Kinematics and dynamics modelling of the biped robot

Bajrami, Xhevahir; Dermaku, Artan; Shala, Ahmet; Likaj, Ramë

doi:10.3182/20130606-3-xk-4037.00032

Cited by 9 publications

(5 citation statements)

References 2 publications

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“…After the target value y(r, s') is calculated, the parameters of critic networks were learned by minimizing the mean square error between Q θ 1 , Q θ 2 and y (r, s ′ ) (5), (6).…”

Section: Reinforcement Learning: Td3 Algorithmmentioning

confidence: 99%

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Control and Simulation of a 6-DOF Biped Robot based on Twin Delayed Deep Deterministic Policy Gradient Algorithm

Khôi¹,

Truong²,

Tan³

2021

IJST

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Objectives:To study an algorithm to control a bipedal robot to walk so that it has a gait close to that of a human. It is known that the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is a highly efficient algorithm with a few changes compared to the popular algorithm -the commonly used Deep Deterministic Policy Gradient (DDPG) in the continuous action space problem in Reinforcement Learning. Methods: Different from the usual sparse reward function model used, in this study, a reward model combined with a sparse reward function and dense reward function will be proposed. The application of the TD3 algorithm together with the proposed reward function model to control a bipedal robot model with 6 degrees of freedom will be presented. The training process is simulated in Gazebo/Robot Operating System (ROS) environment. Finding: The results show that, when choosing a reward model combined with a sparse reward function and a dense reward function suitable for the robot model, will help it learn faster and achieve better results. The biped robot can walk straight with an almost human-like gait. In the paper, the results from the TD3 algorithm combined with the proposed reward model are also compared with the results from other algorithms. Novelty: Applying the TD3 algorithm combined with the proposed reward model for the 6-DOF biped robot and simulating the robot's gait in Gazebo/ROS environment, ROS is a middleware that can be used to control a robot in a real environment in the future.

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“…After the target value y(r, s') is calculated, the parameters of critic networks were learned by minimizing the mean square error between Q θ 1 , Q θ 2 and y (r, s ′ ) (5), (6).…”

Section: Reinforcement Learning: Td3 Algorithmmentioning

confidence: 99%

“…There are many methods used to control biped robots, for traditional solution methods (5,6) , we need to set up the Denavit-Hartenberg table for the robot, kinematic and dynamic analysis, design the motion trajectory for the joints. When robot model is changed, parameters are mostly calculated from beginning.…”

Section: Introductionmentioning

confidence: 99%

Control and Simulation of a 6-DOF Biped Robot based on Twin Delayed Deep Deterministic Policy Gradient Algorithm

Khôi¹,

Truong²,

Tan³

2021

IJST

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“…The first important problem is the dynamics of bipedal robots. Although there have been many studies on the problem of dynamics [8][9][10][11][12][13][14][15][16], because the structure is very diverse for each field of application, this is still an open problem. Normally, Newton-Euler equations and Lagrange equations of the 2nd kind are widely applied when investigating the dynamics of bipedal robots.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic-Based Controller for Bipedal Robot

Khôi

Xuan

2021

Applied Sciences

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In this paper, the problem of controlling a human-like bipedal robot while walking is studied. The control method commonly applied when controlling robots in general and bipedal robots in particular, was based on a dynamical model. This led to the need to accurately define the dynamical model of the robot. The activities of bipedal robots to replace humans, serve humans, or interact with humans are diverse and ever-changing. Accurate determination of the dynamical model of the robot is difficult because it is difficult to fully and accurately determine the dynamical quantities in the differential equations of motion of the robot. Additionally, another difficulty is that because the robot’s operation is always changing, the dynamical quantities also change. There have been a number of works applying fuzzy logic-based controllers and neural networks to control bipedal robots. These methods can overcome to some extent the uncertainties mentioned above. However, it is a challenge to build appropriate rule systems that ensure the control quality as well as the controller’s ability to perform easily and flexibly. In this paper, a method for building a fuzzy rule system suitable for bipedal robot control is proposed. The design of the motion trajectory for the robot according to the human gait and the analysis of dynamical factors affecting the equilibrium condition and the tracking trajectory were performed to provide informational data as well as parameters. Based on that, a fuzzy rule system and fuzzy controller was proposed and built, allowing a determination of the control force/moment without relying on the dynamical model of the robot. For evaluation, an exact controller based on the assumption of an accurate dynamical model, which was a two-feedback loop controller based on integrated inverse dynamics with proportional integral derivative, is also proposed. To confirm the validity of the proposed fuzzy rule system and fuzzy controller, computation and numerical simulation were performed for both types of controllers. Comparison of numerical simulation results showed that the fuzzy rule system and the fuzzy controller worked well. The proposed fuzzy rule system is simple and easy to apply.

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“…The traditional NewtonEuler formulation, which has been widely used in the past [18,19] and is still used for specific tasks by some researchers [20,21], hardly adapts to the particular case of parallel kinematics machines.…”

Section: Introductionmentioning

confidence: 99%

Simplified approach for dynamics estimation of a minor mobility parallel robot

Carbonari

2015

Mechatronics

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Kinematics and dynamics modelling of the biped robot

Cited by 9 publications

References 2 publications

Control and Simulation of a 6-DOF Biped Robot based on Twin Delayed Deep Deterministic Policy Gradient Algorithm

Control and Simulation of a 6-DOF Biped Robot based on Twin Delayed Deep Deterministic Policy Gradient Algorithm

Fuzzy Logic-Based Controller for Bipedal Robot

Simplified approach for dynamics estimation of a minor mobility parallel robot

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