A new control strategy for ROBIAN biped robot inspired from human walking

Serhan, H.; Nasr, Chaiban; Hénaff, Patrick; Ouezdou, Fethi Ben

doi:10.1109/iros.2008.4650612

Cited by 6 publications

(15 citation statements)

References 18 publications

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“…2). More description of human walking phase could be found in our previous article [29]. What is important here is that human locomotion is a succession of passive and active phases [8].…”

Section: A Robot Walking Parametersmentioning

confidence: 99%

“…(2) approach robot speed is controlled by simple variation of B~:x_8 and B1:n_K_8 as described in [29], and the values of Kc, Kd parameters are changed at each phase.…”

Section: A Robot Walking Parametersmentioning

confidence: 99%

“…The work presented in this paper comes after a previous one [29] in which we show how to use a simple description technique based on a Petri-Net algorithm to control a biped robot to walk infinitely without falling, in a similar way to humans. A full comparison between human walking angles and that of our robot was presented and discussed.…”

Section: Introductionmentioning

confidence: 99%

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State machine-based controller for walk-halt-walk transitions on a biped robot

Serhan

Hénaff

Nasr

et al. 2008

Humanoids 2008 - 8th IEEE-RAS International Conference on Humanoid Robots

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In this paper, we show how it is possible to obtain a walk -Standup -walk cycle with a biped robot using only a state machine controller. This work is a continuation of another in which the same approach was used to control the walking gait of the same robot. We introduce four critical angles that affect robot speed and step length. Our control approach consists of two control levels. The low level is a PID controller and the high level is a Dynamic Walking Algorithm, inspired from human locomotion, which tunes the PD controllers and im pose the reference signals based on the walking which increases walk stability. This method could be easily implemented in real time because it needs acceptable calculation time. We validated the control approach to a dynamic simulation of our 14DOF biped called ROBIAN. We prove that we can maintain robot walking and stopping stability and walk cycle's repetition without referencing a predefined trajectory or detecting the center of pressure. Results show that the walk of the biped is very similar to human one.

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“…2). More description of human walking phase could be found in our previous article [29]. What is important here is that human locomotion is a succession of passive and active phases [8].…”

Section: A Robot Walking Parametersmentioning

confidence: 99%

“…(2) approach robot speed is controlled by simple variation of B~:x_8 and B1:n_K_8 as described in [29], and the values of Kc, Kd parameters are changed at each phase.…”

Section: A Robot Walking Parametersmentioning

confidence: 99%

See 1 more Smart Citation

State machine-based controller for walk-halt-walk transitions on a biped robot

Serhan

Hénaff

Nasr

et al. 2008

Humanoids 2008 - 8th IEEE-RAS International Conference on Humanoid Robots

Self Cite

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“…The authors also used human motion data captured by an optical system. Serhan et al [9] proposed to extract "critical angles" from human locomotion that influence the speed and step length. They used a segmentation of the walking cycle in eight phases and defined maximum angles for the different phases.…”

Section: Related Workmentioning

confidence: 99%

“…We define the similarity between the gaits in terms of joint angle difference between the human's and the robot's joint angle trajectories. The advantage of working solely on the basis of joint angles is that we do not need to incorporate expert knowledge into the learning process, e.g., in form of a parameterized gait [7] or in form of a segmentation into different walking phases [9].…”

Section: Introductionmentioning

confidence: 99%

Humanoid Gait Optimization Based on Human Data

Wehner

Bennewitz

2011

Automatika

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Original scientific paperAchieving a stable, human-like gait for humanoid robots is a challenging task. While a variety of techniques exist to generate stable walking patterns, only little attention has been paid to the resemblance to the human gait. Popular gaits, for example, apply the strategy to bend the knees and to swing the torso in the lateral direction in order to ensure stability by shifting the center of mass. As a result, the walking patterns do not look very humanlike. However, human resemblance is an important aspect whenever robots are designed to coexist and interact with humans. In this article, we present techniques to optimize a given, stable gait of a humanoid robot with respect to human resemblance. To acquire human data, we use a full-body motion capture system. We propose four different optimization algorithms that work at joint angle basis and use the joint angle difference as measure of similarity. The experiments carried out with a HOAP-2 robot in simulation demonstrate that all techniques generate a gait that is significantly more human-like compared to the robot's initial gate. As the results show, the optimization methods based on hill climbing and policy gradient estimation yield the best performance. Key words: Humanoid robots, Gait optimization, Human-like walkingOptimizacija držanjačovjekolikih robota utemeljena na podacima izčovjekovog hoda. Postizanje stabilnog,čovjekolikog držanjačovjekolikih robota vrlo je zahtjevan zadatak. Iako postoji mnoštvo tehnika koje se koriste za postizanje stabilnih uzoraka hodanja, malo se pažnje pridaje sličnosti s ljudskim držanjem. Primjerice, neke tehnike koriste strategiju savijanja koljena i njihanja torza u lateralnom smjeru kako bi se osigurala stabilnost kroz promjenu položaja centra mase. Kao rezultat toga, uzorci hodanja nisu sličničovjekovom hodu. Ipak, sličnosť covjeku važan je aspekt u slučajevima kada su roboti izvedeni za suživot i interakciju s ljudima. U ovom sučlanku predstavljene tehnike za optimizaciju danih stabilnih držanjačovjekolikog robota s ciljem sličnostičovjeku. Za prikupljanje podataka očovjeku koristi se sustav za snimanje cjelokupnog gibanja tijela. Predložena sučetiri različita algoritma optimizacije koji koriste zajedničku derivaciju kuta kao mjeru sličnosti. Eksperimenti provedeni koristeći HOAP-2 robot pokazuju da svi postupci generiraju držanje koje je značajno više nalikčovjekovom držanju u odnosu na početno držanje robota. Kao što rezultati pokazuju, metode optimizacije koje se zasnivaju na penjanju uzbrdo i estimaciji gradijenta smjera daju najbolje rezultate.

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