ZMP Constraint Restriction for Robust Gait Generation in Humanoids

Smaldone, Filippo M.; Scianca, Nicola; Modugno, Valerio; Lanari, Leonardo; Oriolo, Giuseppe

doi:10.1109/icra40945.2020.9197171

Cited by 8 publications

(2 citation statements)

References 32 publications

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“…On the one hand, the traditional controller based on ZMP has difficulty dealing with complex nonlinear constraints caused by the change in CoM height [ 34 ]. On the other hand, to improve stability, many constraints, such as structural size and stability margin, must be considered [ 35 ]. MPC can solve nonlinear multiple constraint optimization problems, so it is widely used in humanoids [ 36 , 37 , 38 ].…”

Section: Related Workmentioning

confidence: 99%

Robust Walking for Humanoid Robot Based on Divergent Component of Motion

Zhang

Shan

et al. 2022

Micromachines

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In order to perform various complex tasks in place of humans, humanoid robots should walk robustly in the presence of interference. In the paper, an improved model predictive control (MPC) method based on the divergent components of motion (DCM) is proposed. Firstly, the humanoid robot model is simplified to a finite-sized foot-pendulum model. Then, the gait of the humanoid robot in the single-support phase (SSP) and double-support phase (DSP) is planned based on DCM. The center of mass (CoM) of the robot will converge to the DCM, which simplifies the feedback control process. Finally, an MPC controller incorporating an extended Kalman filter (EKF) is proposed to realize the tracking of the desired DCM trajectory. By adjusting the step duration, the controller can compensate for CoM trajectory errors caused by disturbances. Simulation results show that—compared with the traditional method—the method we propose achieves improvements in both disturbed walking and uneven-terrain walking.

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Section: Related Workmentioning

confidence: 99%

Robust Walking for Humanoid Robot Based on Divergent Component of Motion

Zhang

Shan

et al. 2022

Micromachines

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“…Dynamic stability occurs when the zero moment point (ZMP)-the point with respect to which reaction forces at the contacts between the feet and the ground do not produce any moment in the horizontal direction-is maintained inside the support polygon throughout the gait. Gait patterns whose stability is determined by dynamic conditions allow for faster displacements of the robot because the CoM projection can be located outside of the support polygon for short periods of time [3,4]. Therefore, in order to guarantee stable locomotion, gait synthesizing algorithms must coordinate all limbs of the robot to make it move in the desired direction, while satisfying the static or dynamic equilibrium condition.…”

Section: Introductionmentioning

confidence: 99%

Kinematic Tripod (K3P): A New Kinematic Algorithm for Gait Pattern Generation

Soto-Guerrero,

Ramírez-Torres,

Rodriguez-Tello

2024

Applied Sciences

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Insects are good examples of ground locomotion because they can adapt their gait pattern to propel them in any direction, over uneven terrain, in a stable manner. Nevertheless, replicating such locomotion skills to a legged robot is not a straightforward task. Different approaches have been proposed to synthesize the gait patterns for these robots; each approach exhibits different restrictions, advantages, and priorities. For the purpose of this document, we have classified gait pattern generators for multi-legged robots into three categories: precomputed, heuristic, and bio-inspired approaches. Precomputed approaches rely on a set of precalculated motion patterns obtained from geometric and/or kinematic models that are performed repeatedly whenever necessary and that cannot be modified on-the-fly to adapt to the terrain changes. On the other hand, heuristic and bio-inspired approaches offer on-line adaptability, but parameter-tuning and heading control can be difficult. In this document, we present the K3P algorithm, a real-time kinematic gait pattern generator conceived to command a legged robot. In contrast to other approaches, K3P enables the robot to adapt its gait to follow an arbitrary trajectory, at an arbitrary speed, over uneven terrain. No precomputed motions for the legs are required; instead, K3P modifies the motion of all mechanical joints to propel the body of the robot in the desired direction, maintaining a tripod stability at all times. In this paper, all the specific details of the aforementioned algorithm are presented, as well as different simulation results that validate its characteristics.

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M-A3C: A Mean-Asynchronous Advantage Actor-Critic Reinforcement Learning Method for Real-Time Gait Planning of Biped Robot

Leng

Fan²,

Tang

et al. 2022

IEEE Access

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Bipedal walking is a challenging task for humanoid robots. In this study, we develop a lightweight reinforcement learning method for real-time gait planning of the biped robot. We regard bipedal walking as a process in which the robot constantly interacts with the environment, judges the quality of control action through the walking state, and then adjusts the control strategy. A mean-asynchronous advantage actor-critic (M-A3C) reinforcement learning algorithm is proposed to obtain the continuous state space and action space, and directly obtain the final gait of the robot without introducing the reference gait. We use multiple sub-agents of M-A3C algorithm to train multiple virtual robots independently at the same time in the physical simulation platform. Then we transfer the trained model to the walking control of the actual robot to reduce the number of training on the actual robot, improve the training speed, and ensure the acquisition of the final gait. Finally, a biped robot is designed and fabricated to verify the effectiveness of the proposed method. Various experiments show that the proposed method can achieve the biped robot's continuous and stable gait planning.

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ZMP Constraint Restriction for Robust Gait Generation in Humanoids

Cited by 8 publications

References 32 publications

Robust Walking for Humanoid Robot Based on Divergent Component of Motion

Robust Walking for Humanoid Robot Based on Divergent Component of Motion

Kinematic Tripod (K3P): A New Kinematic Algorithm for Gait Pattern Generation

M-A3C: A Mean-Asynchronous Advantage Actor-Critic Reinforcement Learning Method for Real-Time Gait Planning of Biped Robot

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