Biologically Inspired Dead-beat controller for bipedal running in 3D

Englsberger, Johannes; Kozłowski, Paweł; Ott, Christian

doi:10.1109/iros.2015.7353491

Cited by 6 publications

(13 citation statements)

References 24 publications

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“…. F ss (12) in which control input F ss is the force of the leg actuator and the coefficient matrices can be obtained easily.…”

Section: Single-support Phasementioning

confidence: 99%

“…This system is underactuated and classic controllers are not applicable for trajectory tracking. Therefore, we use a SLIP energy-level control law 15 for the model given in (12) as below so that the ASLIP force actuator can return it to the desired (constant) energy level of SLIP…”

Section: Upper Level Controllermentioning

confidence: 99%

“…9 For a restricted range of initial condition inside a basin of attraction, the SLIP model shows stable walking and running gaits, but outside this region SLIP has periodic unstable gaits. Thus, researchers have proposed several active SLIP, or ASLIP, architectures and controllers, for example, an energy-level control in stance phase, 10 swing leg retraction, 11 dead-beat control, 12 and variable free-leg length and stiffness. 13 Due to the SLIP model's advantages (including stability and energy efficiency), using it as a simple reference model in order to produce walking and running biped gaits for more complicated machines is a possible approach -one pursued in this paper.…”

Section: Introductionmentioning

confidence: 99%

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SLIP-Based Control of Bipedal Walking Based on Two-Level Control Strategy

Dadashzadeh

Macnab

2019

Robotica

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SUMMARYIn this research, we propose a two-level control strategy for simultaneous gait generation and stable control of planar walking of the Assume The Robot Is A Sphere (ATRIAS) biped robot with unlocked torso, utilizing active spring-loaded inverted pendulum (ASLIP) as reference models. The upper level consists of an energy-regulating control calculated using the ASLIP model, producing reference ground reaction forces (GRFs) for the desired gait. In the lower level controller, PID force controllers for the motors ensure tracking of the reference GRFs for ATRIAS direct dynamics. Meanwhile, ATRIAS torso angle is controlled stably to make it able to follow a point mass template model. Advantages of the proposed control strategy include simplicity and efficiency. Simulation results using ATRIAS’s complete dynamic model show that the proposed two-level controller can reject initial condition disturbances while generating stable and steady walking motion.

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“…. F ss (12) in which control input F ss is the force of the leg actuator and the coefficient matrices can be obtained easily.…”

Section: Single-support Phasementioning

confidence: 99%

Section: Upper Level Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SLIP-Based Control of Bipedal Walking Based on Two-Level Control Strategy

Dadashzadeh

Macnab

2019

Robotica

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“…Thus, haptic perception is a fundamental theme towards action understanding and control. The monitoring of contact forces is already widely used in various fields such as robot learning from demonstration and control [1], [2], physics-based animation [3], [4], and visual tracking [5], [6]. Measurement of contact forces is usually achieved by mounting force transducers at pre-fixed contact locations, making it a costly, cumbersome and intrusive process that is difficult to use in daily settings.…”

Section: Introductionmentioning

confidence: 99%

Multicontact Interaction Force Sensing From Whole-Body Motion Capture

Pham

Caron

Kheddar

2018

IEEE Trans. Ind. Inf.

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We present a novel technique that unobtrusively estimates forces exerted by human participants in multi-contact interaction with rigid environments. Our method uses motion capture only, thus circumventing the need to setup cumbersome force transducers at all potential contacts between the human body and the environment. This problem is particularly challenging, as the knowledge of a given motion only characterizes the resultant force, which can generally be caused by an infinity of force distributions over individual contacts. We collect and release a large-scale dataset on how humans instinctively regulate interaction forces on diverse multi-contact tasks and motions. The force estimation framework we propose leverages physicsbased optimization and neural networks to reconstruct force distributions that are physically realistic and compatible with real interaction force patterns. We show the effectiveness of our approach on various locomotion and multi-contact scenarios.

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“…The contact forces that are applied during a given task are informative on both the resulting motion and the underlying intent. Thus, force sensing has direct applications in research fields such as robot learning from demonstration and control [1], [2], physics-based animation [3], [4] and visual tracking [5], [6]. Contact forces are typically measured using force transducers that are costly, cumbersome and of limited, varying accuracy under repeated stress [7].…”

Section: Introductionmentioning

confidence: 99%

Whole-body contact force sensing from motion capture

Pham¹,

Bufort²,

Caron³

et al. 2016

2016 IEEE/SICE International Symposium on System Integration (SII)

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Using motion capture only, we challenge in this paper the estimation of the contact forces-backed with ground-truth sensing, in human whole-body interaction with the environment. Our method is novel and makes it possible to get rid of cumbersome force sensors in monitoring multicontact motion and its interaction force data. This problem is very difficult. Indeed, while a given force distribution uniquely determines the resulting kinematics, the converse is generally not true in multi-contact. In such scenarios, physics-based optimization alone may only capture force distributions that are physically compatible with a given motion rather than the actual forces being applied. We address this indeterminacy by collecting a large-scale dataset on whole-body motion and contact forces humans apply in multi-contact scenarios. We then train recurrent neural networks on real human force distribution patterns and complement them with a second-order cone program ensuring the physical validity of the predictions. Extensive validation on challenging dynamic and multi-contact scenarios shows that the method we propose can outperform physical force sensing both in terms of accuracy and usability.

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Biologically Inspired Dead-beat controller for bipedal running in 3D

Abstract: Fig. 1: Bipedal point-mass model runs in 3D based on Biologically Inspired Dead-beat (BID) control.

Cited by 6 publications

References 24 publications

SLIP-Based Control of Bipedal Walking Based on Two-Level Control Strategy

SLIP-Based Control of Bipedal Walking Based on Two-Level Control Strategy

Multicontact Interaction Force Sensing From Whole-Body Motion Capture

Whole-body contact force sensing from motion capture

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