Whole body motion controller with long-term balance constraints

Sherikov, Alexander; Dimitrov, Dimitar; Wieber, Pierre-Brice

doi:10.1109/humanoids.2014.7041399

Cited by 41 publications

(53 citation statements)

References 29 publications

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“…Furthermore, the WPG presented here was from a known simplified form, but the core concepts presented here are not conflicting with improvements such as [19] which added robustness, allowing stair climbing. Apart from improving the WPG itself, its integration to whole-body control can also be improved with works such as [20]. The weight tuning issue also needs to be considered depending on the particular problem.…”

Section: Discussionmentioning

confidence: 99%

Walking pattern generators designed for physical collaboration

Agravante

Sherikov

Wieber

et al. 2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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International audienceThis paper is about the design of humanoid walking pattern generators to be used for physical collaboration. A particular use case is a humanoid robot helping a human to carry large and/or heavy objects. To do this, we construct a reduced model which takes into account physical interaction. This is used in a model predictive control framework to generate separate behaviors for being a follower or a leader. The approach is then validated both on simulation and on the HRP-4 humanoid robot

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Section: Discussionmentioning

confidence: 99%

Walking pattern generators designed for physical collaboration

Agravante

Sherikov

Wieber

et al. 2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

Self Cite

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“…Interestingly, the terminal constraint (20) is equivalent to the capturability constraint, originally introduced in [18].…”

Section: ) Truncated Tailmentioning

confidence: 99%

“…Once a form is chosen for the residual part of the tail, this formula leads to a closed-form expression of the stability constraint which consists of a finite number of terms, and is therefore still amenable to real-time implementation. Similarly, one can use (18) to derive the corresponding expression of the terminal constraint. In the following, and specifically in the feasibility analysis of Sect.…”

Section: The Stability Constraint Becomesmentioning

confidence: 99%

MPC for Humanoid Gait Generation: Stability and Feasibility

et al. 2020

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We present IS-MPC, an intrinsically stable MPC framework for humanoid gait generation which incorporates an explicit stability constraint in the formulation. The proposed method uses as prediction model a dynamically extended LIP where ZMP velocities are the control inputs, producing in real time a gait (including footsteps with the associated timing) that realizes omnidirectional motion commands coming from an external source. The stability constraint links the future ZMP velocities to the current system state so as to guarantee the essential requirement that the generated CoM trajectory is bounded with respect to the ZMP trajectory. Since the control horizon of the MPC algorithm is finite, only part of the future ZMP velocities are decision variables of the QP problem; the remaining part, called tail, must be either conjectured or anticipated using preview information on the reference motion. Several possible options for the tail are discussed, and each of them is shown to correspond to a specific terminal constraint. A theoretical analysis of the feasibility of the generic MPC iteration is developed and used to obtain sufficient conditions for recursive feasibility. Finally, it is proved that IS-MPC guarantees stability of the CoM/ZMP dynamics if it is recursively feasible. Simulation and experimental results on the NAO and the HRP-4 humanoids are presented to illustrate the performance of the proposed method.

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“…Starting with [16], various optimal control formulation have been proposed by the community, to either tackle the robustness problem [30], include viability conditions [25], allow altitude variations of the center of mass (CoM) [2], or also include foot placements as parameters of the problem [11].…”

Section: Introductionmentioning

confidence: 99%

Learning Feasibility Constraints for Multicontact Locomotion of Legged Robots

Carpentier¹,

Budhiraja²,

Mansard³

2017

Robotics: Science and Systems XIII

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Abstract-Relying on reduced models is nowadays a standard cunning to tackle the computational complexity of multi-contact locomotion. To be really effective, reduced models must respect some feasibility constraints in regards to the full model. However, such kind of constraints are either partially considered or just neglected inside the existing reduced problem formulation. This work presents a systematic approach to incorporate feasibility constraints inside trajectory optimization problems. In particular, we show how to learn the kinematic feasibility of the centre of mass to be achievable by the whole-body model. We validate the proposed method in the context of multi-contact locomotion: we perform two stairs climbing experiments on two humanoid robots, namely the HRP-2 robot and the new TALOS platform.

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Whole body motion controller with long-term balance constraints

Cited by 41 publications

References 29 publications

Walking pattern generators designed for physical collaboration

Walking pattern generators designed for physical collaboration

MPC for Humanoid Gait Generation: Stability and Feasibility

Learning Feasibility Constraints for Multicontact Locomotion of Legged Robots

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