Direct Centroidal Control for Balanced Humanoid Locomotion

Ficht, Grzegorz; Behnke, Sven

doi:10.1007/978-3-031-15226-9_24

Cited by 7 publications

(5 citation statements)

References 43 publications

(74 reference statements)

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“…We have adopted the idea of the Direct Centroidal Controller (DCC) from [5] to use a Kalman Filter to estimate the CoM state c = c ċ c on the sagittal c x and lateral c y planes and supplementing the measurement model z k with the unrotated and unbiased for gravity g trunk acceleration G ẍt from the IMU:…”

Section: Balance State Estimationmentioning

confidence: 99%

See 1 more Smart Citation

RoboCup 2022 AdultSize Winner NimbRo: Upgraded Perception, Capture Steps Gait and Phase-based In-walk Kicks

Pavlichenko¹,

Ficht²,

Amini³

et al. 2023

Preprint

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Beating the human world champions by 2050 is an ambitious goal of the Humanoid League that provides a strong incentive for RoboCup teams to further improve and develop their systems. In this paper, we present upgrades of our system which enabled our team NimbRo to win the Soccer Tournament, the Drop-in Games, and the Technical Challenges in the Humanoid AdultSize League of RoboCup 2022. Strong performance in these competitions resulted in the Best Humanoid award in the Humanoid League. The mentioned upgrades include: hardware upgrade of the vision module, balanced walking with Capture Steps, and the introduction of phase-based in-walk kicks.

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Section: Balance State Estimationmentioning

confidence: 99%

“…Fig. 6 shows three examples of the generated swing trajectories 5 . The function of the swing trajectory s is defined as:…”

Section: Phase-based In-walk Kickmentioning

confidence: 99%

RoboCup 2022 AdultSize Winner NimbRo: Upgraded Perception, Capture Steps Gait and Phase-based In-walk Kicks

Pavlichenko¹,

Ficht²,

Amini³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Because the walking pattern is produced from a specified ZMP trajectory, this method is also called ZMP-based walking pattern generation [11]. Different from using the CoM of the whole robot, the authors in [12] utilize a five-mass distribution model to plan wholebody motion. The momentum vector has a linear connection to the robot joint speed vector.…”

Section: Introductionmentioning

confidence: 99%

Model-Free and Learning-Free Proprioceptive Humanoid Movement Control

Borén¹,

Tao²,

Han³

et al. 2023

Preprint

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This paper presents a novel model-free method for humanoid-robot quasi-static movement control. Traditional model-based methods often require precise robot model parameters. Additionally, existing learning-based frameworks often train the policy in simulation environments, thereby indirectly relying on a model. In contrast, we propose a proprioceptive framework based only on sensory outputs. It does not require prior knowledge of a robot's kinematic model or inertial parameters. Our method consists of three steps: 1. Planning different pairs of center of pressure (CoP) and foot position objectives within a single cycle. 2. Searching around the current configuration by slightly moving the robot's leg joints back and forth while recording the sensor measurements of its CoP and foot positions. 3. Updating the robot motion with an optimization algorithm until all objectives are achieved. We demonstrate our approach on a NAO humanoid robot platform. Experiment results show that it can successfully generate stable robot motions.

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“…Furthermore, if the swing leg is heavy enough, its motion may significantly affect the motion of the entire robot [ 1 ]. In this sense, the problem of robot balance control with one foot is similar to that of robot balance control with two arms [ 2 ]. From a physical point of view, maintaining the balance of a biped robot with its arms reduces the difficulty of the control problem because the robot’s supporting polygon remains unchanged, while extra degrees of freedom can be used to accomplish the balance task [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

“…Current MPC algorithms for legged robots usually adopt simplified models [ 12 ], for example, the linear inverted pendulum (LIP) model [ 2 , 13 , 14 ], the single particle model [ 15 ] or the single rigid body (SRB) model [ 6 ], to balance between model precision and computation time. In the case of balancing on one foot, these models are unsuitable because they adopt the dynamics model of a particle or a rigid body.…”

Section: Introductionmentioning

confidence: 99%

Balanced Standing on One Foot of Biped Robot Based on Three-Particle Model Predictive Control

et al. 2022

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Balancing is a fundamental task in the motion control of bipedal robots. Compared to two-foot balancing, one-foot balancing introduces new challenges, such as a smaller supporting polygon and control difficulty coming from the kinematic coupling between the center of mass (CoM) and the swinging leg. Although nonlinear model predictive control (NMPC) may solve this problem, it is not feasible to implement it on the actual robot because of its large amount of calculation. This paper proposes the three-particle model predictive control (TP-MPC) approach. It combines with the hierarchical whole-body control (WBC) to solve the one-leg balancing problem in real time. The bipedal robot’s torso and two legs are modeled as three separate particles without inertia. The TP-MPC generates feasible swing leg trajectories, followed by the WBC to adjust the robot’s center of mass. Since the three-particle model is linear, the TP-MPC requires less computational cost, which implies real-time execution on an actual robot. The proposed method is verified in simulation. Simulation results show that our method can resist much larger external disturbance than the WBC-only control scheme.

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Direct Centroidal Control for Balanced Humanoid Locomotion

Cited by 7 publications

References 43 publications

RoboCup 2022 AdultSize Winner NimbRo: Upgraded Perception, Capture Steps Gait and Phase-based In-walk Kicks

RoboCup 2022 AdultSize Winner NimbRo: Upgraded Perception, Capture Steps Gait and Phase-based In-walk Kicks

Model-Free and Learning-Free Proprioceptive Humanoid Movement Control

Balanced Standing on One Foot of Biped Robot Based on Three-Particle Model Predictive Control

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