Landing Pattern Modification Method with Predictive Attitude and Compliance Control to Deal with Uneven Terrain

Hashimoto, Kenji; Sugahara, Yusuke; Kawase, M.; Ohta, Atsuyuki; Tanaka, Chiaki; Hayashi, Atsushi; Endo, Nobutsuna; Sawato, Terumasa; Lim, Hun-ok; Takanashi, A.

doi:10.1109/iros.2006.282213

Cited by 16 publications

(5 citation statements)

References 15 publications

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“…While we admit that these strategies relying on walking pattern generators can in the long term benefit from the developments in these techniques that would allow them to autonomously cope with unstructured terrain (variable height stairs, rough terrain) (Takanishi and Kato, 1994 ; Hashimoto et al, 2006 ; Herdt et al, 2010 ; Morisawa et al, 2011 ), and that they can as well use the hierarchical architectures in which they are embedded as it is the case in Chung et al ( 2011 ); Bryan et al ( 2011 ); Ma et al ( 2013 ) for switching, for example, to an appropriate stair-climbing controller when facing stairs, we choose in this work to investigate an entirely different approach that does not incorporate any kind of walking or high-level controller. Instead, we propose to allow the user to perform lower-level joint/link level control of the whole-body motion of humanoid, driven again by the desire of replicating the human low-level motor-control strategies into the humanoid, but also by the belief that a generic-motion generating approach will allow the robot assistant to deal more systematically with unpredictable situations that inevitably occur in everyday living scenarios and for which the discussed hierarchical architectures would not have exhaustively accounted.…”

Section: Related Work and Proposed Solutionmentioning

confidence: 99%

Brain-machine interfacing control of whole-body humanoid motion

Bouyarmane¹,

Vaillant²,

Sugimoto³

et al. 2014

Front. Syst. Neurosci.

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We propose to tackle in this paper the problem of controlling whole-body humanoid robot behavior through non-invasive brain-machine interfacing (BMI), motivated by the perspective of mapping human motor control strategies to human-like mechanical avatar. Our solution is based on the adequate reduction of the controllable dimensionality of a high-DOF humanoid motion in line with the state-of-the-art possibilities of non-invasive BMI technologies, leaving the complement subspace part of the motion to be planned and executed by an autonomous humanoid whole-body motion planning and control framework. The results are shown in full physics-based simulation of a 36-degree-of-freedom humanoid motion controlled by a user through EEG-extracted brain signals generated with motor imagery task.

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Section: Related Work and Proposed Solutionmentioning

confidence: 99%

Brain-machine interfacing control of whole-body humanoid motion

Bouyarmane¹,

Vaillant²,

Sugimoto³

et al. 2014

Front. Syst. Neurosci.

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“…A method that uses preview control from the current inclination of the upper body to a stable future state in order to update the upper body trajectory was proposed in [3]. Another method [4] is based on a predictive attitude compensation control and a nonlinear compliance control using only force sensors and a particular biped foot system that adapts to a rough terrain.A technique to stabilize the upper body postion under varying ground reaction forces is proposed in [5]. This technique generates moments to handle Fig.…”

Section: Introductionmentioning

confidence: 99%

Walking on non-planar surfaces using an inverse dynamic stack of tasks

Ramos

Mansard

Stasse

et al. 2012

2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012)

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This paper presents a method to handle walking on non-planar surfaces. The method obtains the trajectory of the center of mass and the next position of the foot from a pattern generator. Then, an inverse dynamics control scheme with a quadratic programming optimization solver is used to let the foot go from its initial to its final position, controlling also the center of mass and the waist. This solver is able to handle an arbitrary number of contact points. When the swinging foot is going down, collision points are detected and they are added as contact points to the model as soon as they appear. If there are three or more contact points, the foot can safely step, but if there are one or two contact points, the foot rotates properly to generate the largest support polygon. Using this heuristic, the foot can stand on non-planar surfaces. The results show the simulation of HRP-2 walking on a surface with obstacles.

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“…However, to attempt to achieve such a barrier-free concept through infrastructural improvements alone can be very expensive and complex. Therefore, we have developed a biped robot that can enable a human to overcome typical barriers within the human environment [1][2][3][4]. In particular, in November 2003, WL-16 (Waseda LegNo.…”

Section: Introductionmentioning

confidence: 99%

Disturbance Compensation Control for a Biped Vehicle

2011

Self Cite

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This paper describes an avoidance behavior against unknown forces acting from outside the system on a biped vehicle. External forces that act on a biped vehicle can be divided into two categories: disturbances from the rider of the vehicle and disturbances from outside the system. Disturbances from the rider are measured by a force sensor placed between the rider's seat and the pelvis, while disturbances from outside the system are estimated from zero moment point (ZMP) errors. To guarantee walking stability, the waist position is adjusted to match the measured ZMP to the reference ZMP and the position of the landing foot is adjusted such that the waist trajectory does not diverge. On implementing the developed method on the human-carrying biped robot in an experimental setup, the robot could realize a stable walk even while subjected to unknown external forces in the environment. On applying a pushing force to the walking robot, the robot moved in the pushed direction and away from the source of the externally generated forces. On pushing the robot that was walking forward, the robot stopped moving forward and avoided getting closer to the source of the externally generated forces. We confirmed the effectiveness of the proposed control through these experiments.

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Landing Pattern Modification Method with Predictive Attitude and Compliance Control to Deal with Uneven Terrain

Cited by 16 publications

References 15 publications

Brain-machine interfacing control of whole-body humanoid motion

Brain-machine interfacing control of whole-body humanoid motion

Walking on non-planar surfaces using an inverse dynamic stack of tasks

Disturbance Compensation Control for a Biped Vehicle

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