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

Xi, Ao; Chen, Chao

doi:10.3390/s20164468

Cited by 5 publications

(5 citation statements)

References 33 publications

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“…Cristyan et al [11] proposed a multilevel system and used the Q-learning method in this system to make the NAO robot walk quickly in simulation. Ao et al [12] proposed a hybrid reinforcement learning method to keep the NAO robot balanced on an oscillation platform with different frequencies and amplitudes in the simulation. Takamitsu et al [13] proposed a learning framework for central pattern generation (CPG)-based biped locomotion with a policy gradient method and applied it to real robots.…”

Section: Biped Robot Controlled By Drlmentioning

confidence: 99%

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A Disturbance Rejection Control Method Based on Deep Reinforcement Learning for a Biped Robot

et al. 2021

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The disturbance rejection performance of a biped robot when walking has long been a focus of roboticists in their attempts to improve robots. There are many traditional stabilizing control methods, such as modifying foot placements and the target zero moment point (ZMP), e.g., in model ZMP control. The disturbance rejection control method in the forward direction of the biped robot is an important technology, whether it comes from the inertia generated by walking or from external forces. The first step in solving the instability of the humanoid robot is to add the ability to dynamically adjust posture when the robot is standing still. The control method based on the model ZMP control is among the main methods of disturbance rejection for biped robots. We use the state-of-the-art deep-reinforcement-learning algorithm combined with model ZMP control in simulating the balance experiment of the cart–table model and the disturbance rejection experiment of the ASIMO humanoid robot standing still. Results show that our proposed method effectively reduces the probability of falling when the biped robot is subjected to an external force in the x-direction.

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Section: Biped Robot Controlled By Drlmentioning

confidence: 99%

“…The purpose of this review is to improve the estimation of the value function. The loss function is constructed as loss = Q − V(s t ; w) 2 (12) and the single-step update w is…”

Section: Policy Trainingmentioning

confidence: 99%

A Disturbance Rejection Control Method Based on Deep Reinforcement Learning for a Biped Robot

et al. 2021

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“…We also implemented the velocity loop control for experiments. We applied the PI controller of Equation (15) to simplify hardware implementation, because it provided similar responses to the standard robust controller but with a much simpler form. We set the TWIP system at the two locations (see Figure 5a) with an initial angle of ψ(0) = −9.5 • and a reference velocity V re f = 0.…”

Section: The Velocity Loop Controlmentioning

confidence: 99%

“…Liu et al [14] proposed Machines 2021, 9, 205 2 of 21 a real-time balance control for a small-sized biped robot and applied a gyroscope and an accelerometer to detect the robot inclination and balance it when being pushed. Xi and Chen [15] applied inverse kinematics and reinforcement learning to balance a biped robot on an oscillating platform.…”

Section: Introductionmentioning

confidence: 99%

Decoupled Multi-Loop Robust Control for a Walk-Assistance Robot Employing a Two-Wheeled Inverted Pendulum

et al. 2021

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This paper develops a decoupled multi-loop control for a two-wheeled inverted pendulum (TWIP) robot that can assist user’s with walking. The TWIP robot is equipped with two wheels driven by electrical motors. We derive the system’s transfer function and design a robust loop-shaping controller to balance the system. The simulation and experimental results show that the TWIP system can be balanced but might experience velocity drifts because its balancing point is affected by model variations and disturbances. Therefore, we propose a multi-loop control layout consisting of a velocity loop and a position loop for the TWIP robot. The velocity loop can adjust the balancing point in real-time and regulate the forward velocity, while the position loop can achieve position tracking. For walking assistance, we design a decoupled control structure that transfers the linear and rotational motions of the robot to the commands of two parallel motors. We implement the designed controllers for simulation and experiments and show that the TWIP system employing the proposed decoupled multi-loop control can provide satisfactory responses when assisting with walking.

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“…In [32], the analytical procedure is extended. See [33][34][35][36][37][38] for further information on bipedal robot balance or stability control. As a result, the current study proposes a high-level control system to maintain the ZMP trajectory within a stable zone while following the intended COM references.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Stabilization and Tracking Control of a Flexible-Joint Bipedal Robot Based on Anti-Windup and Adaptive Approximation Control

Al-Shuka,

Kaleel,

Al-Bakri

2024

Journal of Robotics

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Bipedal robotic mechanisms are unstable due to the unilateral contact passive joint between the sole and the ground. Hierarchical control layers are crucial for creating walking patterns, stabilizing locomotion, and ensuring correct angular trajectories for bipedal joints due to the system’s various degrees of freedom. This work provides a hierarchical control scheme for a bipedal robot that focuses on balance (stabilization) and low-level tracking control while considering flexible joints. The stabilization control method uses the Newton–Euler formulation to establish a mathematical relationship between the zero-moment point (ZMP) and the center of mass (COM), resulting in highly nonlinear and coupled dynamic equations. Adaptive approximation-based feedback linearization control (so-called adaptive computed torque control) combined with an anti-windup compensator is designed to track the desired COM produced by the high-level command. Along the length of the support sole, the ZMP with physical restrictions serves as the control input signal. The viability of the suggested controller is established using Lyapunov’s theory. The low-level control tracks the intended joint movements for a bipedal mechanism with flexible joints. We use two control strategies: position-based adaptive approximation control and cascaded position-torque adaptive approximation control (cascaded PTAAC). The interesting point is that the cascaded PTAAC can be extended to deal with variable impedance robotic joints by using the required velocity concept, including the desired velocity and terms related to control errors such as position, force, torque, or impedance errors if needed. A 6-link bipedal robot is used in simulation and validation experiments to demonstrate the viability of the suggested control structure.

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

Cited by 5 publications

References 33 publications

A Disturbance Rejection Control Method Based on Deep Reinforcement Learning for a Biped Robot

A Disturbance Rejection Control Method Based on Deep Reinforcement Learning for a Biped Robot

Decoupled Multi-Loop Robust Control for a Walk-Assistance Robot Employing a Two-Wheeled Inverted Pendulum

Hierarchical Stabilization and Tracking Control of a Flexible-Joint Bipedal Robot Based on Anti-Windup and Adaptive Approximation Control

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