Research on the Stability of Biped Robot Walking on Different Road Surfaces

Lee, Hai-Wu; Yang, Jinlong; Zhang, Shaoquan; Chen, Qi

doi:10.1109/ickii.2018.8569084

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…The importance of this work lies in the implementation on a NAO robot, where the authors explain that even when omnidirectional walking and preview control have been explored extensively, in many articles, they do not provide detailed working of the walk engine and the results are often based on simulated experiments. Balance on different road surfaces (for example soil, grass, ceramic) was studied in Reference [25]. The authors used fuzzy control theory based on ZMP information from various sensors.…”

Section: Zero Moment Point Approachesmentioning

confidence: 99%

Learning an Efficient Gait Cycle of a Biped Robot Based on Reinforcement Learning and Artificial Neural Networks

Morales¹,

Rufino²

2019

Applied Sciences

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Programming robots for performing different activities requires calculating sequences of values of their joints by taking into account many factors, such as stability and efficiency, at the same time. Particularly for walking, state of the art techniques to approximate these sequences are based on reinforcement learning (RL). In this work we propose a multi-level system, where the same RL method is used first to learn the configuration of robot joints (poses) that allow it to stand with stability, and then in the second level, we find the sequence of poses that let it reach the furthest distance in the shortest time, while avoiding falling down and keeping a straight path. In order to evaluate this, we focus on measuring the time it takes for the robot to travel a certain distance. To our knowledge, this is the first work focusing both on speed and precision of the trajectory at the same time. We implement our model in a simulated environment using q-learning. We compare with the built-in walking modes of an NAO robot by improving normal-speed and enhancing robustness in fast-speed. The proposed model can be extended to other tasks and is independent of a particular robot model.

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Section: Zero Moment Point Approachesmentioning

confidence: 99%

Learning an Efficient Gait Cycle of a Biped Robot Based on Reinforcement Learning and Artificial Neural Networks

Morales¹,

Rufino²

2019

Applied Sciences

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“…Conventional control methods are based on the dynamic model of the robot, which involves the position of center of mass of each joint and each link, the length and width of each link, etc. These methods generate control signals for joints according to the deviation between the desired zero moment point (ZMP) and the measured ZMP [4][5][6][7][8]. The problem of conventional control methods is that they highly rely on the accuracy of the dynamics model.…”

Section: Introductionmentioning

confidence: 99%

Stability Control of a Biped Robot on a Dynamic Platform Based on Hybrid Reinforcement Learning

Chen

2020

Sensors

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In this work, we introduced a novel hybrid reinforcement learning scheme to balance a biped robot (NAO) on an oscillating platform, where the rotation of the platform is considered as the external disturbance to the robot. The platform had two degrees of freedom in rotation, pitch and roll. The state space comprised the position of center of pressure, and joint angles and joint velocities of two legs. The action space consisted of the joint angles of ankles, knees, and hips. By adding the inverse kinematics techniques, the dimension of action space was significantly reduced. Then, a model-based system estimator was employed during the offline training procedure to estimate the dynamics model of the system by using novel hierarchical Gaussian processes, and to provide initial control inputs, after which the reduced action space of each joint was obtained by minimizing the cost of reaching the desired stable state. Finally, a model-free optimizer based on DQN (λ) was introduced to fine tune the initial control inputs, where the optimal control inputs were obtained for each joint at any state. The proposed reinforcement learning not only successfully avoided the distribution mismatch problem, but also improved the sample efficiency. Simulation results showed that the proposed hybrid reinforcement learning mechanism enabled the NAO robot to balance on an oscillating platform with different frequencies and magnitudes. Both control performance and robustness were guaranteed during the experiments.

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Learning-Based Motion Planning of a 14-DOF Biped Robot on 3D Uneven Terrain Containing a Ditch

Kumar

Dutta

2021

Int. J. Human. Robot.

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In this paper, a new method is proposed that integrates the 3D terrain information, ditch geometry, and biped dynamics for motion planning of a 14-DOF biped robot on 3D terrain containing a 3D ditch. The path planning is modeled as a wavefront propagation in Non-uniform medium represented by the Eikonal equation. A speed function expresses the inhomogeneity of uneven terrain. The Eikonal equation is solved by the Fast marching method (FMM) to obtain the global path. Ten Footstep variables (FSVs) characterize one step of walk on a 3D uneven terrain surface. The hip and foot trajectory parameters (HFTPs) are used to construct cubic spline-based hip and foot trajectories of the biped robot. The optimal value of HFTPs is obtained by a Genetic algorithm by minimization of energy and satisfying the constraint of dynamic balance. A walk-dataset is created that contains the optimal trajectories for different FSVs. The generated walk-dataset was used to train the biped to walk on rough terrain using a feed-forward Neural network for making a real-time estimate of optimal HFTPs. Simulation results validate the effectiveness of the proposed method of ditch crossing on different uneven terrains.

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Research on the Stability of Biped Robot Walking on Different Road Surfaces

Cited by 3 publications

References 5 publications

Learning an Efficient Gait Cycle of a Biped Robot Based on Reinforcement Learning and Artificial Neural Networks

Learning an Efficient Gait Cycle of a Biped Robot Based on Reinforcement Learning and Artificial Neural Networks

Stability Control of a Biped Robot on a Dynamic Platform Based on Hybrid Reinforcement Learning

Learning-Based Motion Planning of a 14-DOF Biped Robot on 3D Uneven Terrain Containing a Ditch

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