Toward Developing a Computational Model for Bipedal Push Recovery–A Brief

Semwal, Vijay Bhaskar; Nandi, Gora Chand

doi:10.1109/jsen.2015.2389525

Cited by 59 publications

(28 citation statements)

References 9 publications

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“…Human motion capture data (HMCD) [57,58] can plan complex and diverse actions. As the reference action is generated by the human body, the planning method becomes simpler.…”

Section: Motion Planningmentioning

confidence: 99%

State of the Art: Bipedal Robots for Lower Limb Rehabilitation

Yang

She

et al. 2017

Applied Sciences

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Abstract:The bipedal robot is one of the most attractive robots types given its similarity to the locomotion of human beings and its ability to assist people to walk during rehabilitation. This review summarizes the chronological historical development of bipedal robots and introduces some current popular bipedal robots age. Then, the basic theory-stability control and key technology-motion planning of bipedal robots are introduced and analyzed. Bipedal robots have a wide range of applications in the service, education, entertainment, and other industries. After that, we specifically discuss the applications of bipedal robots in lower limb rehabilitation, including wearable exoskeleton robots, rehabilitation equipment, soft exoskeleton robots, and unpowered exoskeleton robots, and their control methods. Lastly, the future development and the challenges in this field are discussed.

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“…Human motion capture data (HMCD) [57,58] can plan complex and diverse actions. As the reference action is generated by the human body, the planning method becomes simpler.…”

Section: Motion Planningmentioning

confidence: 99%

State of the Art: Bipedal Robots for Lower Limb Rehabilitation

Yang

She

et al. 2017

Applied Sciences

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“…This profile can be compared with the velocity profile trajectories produced by human arm [61]. This conclusively proves that it is possible to communicate simulated emotions of robots These values of velocity reflect the change of emotions for three different gestures by introducing variation in motion parameters of generally existing robotic structured components [64][65][66][67][68][69]. Using this approach, social robots having mechanical components like robot arms can convey their emotional states to humans without requiring any additional components which directly exhibit zoomorphic features/expressions.…”

Section: Resultsmentioning

confidence: 62%

Interaction of robot with humans by communicating simulated emotional states through expressive movements

Sial

Ayaz

et al. 2016

Intel Serv Robotics

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This paper presents a non-verbal and non-facial method for effective communication of a "mechanoid robot" by conveying the emotions through gestures. This research focuses on human-robot interaction using a mechanoid robot that does not possess any anthropomorphic facial features for conveying gestures. Another feature of this research is the use of human-like smooth motion of this mechanoid robot in contrast to the traditional trapezoidal velocity profile for its communication. For conveying gestures, the connection between motion of robot and perceived emotions is established by varying the velocity and acceleration of the mechanoid structure. The selected motion parameters are changed systematically to observe the variation in perceived emotions. The perceived emotions have been further investigated using three different emotional behavior models: Russell's circumplex model of affect, Tellegen-WatsonClark model and PAD model. Results obtained show that the designated motion parameters are linked with the change of emotions. Moreover, the emotions perceived by the user are same through all three models, validating the reliability of all the three emotional scale models and also of the emotions perceived by the user.

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“…The passive gait was captured by using a plank and making it inclined at 4 degree angle and making the subject to walk on it down the inclined. We used an android application named Physics Toolbox Accelerometer developed by Vieyra Software, which will give us the acceleration along x and y axis direction [11], [12]. The smartphone was attached to the subject's hip and knees and the data was captured shown in Fig.…”

Section: Experimental Data Collectionmentioning

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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Toward Developing a Computational Model for Bipedal Push Recovery–A Brief

Cited by 59 publications

References 9 publications

State of the Art: Bipedal Robots for Lower Limb Rehabilitation

State of the Art: Bipedal Robots for Lower Limb Rehabilitation

Interaction of robot with humans by communicating simulated emotional states through expressive movements

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

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