A survey: dynamics of humanoid robots

Sugihara, Tomomichi; Morisawa, Masaaki

doi:10.1080/01691864.2020.1778524

Cited by 33 publications

(21 citation statements)

References 91 publications

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“…The dynamics of a humanoid, which could be either a real human or a humanoid robot, is represented by a large-scale equation of motion and many inequalities originated from the limitation of contact forces. However, macroscopic characteristics embedded in the dynamics can be abstracted by focusing on the relationship between the COM and the ZMP ( Sugihara and Morisawa, 2020 ). Let us consider lateral motion of a humanoid as shown in Figure 1 .…”

Section: Dynamics Model Of the Com-zmp Regulatormentioning

confidence: 99%

Identification of COM Controller of a Human in Stance Based on Motion Measurement and Phase-Space Analysis

Sugihara

Kaneta²,

Murai³

2022

Front. Robot. AI

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This article proposes a process to identify the standing stabilizer, namely, the controller in humans to keep upright posture stable against perturbations. We model the controller as a piecewise-linear feedback system, where the state of the center of mass (COM) is regulated by coordinating the whole body so as to locate the zero-moment point (ZMP) at the desired position. This was developed for humanoid robots and is possibly able to elaborate the fundamental control scheme used by humans to stabilize themselves. Difficulties lie on how to collect motion trajectories in a wide area of the state space for reliable identification and how to identify the piecewise-affine dynamical system. For the former problem, a motion measurement protocol is devised based on the theoretical phase portrait of the system. Regarding the latter problem, some clustering techniques including K-means method and EM (Expectation-and-Maximization) algorithm were examined. We found that a modified K-means method produced the most accurate result in this study. The method was applied to the identification of a lateral standing controller of a human subject. The result of the identification quantitatively supported a hypothesis that the COM-ZMP regulator reasonably models the human’s controller when deviations of the angular momentum about the COM are limited.

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Section: Dynamics Model Of the Com-zmp Regulatormentioning

confidence: 99%

Identification of COM Controller of a Human in Stance Based on Motion Measurement and Phase-Space Analysis

Sugihara

Kaneta²,

Murai³

2022

Front. Robot. AI

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“…The equation of motion of the COM is represented as

where x and x Z are the longitudinal positions of the COM and the ZMP, respectively, z is the height of the COM with respect to the nominal ground, which is assumed to be constant, and g = 9.8 m/s 2 is the acceleration due to the gravity. For the mathematical derivation, refer Sugihara and Morisawa (2020) . The ZMP is naturally constrained within the supporting region due to the unilaterality of the contact forces as

where x Zmin and x Zmax are the rear and front ends of the supporting region in x -axis, respectively.…”

Section: Com-zmp Model For Human Dynamicsmentioning

confidence: 99%

Identification of a Step-And-Brake Controller of a Human Based on Prediction of Capturability

Kojima

Sugihara

2022

Front. Robot. AI

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An explicit mathematical form of a human’s step-and-brake controller is identified through motion measurement of the human subject. The controller was originally designed for biped robots based on the reduced-order dynamics and the model predictive control scheme with the terminal capturability condition, and is compatible with both stand-still and stepping motions. The minimal number of parameters facilitates the identification from measured trajectories of the center of mass and the zero-moment point of the human subject. In spite of the minimality, the result only suited the human’s behaviors well with slight modifications of the model by taking direction-dependency of the natural falling speed and the inertial torque about the center of mass into account. Furthermore, the parameters are successfully identified even from the first half of motion sequence, which means that the proposed method is available in designing on-the-fly systems to evaluate balancing abilities of humans and to assist balances of humans in walking.

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“…The general model of a legged robot is comprised of a b-DoF floating-base (the torso) and a serial chain of m-links (the limbs) [22]. The limbs are driven by m-rotors through mechanical transmissions as defined by a reduction matrix R m and an actuator topology matrix D m .…”

Section: The Dynamics Of Dissipative Rigid-body Systemsmentioning

confidence: 99%

The dynamic effect of mechanical losses of transmissions on the equation of motion of legged robots

Sim

Ramos

2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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Industrial manipulators do not collapse under their own weight when powered off due to the friction in their joints. Although these mechanism are effective for stiff position control of pick-and-place, they are inappropriate for legged robots that must rapidly regulate compliant interactions with the environment. However, no metric exists to quantify the robot's performance degradation due to mechanical losses in the actuators and transmissions. This paper provides a fundamental formulation that uses the mechanical efficiency of transmissions to quantify the effect of power losses in the mechanical transmissions on the dynamics of a whole robotic system. We quantitatively demonstrate the intuitive fact that the apparent inertia of the robots increase in the presence of joint friction. We also show that robots that employ high gear ratio and low efficiency transmissions can statically sustain more substantial external loads. We expect that the framework presented here will provide the fundamental tools for designing the next generation of legged robots that can effectively interact with the world.

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A survey: dynamics of humanoid robots

Cited by 33 publications

References 91 publications

Identification of COM Controller of a Human in Stance Based on Motion Measurement and Phase-Space Analysis

Identification of COM Controller of a Human in Stance Based on Motion Measurement and Phase-Space Analysis

Identification of a Step-And-Brake Controller of a Human Based on Prediction of Capturability

The dynamic effect of mechanical losses of transmissions on the equation of motion of legged robots

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