Contact Force/Torque Control Based on Viscoelastic Model for Stable Bipedal Walking on Indefinite Uneven Terrain

Li, Qingqing; Yu, Zhangguo; Zhou, Qinqin; Meng, Libo

doi:10.1109/tase.2019.2903564

Cited by 34 publications

(7 citation statements)

References 32 publications

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“…(2) Motion robustness: when the system needs to consider a large external disturbance as it is designed, the motion pattern needs to undergo a large change to achieve real-time stability recovery [ 9 ]. However, in existing research methods, the generalization and robustness of robot motion are often designed separately, without effective combination [ 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Learning 3D Bipedal Walking with Planned Footsteps and Fourier Series Periodic Gait Planning

Wang

Piao

Leng

et al. 2023

Sensors

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Reinforcement learning provides a general framework for achieving autonomy and diversity in traditional robot motion control. Robots must walk dynamically to adapt to different ground environments in complex environments. To achieve walking ability similar to that of humans, robots must be able to perceive, understand and interact with the surrounding environment. In 3D environments, walking like humans on rugged terrain is a challenging task because it requires complex world model generation, motion planning and control algorithms and their integration. So, the learning of high-dimensional complex motions is still a hot topic in research. This paper proposes a deep reinforcement learning-based footstep tracking method, which tracks the robot’s footstep position by adding periodic and symmetrical information of bipedal walking to the reward function. The robot can achieve robot obstacle avoidance and omnidirectional walking, turning, standing and climbing stairs in complex environments. Experimental results show that reinforcement learning can be combined with real-time robot footstep planning, avoiding the learning of path-planning information in the model training process, so as to avoid the model learning unnecessary knowledge and thereby accelerate the training process.

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Section: Introductionmentioning

confidence: 99%

Learning 3D Bipedal Walking with Planned Footsteps and Fourier Series Periodic Gait Planning

Wang

Piao

Leng

et al. 2023

Sensors

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“…Damping components can also be added in the contact model [24], [25], but the main assumption remains that the robot makes contact with the environment at isolated points, not on surfaces. Still using lumped parameters, another approach to model soft contact surfaces consists in assuming that the contact interaction can be characterized by equivalent springs and rotational dampers, which can then be employed for robot control [26], [27]. This approach leads to the problem of giving a physical meaning to the contact parameters associated with the torque in the contact wrench.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Visco-Elastic Environments for Humanoid Robot Motion Control

Romualdi

Dafarra

Pucci

2021

IEEE Robot. Autom. Lett.

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This manuscript presents a model of compliant contacts for time-critical humanoid robot motion control. The proposed model considers the environment as a continuum of spring-damper systems, which allows us to compute the equivalent contact force and torque that the environment exerts on the contact surface. We show that the proposed model extends the linear and rotational springs and dampers -classically used to characterize soft terrains -to the case of large contact surface orientations. The contact model is then used for the real-time whole-body control of humanoid robots walking on visco-elastic environments. The overall approach is validated by simulating walking motions of the iCub humanoid robot. Furthermore, the paper compares the proposed whole-body control strategy and state of the art approaches. In this respect, we investigate the terrain compliance that makes the classical approaches assuming rigid contacts fail. We finally analyze the robustness of the presented control design with respect to non-parametric uncertainty in the contact-model.

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“…Robot-walking control on a specific surface requires accurate detection of the surface-profile information [11][12][13][14][15][16][17][18]. Most of the development in this field extensively utilizes specific measured terrain information to stabilize the walking pattern [14][15].…”

Section: Introductionmentioning

confidence: 99%

“…Typically, the environment information is collected by cameras or similar vision-based sensors [19][20]. Therefore, the case of a "blind" humanoid robot, without any camera to sense the environment, can be a worst-case scenario for robot-stability control [16][17][18]. In such a case, the surface interaction with force/torque sensors, integrated under the robot feet [16], becomes an important information source for assuring robustness and stability of the robot.…”

Section: Introductionmentioning

confidence: 99%

“…In summary, the balancing of the light humanoid robots, cannot be efficiently realized using such previously proposed dynamic walking-pattern control. Further, the balancing of light-weight robots on an inclined surface is more difficult than that of a heavy-weight robot [15][16][17][18]. Under observation of the scalability rules [40], very few investigations on balancing light-weight humanoid robots on an inclined surface have been reported [41][42].…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Sensor-Based Real-Time Balancing of Humanoid Robots on Inclined Surfaces

et al. 2020

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An experimental and theoretical study of real-time robot balancing on inclined surfaces with electrical feedback circuitry is presented. Force sensors are experimentally shown to extend the sustainability of a stable robot posture beyond a critical surface inclination. For this purpose, the inclination feedback from the force sensors is used to adjust the robot's ankle-pitch-motor angle above the critical inclination, thus enabling the maintenance of a stable robot posture. Further, the Inverted Pendulum Model (IPM) [43-45] is extended to the case of inclined surfaces. Through application of this extended IPM it is demonstrated, that simultaneous use of gyro-sensor data can minimize the necessary initial adjustment of the motor angle for controlled robot-body rotation, which additionally has the positive effect of reducing possible overshoots of the motor's rotation angle during feedback. Consequently, the reported feedback control improves the robotbody stability on inclined surfaces. Efficient implementation of the developed control scheme into an existing robot's electrical system is proposed. INDEX TERMS Balanced walking control, force sensor, gyro sensor, humanoid robot, servo motor.

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Contact Force/Torque Control Based on Viscoelastic Model for Stable Bipedal Walking on Indefinite Uneven Terrain

Cited by 34 publications

References 32 publications

Learning 3D Bipedal Walking with Planned Footsteps and Fourier Series Periodic Gait Planning

Learning 3D Bipedal Walking with Planned Footsteps and Fourier Series Periodic Gait Planning

Modeling of Visco-Elastic Environments for Humanoid Robot Motion Control

Analysis of Sensor-Based Real-Time Balancing of Humanoid Robots on Inclined Surfaces

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