A Feedback Controller for Biped Humanoids that Can Counteract Large Perturbations During Gait

Komura, Taku; Leung, Howard; Kudoh, Shinzoh; Kuffner, James

doi:10.1109/robot.2005.1570405

Cited by 75 publications

(49 citation statements)

References 12 publications

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“…In particular, the Angular Momentum Pendulum Model (AMPM) [6], [7] is the closest to the model we present as the flywheel in our model can be seen as a physical embodiment of an angular momentum generator of the AMPM model.…”

Section: Introductionmentioning

confidence: 91%

Capture Point: A Step toward Humanoid Push Recovery

Pratt

Carff

Drakunov

et al. 2006

2006 6th IEEE-RAS International Conference on Humanoid Robots

911

610

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Abstract-It is known that for a large magnitude push a human or a humanoid robot must take a step to avoid a fall. Despite some scattered results, a principled approach towards "When and where to take a step" has not yet emerged.Towards this goal, we present methods for computing Capture Points and the Capture Region, the region on the ground where a humanoid must step to in order to come to a complete stop. The intersection between the Capture Region and the Base of Support determines which strategy the robot should adopt to successfully stop in a given situation.Computing the Capture Region for a humanoid, in general, is very difficult. However, with simple models of walking, computation of the Capture Region is simplified. We extend the wellknown Linear Inverted Pendulum Model to include a flywheel body and show how to compute exact solutions of the Capture Region for this model. Adding rotational inertia enables the humanoid to control its centroidal angular momentum, much like the way human beings do, significantly enlarging the Capture Region.We present simulations of a simple planar biped that can recover balance after a push by stepping to the Capture Region and using internal angular momentum. Ongoing work involves applying the solution from the simple model as an approximate solution to more complex simulations of bipedal walking, including a 3D biped with distributed mass.

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Section: Introductionmentioning

confidence: 91%

Capture Point: A Step toward Humanoid Push Recovery

Pratt

Carff

Drakunov

et al. 2006

2006 6th IEEE-RAS International Conference on Humanoid Robots

911

610

View full text Add to dashboard Cite

show abstract

“…R j : if x 1 is µ 1,j and ... and x in is µ in,j then y 1 is ν 1,j and ... and y out is ν out,j (6) where µ ij and ν lj are fuzzy sets, the indexes i = {1, ..., in} inputs, l = {1, ..., out} outputs and j = {1, ..., r} fuzzy rules. Layer 1 (Inputs): The nodes in this layer transmit the inputs directly to the next layer (the vector parameters has unity values w 1 i = 1 ∀i) eq.…”

Section: Neuro-fuzzy Controllermentioning

confidence: 99%

Walking Motion Generation and Neuro-Fuzzy Control with Push Recovery for Humanoid Robot

Méndez-Monroy

2017

INT J COMPUT COMMUN

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Push recovery is an essential requirement for a humanoid robot with the objective of safely performing tasks within a real dynamic environment. In this environment, the robot is susceptible to external disturbance that in some cases is inevitable, requiring push recovery strategies to avoid possible falls, damage in humans and the environment. In this paper, a novel push recovery approach to counteract disturbance from any direction and any walking phase is developed. It presents a pattern generator with the ability to be modified according to the push recovery strategy. The result is a humanoid robot that can maintain its balance in the presence of strong disturbance taking into account its magnitude and determining the best push recovery strategy. Push recovery experiments with different disturbance directions have been performed using a 20 DOF Darwin-OP robot. The adaptability and low computational cost of the whole scheme allows is incorporation into an embedded system.

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“…The second problem of push recovery been addressed by force-based approaches which uses force sensors and full body model of the robot to calculate the joint torques required to reject external disturbance force [12], [13] and biomechanically motivated push recovery approaches which focus on simple biomechanically motivated push recovery behavior based on a simplified model of the robot [14], [15], [16], [17], [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Practical bipedal walking control on uneven terrain using surface learning and push recovery

Zhang

Hong

et al. 2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Bipedal walking in human environments is made difficult by the unevenness of the terrain and by external disturbances. Most approaches to bipedal walking in such environments either rely upon a precise model of the surface or special hardware designed for uneven terrain. In this paper, we present an alternative approach to stabilize the walking of an inexpensive, commercially-available, position-controlled humanoid robot in difficult environments. We use electrically compliant swing foot dynamics and onboard sensors to estimate the inclination of the local surface, and use a online learning algorithm to learn an adaptive surface model. Perturbations due to external disturbances or model errors are rejected by a hierarchical push recovery controller, which modulates three biomechanically motivated push recovery controllers according to the current estimated state. We use a physically realistic simulation with an articulated robot model and reinforcement learning algorithm to train the push recovery controller, and implement the learned controller on a commercial DARwIn-OP small humanoid robot. Experimental results show that this combined approach enables the robot to walk over unknown, uneven surfaces without falling down.

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A Feedback Controller for Biped Humanoids that Can Counteract Large Perturbations During Gait

Cited by 75 publications

References 12 publications

Capture Point: A Step toward Humanoid Push Recovery

Capture Point: A Step toward Humanoid Push Recovery

Walking Motion Generation and Neuro-Fuzzy Control with Push Recovery for Humanoid Robot

Practical bipedal walking control on uneven terrain using surface learning and push recovery

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