guanwen; Hu, Xiao; Shen, Jiayun

doi:10.1007/s11042-017-5332-3

Cited by 12 publications

(18 citation statements)

References 26 publications

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“…In order to introduce the function of intelligent integration into the control algorithm, we must first solve the problem of logical judgment of introducing intelligent integration. Overview of Some Intelligent Control Structures and Dedicated Algorithms DOI: http://dx.doi.org /10.5772/intechopen.91966 This condition can be determined by comparing the intelligent integration curve in Figure 21 with Figure 22 and Table 2 [16].…”

Section: Human-like Intelligent Integration Control Algorithmmentioning

confidence: 99%

Overview of Some Intelligent Control Structures and Dedicated Algorithms

Chang¹,

Chu²,

Lin³

et al. 2021

Automation and Control

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Automatic control refers to the use of a control device to make the controlled object automatically run or keep the state unchanged without the participation of people. The guiding ideology of intelligent control is based on people's way of thinking and ability to solve problems, in order to solve the current methods that require human intelligence. We already know that the complexity of the controlled object includes model uncertainty, high nonlinearity, distributed sensors/actuators, dynamic mutations, multiple time scales, complex information patterns, big data process, and strict characteristic indicators, etc. In addition, the complexity of the environment manifests itself in uncertainty and uncertainty of change. Based on this, various researches continue to suggest that the main methods of intelligent control can include expert control, fuzzy control, neural network control, hierarchical intelligent control, anthropomorphic intelligent control, integrated intelligent control, combined intelligent control, chaos control, wavelet theory, etc. However, it is difficult to want all the intelligent control methods in a chapter, so this chapter focuses on intelligent control based on fuzzy logic, intelligent control based on neural network, expert control and human-like intelligent control, and hierarchical intelligent control and learning control, and provide relevant and useful programming for readers to practice.

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Section: Human-like Intelligent Integration Control Algorithmmentioning

confidence: 99%

Overview of Some Intelligent Control Structures and Dedicated Algorithms

Chang¹,

Chu²,

Lin³

et al. 2021

Automation and Control

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show abstract

“…The observation process usually relies on either marker-based or markerless visual capture systems or dynamic capture systems to acquire human motions. In a marker-based visual capture system, markers must be placed on the demonstrators to indicate the positions of their body parts [10][11][12][13][14], whereas a markerless visual capture system can directly extract 2D or 3D information from video frames [15][16][17][18][19][20]. The imitation system presented in [21] obtained motion data using the Xsens MVN dynamic capture system (Enschede, The Netherlands), which offers high precision but is inconvenient and expensive.…”

Section: Introductionmentioning

confidence: 99%

“…To this end, several analytical methods have been exploited [13,14,19,20,[25][26][27][28][29]. In addition to the positions of the end effectors, other information such as the positions of the joints is obtained in these methods.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Whole-Body Imitation by Humanoid Robots and Task-Oriented Teleoperation Using an Analytical Mapping Method and Quantitative Evaluation

et al. 2018

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Due to the limitations on the capabilities of current robots regarding task learning and performance, imitation is an efficient social learning approach that endows a robot with the ability to transmit and reproduce human postures, actions, behaviors, etc., as a human does. Stable whole-body imitation and task-oriented teleoperation via imitation are challenging issues. In this paper, a novel comprehensive and unrestricted real-time whole-body imitation system for humanoid robots is designed and developed. To map human motions to a robot, an analytical method called geometrical analysis based on link vectors and virtual joints (GA-LVVJ) is proposed. In addition, a real-time locomotion method is employed to realize a natural mode of operation. To achieve safe mode switching, a filter strategy is proposed. Then, two quantitative vector-set-based methods of similarity evaluation focusing on the whole body and local links, called the Whole-Body-Focused (WBF) method and the Local-Link-Focused (LLF) method, respectively, are proposed and compared. Two experiments conducted to verify the effectiveness of the proposed methods and system are reported. Specifically, the first experiment validates the good stability and similarity features of our system, and the second experiment verifies the effectiveness with which complicated tasks can be executed. At last, an imitation learning mechanism in which the joint angles of demonstrators are mapped by GA-LVVJ is presented and developed to extend the proposed system.

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“…Originally, human motion was captured and optimized off-line to adapt to the robot structure and constraints [1][2][3]. Recently, real-time imitation systems [4][5][6][7][8][9][10][11][12][13] have been developed owing to the development of three-dimensional (3D) motion-tracking equipment such as Kinect [14] and Xsens MVN [15]. However, owing to the differences in the joints of the human body and robot, there is a discussion about the motion similarity during mapping motions from humans to robots [16,17].…”

Section: Introductionmentioning

confidence: 99%

“…However, owing to the differences in the joints of the human body and robot, there is a discussion about the motion similarity during mapping motions from humans to robots [16,17]. Generally, there are two definitions for motion similarity: one is the similarity between end-effector trajectories [8][9][10][11][12][13], and the other is the similarity between angular configurations [4][5][6][7]. Both definitions have been used for robot control.…”

Section: Introductionmentioning

confidence: 99%

Work chain-based inverse kinematics of robot to imitate human motion with Kinect

2018

Self Cite

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The ability to realize human-motion imitation using robots is closely related to developments in the field of artificial intelligence. However, it is not easy to imitate human motions entirely owing to the physical differences between the human body and robots. In this paper, we propose a work chain-based inverse kinematics to enable a robot to imitate the human motion of upper limbs in real time. Two work chains are built on each arm to ensure that there is motion similarity, such as the end effector trajectory and the joint-angle configuration. In addition, a twophase filter is used to remove the interference and noise, together with a self-collision avoidance scheme to maintain the stability of the robot during the imitation. Experimental results verify the effectiveness of our solution on the humanoid robot Nao-H25 in terms of accuracy and real-time performance.

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Lower-body control of humanoid robot NAO via Kinect

Cited by 12 publications

References 26 publications

Overview of Some Intelligent Control Structures and Dedicated Algorithms

Overview of Some Intelligent Control Structures and Dedicated Algorithms

Real-Time Whole-Body Imitation by Humanoid Robots and Task-Oriented Teleoperation Using an Analytical Mapping Method and Quantitative Evaluation

Work chain-based inverse kinematics of robot to imitate human motion with Kinect

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