Continuous steps toward humanoid push recovery

Mao, Wentao; Kim, Jeong-Jung; Lee, Ju-Jang

doi:10.1109/ical.2009.5262995

Cited by 9 publications

(4 citation statements)

References 11 publications

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“…Footstep planning is also considered in humanoid push recovery [15]- [18]. Undergoing a high magnitude push, a robot might lose balance and need to take several steps to recover.…”

Section: A Biped Locomotion Generation and Footstep Planningmentioning

confidence: 99%

Ball walker: A case study of humanoid robot locomotion in non-stationary environments

Zheng

Yamane

2011

2011 IEEE International Conference on Robotics and Automation

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This paper presents a control framework for a biped robot to maintain balance and walk on a rolling ball. The control framework consists of two primary components: a balance controller and a footstep planner. The balance controller is responsible for the balance of the whole system and combines a state-feedback controller designed by pole assignment with an observer to estimate the system's current state. A wheeled linear inverted pendulum is used as a simplified model of the robot in the controller design. Taking the output of the balance controller, namely the ideal center of pressure of the biped robot on the ball, as the input, the footstep planner computes the foot placements for the robot to track the ideal center of pressure and avoid a fall from the rolling ball. Simulation results show that the proposed controller can enable a biped robot to stably walk on balls of different sizes and rotate a ball to desired positions at desired speeds.

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“…Footstep planning is also considered in humanoid push recovery [15]- [18]. Undergoing a high magnitude push, a robot might lose balance and need to take several steps to recover.…”

Section: A Biped Locomotion Generation and Footstep Planningmentioning

confidence: 99%

Ball walker: A case study of humanoid robot locomotion in non-stationary environments

Zheng

Yamane

2011

2011 IEEE International Conference on Robotics and Automation

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“…The system is trained using K-mean algorithm which is an unsupervised classification technique. Algorithm 2: train the system using K-Mean algorithm START [1] Choose any arbitrary vector mean when a=0, [2] Mean will be [w 1 , w 2 , w 3 …w z ] a where z is no. of cluster head.…”

Section: Trainingmentioning

confidence: 99%

“…When we talk about the push recovery features in humanoid robot we face difficulties as compared to animals and humans. Humans and animals are completely versed where as humanoid robots have certain other features [2]. It's contemplated to make a humanoid robot that can work smoothly and practically.…”

mentioning

confidence: 99%

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

Parashar

Goyal

et al. 2016

Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies

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Push recovery is prime ability that is essential to be incorporated in the process of developing a robust humanoid robot to support bipedalism. In real environment it is very essential for humanoid robot to maintain balance. In this paper we are generating a control system and push recovery controller for humanoid robot walking. We apply different kind of pushes to humanoid robot and the algorithm that can bring a change in the walking stage to sustain walking. The simulation is done in 3D environment using Webots. This paper describes techniques for feature selection to foreshow push recovery for hip, ankle and knee joint. We train the system by K-Mean algorithm and testing is done on crouch data and tested results are reported. Random push data of humanoid robot is collected and classified to see whether push lie in safer region and then tested on given proposed system.

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“…Humans and animals are completely versed where as humanoid robots have certain other features [2]. It's contemplated to make a humanoid robot that can work smoothly and practically.…”

mentioning

confidence: 99%

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

Parashar¹,

Parashar²,

Goyal³

2016

IJIMAI

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-Push recovery is prime ability that is essential to be incorporated in the process of developing a robust humanoid robot to support bipedalism. In real environment it is very essential for humanoid robot to maintain balance. In this paper we are generating a control system and push recovery controller for humanoid robot walking. We apply different kind of pushes to humanoid robot and the algorithm that can bring a change in the walking stage to sustain walking. The simulation is done in 3D environment using Webots. This paper describes techniques for feature selection to foreshow push recovery for hip, ankle and knee joint. We train the system by K-Mean algorithm and testing is done on crouch data and tested results are reported. Random push data of humanoid robot is collected and classified to see whether push lie in safer region and then tested on given proposed system.

show abstract

Continuous steps toward humanoid push recovery

Cited by 9 publications

References 11 publications

Ball walker: A case study of humanoid robot locomotion in non-stationary environments

Ball walker: A case study of humanoid robot locomotion in non-stationary environments

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

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

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