Push Recovery for Humanoid Robot in Dynamic Environment and Classifying the Data Using K-Mean

Parashar, Anubha; Parashar, Apoorva; Goyal, Somya; Sahjalan, Bharat

doi:10.1145/2905055.2905207

Cited by 8 publications

(4 citation statements)

References 22 publications

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“…So, the phase order during leg moment will be the left leg goes into swing phase and right will go in stance phase. [19].…”

Section: Design Of Atomic Components For Camentioning

confidence: 99%

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Machine Intelligence and Signal Analysis

Tanveer¹,

Pachori²

2019

Advances in Intelligent Systems and Computing

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The series "Advances in Intelligent Systems and Computing" contains publications on theory, applications, and design methods of Intelligent Systems and Intelligent Computing. Virtually all disciplines such as engineering, natural sciences, computer and information science, ICT, economics, business, e-commerce, environment, healthcare, life science are covered. The list of topics spans all the areas of modern intelligent systems and computing such as: computational intelligence, soft computing including neural networks, fuzzy systems, evolutionary computing and the fusion of these paradigms, social intelligence, ambient intelligence, computational neuroscience, artificial life, virtual worlds and society, cognitive science and systems, Perception and Vision, DNA and immune based systems, self-organizing and adaptive systems, e-Learning and teaching, human-centered and human-centric computing, recommender systems, intelligent control, robotics and mechatronics including human-machine teaming, knowledge-based paradigms, learning paradigms, machine ethics, intelligent data analysis, knowledge management, intelligent agents, intelligent decision making and support, intelligent network security, trust management, interactive entertainment,Web intelligence and multimedia.The publications within "Advances in Intelligent Systems and Computing" are primarily proceedings of important conferences, symposia and congresses. They cover significant recent developments in the field, both of a foundational and applicable character. An important characteristic feature of the series is the short publication time and world-wide distribution. This permits a rapid and broad dissemination of research results. Advisory BoardChairman Nikhil R. Pal,

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“…So, the phase order during leg moment will be the left leg goes into swing phase and right will go in stance phase. [19].…”

Section: Design Of Atomic Components For Camentioning

confidence: 99%

“…So we consider binary stage 0 and 1 for each component. 0 represents atomic components are in rest and 1 represents atomic components are in motion [19]. Since, there are three atomic components and each have two state either 0 or 1.…”

Section: Developing Cellular Automata( )mentioning

confidence: 99%

Machine Intelligence and Signal Analysis

Tanveer¹,

Pachori²

2019

Advances in Intelligent Systems and Computing

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“…The optimized detector is integrated into a 3D real time augmented reality application where the position of the face is used as a marker-less object, overcoming the difficulty of using markers in augmented reality application. Next paper, authored by Parashar et al [5], describes a control system and push recovery controller for humanoid robot walking. Push recovery is an ability that bipedal robots must have.…”

Section: T He International Journal Of Interactive Multimedia and Artmentioning

confidence: 99%

Editor's Note

Verdú¹

2016

IJIMAI

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“…Through this compensation, we are incorporating the non-linearity of the actual gait in the output of the analytical model. We also observed the bounded nature of the difference which led us to conclude that the strategy for human locomotion control is generic in nature [8], [9], [13]. This paper has been arranged in the following manner, in the next section different types of biped models have been described followed by their critical analysis.…”

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confidence: 99%

Hybrid Model for Passive Locomotion Control of a Biped Humanoid:The Artificial Neural Network Approach

Raj¹,

Semwal²,

Nandi³

2018

IJIMAI

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Developing a correct model for a biped robot locomotion is extremely challenging due to its inherently unstable structure because of the passive joint located at the unilateral foot-ground contact and varying configurations throughout the gait cycle, resulting variation of dynamic descriptions and control laws from phase to phase. The present research describes the development of a hybrid biped model using an Open Dynamics Engine (ODE) based analytical three link leg model as a base model and, on top of it, an Artificial Neural Network based learning model which ensures better adaptability, better limits cycle behaviors and better generalization while negotiating along a down slope. The base model has been configured according to the individual subjects and data have been collected using a novel technique through an android app from those subjects while walking down a slope. The pattern between the deviation of the actual trajectories and the base model generated trajectories has been found using a back propagation based artificial neural network architecture. It has been observed that this base model with learning based compensation enables the biped to better adapt in a real walking environment, showing better limit cycle behaviors. We also observed the bounded nature of deviation which led us to conclude that the strategy for biped locomotion control is generic in nature and largely dominated by learning.

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Push Recovery for Humanoid Robot in Dynamic Environment and Classifying the Data Using K-Mean

Cited by 8 publications

References 22 publications

Machine Intelligence and Signal Analysis

Machine Intelligence and Signal Analysis

Editor's Note

Hybrid Model for Passive Locomotion Control of a Biped Humanoid:The Artificial Neural Network Approach

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