Modeling and motion planning of the infant-size humanoid robot THBIP-II

Xia, Zeyang; Chen, Ken; He, Yeming

doi:10.1109/ichr.2007.4813929

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…The superscript denotes the layer, the subscript denotes Layer 2 (Context Layer): In the context layer, the input node and the output node are represented as u (2) …”

Section: B Rwenn For Fault Estimationmentioning

confidence: 99%

“…where u (2) k and o (2) k are the input and output of the kth node in this layer, 0 ≤ β < 1 is the self-connection feedback gain, o (4) k is the output of the hidden layer, and n is the number of both context and hidden layer neurons.…”

Section: B Rwenn For Fault Estimationmentioning

confidence: 99%

“…The control system must be analyzed theoretically in the virtual domain using less assumptions and more generalization. In particular, the uncertainties and faults occurring within and outside the system must be calculated beforehand by autonomous robotic systems, because they usually work remotely by the operator [1], [2], so that fault can be tolerated during some period of time without damaging the system and degrading the performance. The fault accommodation is important for the autonomous robotic systems since they have to complete the task in case of danger.…”

mentioning

confidence: 99%

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Fault Accommodation Control for a Biped Robot Using a Recurrent Wavelet Elman Neural Network

Lin

Boldbaatar

2017

IEEE Systems Journal

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A model-based fault accommodation control scheme that uses a recurrent wavelet Elman neural network (RWENN) is proposed to achieve satisfactory control without performance degradation for biped robot locomotion with unknown uncertainties and faults. In the fault accommodation scheme, a computed torque control is the main control that is used to track the desired trajectory when there is no fault; and a compensation control is used to eliminate the unknown model uncertainties. The proposed RWENN has an input from a context layer with self-feedback and an output recurrent layer to the hidden layer, which increases the precision and convergence time of the network compared with a recurrent neural network, a recurrent fuzzy neural network, and a recurrent wavelet neural network, so that any dynamic change, such as a fault on the system, can be estimated properly. Thus, it enhances the capability of fault accommodation. The adaptive laws of the RWENN-based fault accommodation control are derived from the Lyapunov theorem; hence, the stability of the system can be guaranteed. Finally, a case study of biped robot control with multiple faults and uncertainties is analyzed, and the effectiveness of the proposed fault accommodation scheme is demonstrated by simulation results. Its superiority is also assessed by a numerical comparison with other neural-network-based control schemes.Index Terms-Biped robot, Elman neural network, fault accommodation, recurrent wavelet neural network (RWNN).

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“…The superscript denotes the layer, the subscript denotes Layer 2 (Context Layer): In the context layer, the input node and the output node are represented as u (2) …”

Section: B Rwenn For Fault Estimationmentioning

confidence: 99%

Section: B Rwenn For Fault Estimationmentioning

confidence: 99%

mentioning

confidence: 99%

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Fault Accommodation Control for a Biped Robot Using a Recurrent Wavelet Elman Neural Network

Lin

Boldbaatar

2017

IEEE Systems Journal

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“…Thus, computing motions which are optimal, is time consuming and is often done off-line to create a database. Databased motions allow to realize global navigation: [1], [2], [3] start from a set of possible step motions and plan a sequence of steps to reach the goal thanks to algorithms such as Rapidly-exploring randomized Trees (RRT) algorithms [4]. Motion planning also includes the problem of digital actors' locomotion [5], kick motion generation on HRP-2 robot [6], computing a manipulator robot's trajectory [7] or smoothing pre-calculated motions [8].…”

Section: Introductionmentioning

confidence: 99%

Safe motion planning computation for databasing balanced movement of humanoid robots

Lengagne

Ramdani

Fraisse

2009

2009 IEEE International Conference on Robotics and Automation

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Motion planning can be seen as a Semi-Infinite Programming problem (SIP) since it involves a finite number of variables over an infinite set of constraints. Most methods solve the SIP problem by transforming it into a finite programming one using a discretization over a prescribed grid. We show that this approach is risky because it can lead to motions which may violate one or several constraints. Then we introduce our new method for planning safe motions. It uses Interval Analysis techniques in order to achieve a safe discretization of the constraints. We show how to implement this method and use it with state-of-the-art constrained optimization packages. Then, we illustrate its capabilities for planning safe motions dedicated to the HOAP-3 humanoid robot.

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Bioinspired Liquid Metal Based Soft Humanoid Robots

Li,

Yuan,

et al. 2024

Advanced Materials

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The pursuit of constructing humanoid robots to replicate the anatomical structures and capabilities of human beings has been a long‐standing significant undertaking and especially garnered tremendous attention in recent years. However, despite the progress made over recent decades, humanoid robots have predominantly been confined to those rigid metallic structures, which however starkly contrast with the inherent flexibility observed in biological systems. To better innovate this area, the present article systematically explores the value and potential of liquid metals and their derivatives in facilitating a crucial transition towards soft humanoid robots. Through a comprehensive interpretation of bionics, we present an overview of liquid metals’ multifaceted roles as essential components in constructing advanced humanoid robots – functioning as soft actuators, sensors, power sources, logical devices, circuit systems, and even transformable skeletal structures. We conceived that the integration of these components with flexible structures, facilitated by the unique properties of liquid metals, can create unexpected versatile functionalities and behaviors to better fulfill human needs. Finally, we envision a revolution in humanoid robots, transitioning from metallic frameworks to hybrid soft‐rigid structures resembling that of biological tissues. This article is expected to provide fundamental guidance for the coming research, thereby advancing the area.This article is protected by copyright. All rights reserved

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Modeling and motion planning of the infant-size humanoid robot THBIP-II

Cited by 6 publications

References 13 publications

Fault Accommodation Control for a Biped Robot Using a Recurrent Wavelet Elman Neural Network

Fault Accommodation Control for a Biped Robot Using a Recurrent Wavelet Elman Neural Network

Safe motion planning computation for databasing balanced movement of humanoid robots

Bioinspired Liquid Metal Based Soft Humanoid Robots

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