DCM-Based Gait Generation for Walking on Moving Support Surfaces

Englsberger, Johannes; Mesesan, George; Ott, Christian; Albu‐Schäffer, Alin

doi:10.1109/humanoids.2018.8625006

Cited by 9 publications

(11 citation statements)

References 24 publications

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“…This leads to the discontinuity of the external contact wrenches and consequentially of the desired joint torques. The development of a DCM trajectory generator that handles non-instantaneous transitions between two single support phases becomes pivotal [22], [27]. In the following, we decide to implement the solution proposed by Englsberger in [22].…”

Section: A Trajectory Optimization Layermentioning

confidence: 99%

“…By substituting (27) into the DCM dynamics (11), the following ordinary differential equation (ODE) holds:…”

Section: Step Adaptermentioning

confidence: 99%

“…Finally, the relation described in (36) is treated as an equality constraint. It is worth to notice that the linearity of (36) is ensured by the specific choice of (27), and this choice also guarantees the boundedness of the ZMP trajectory. Since the cost function and the constraints depend quadratically and linearly on the unknown variables, all the above can be casted as a QP problem.…”

Section: Step Adaptermentioning

confidence: 99%

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Online DCM Trajectory Generation for Push Recovery of Torque-Controlled Humanoid Robots

Shafiee

Romualdi

Dafarra

et al. 2019

2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids)

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We present a computationally efficient method for online planning of bipedal walking trajectories with push recovery. In particular, the proposed methodology fits control architectures where the Divergent-Component-of-Motion (DCM) is planned beforehand, and adds a step adapter to adjust the planned trajectories and achieve push recovery. Assuming that the robot is in a single support state, the step adapter generates new positions and timings for the next step. The step adapter is active in single support phases only, but the proposed torque-control architecture considers double support phases too. The key idea for the design of the step adapter is to impose both initial and final DCM step values using an exponential interpolation of the time varying ZMP trajectory. This allows us to cast the push recovery problem as a Quadratic Programming (QP) one, and to solve it online with stateof-the-art optimisers. The overall approach is validated with simulations of the torque-controlled 33 kg humanoid robot iCub. Results show that the proposed strategy prevents the humanoid robot from falling while walking at 0.28 m/s and pushed with external forces up to 150 Newton for 0.05 seconds.

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Section: A Trajectory Optimization Layermentioning

confidence: 99%

“…By substituting (27) into the DCM dynamics (11), the following ordinary differential equation (ODE) holds:…”

Section: Step Adaptermentioning

confidence: 99%

Section: Step Adaptermentioning

confidence: 99%

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Online DCM Trajectory Generation for Push Recovery of Torque-Controlled Humanoid Robots

Shafiee

Romualdi

Dafarra

et al. 2019

2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids)

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“…They applied a general form of spline to connect to each ZMP terminal, and derived a recursive form solution for DCM, instead of COM. Moreover, the gait generator was extended to allow to cross on moving support surfaces (Englsberger et al, 2018). Takenaka et al (2009) decomposed the equation of motion of the three-mass LIPM into DCM, and convergent component of motion.…”

Section: Related Workmentioning

confidence: 99%

Closed‐form solution for real‐time optimal walking pattern generation constrained by the desired amount of capture point

Park¹

2021

Journal of Field Robotics

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I present a calculus variations problem for an optimal zero-moment point (ZMP) pattern, and derive a closed-form solution satisfying two-boundary conditions of ZMP and an additional constraint, which is a difference between a current capture point and another. In addition, I present a closed-form solution for the equation of motion of the linear inverted pendulum model, or a cart-table model using the optimal ZMP pattern. Through the closed-form solutions, a current walking pattern

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“…The Divergent Component of Motion (DCM) is widely used for gait generation, e.g. refer to the example by Englsberger et al (2018). DCM is defined as a twodimensional point ξ ∈ R 2 by…”

Section: Centroidal Space Constraintsmentioning

confidence: 99%

Impact-Aware Task-Space Quadratic-Programming Control

Wang¹,

Dehio²,

Tanguy³

et al. 2020

Preprint

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Generating on-purpose impacts with rigid robots is challenging as they may lead to severe hardware failures due to abrupt changes in the velocities and torques. Without dedicated hardware and controllers, robots typically operate at a near-zero velocity in the vicinity of contacts. We assume knowing how much of impact the hardware can absorb and focus solely on the controller aspects. Hybrid controllers with reset maps provided elegant solutions for given impact tasks. The novelty of our approach is twofold: (i) it uses the task-space inverse dynamics formalism that we extend by seamlessly integrating impact tasks; (ii) it does not require separate models with switches or a reset map to operate the robot undergoing impact tasks. Our main idea lies in integrating post-impact states prediction and impactaware inequality constraints as part of our existing general-purpose whole-body controller. To achieve such prediction, we formulate task-space impacts and its spreading along the kinematic and potentially arborescent, structure of a floating-base robot with subsequent joint velocity and torque jumps. As a result, the feasible solution set accounts for various constraints due to expected impacts. In a multi-contact situation of under-actuated legged robots subject to multiple impacts, we also enforce the dynamic equilibrium margins. By design, our controller does not require precise knowledge of impact location and timing. We assessed our formalism with the humanoid robot HRP-4, generating maximum contact velocities, neither breaking established contacts nor damaging the hardware.

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DCM-Based Gait Generation for Walking on Moving Support Surfaces

Cited by 9 publications

References 24 publications

Online DCM Trajectory Generation for Push Recovery of Torque-Controlled Humanoid Robots

Online DCM Trajectory Generation for Push Recovery of Torque-Controlled Humanoid Robots

Closed‐form solution for real‐time optimal walking pattern generation constrained by the desired amount of capture point

Impact-Aware Task-Space Quadratic-Programming Control

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