Fuzzy-based-admittance controller for safe natural human–robot interaction

Toan, Nguyen Van; Khôi, Phan Bùi

doi:10.1080/01691864.2019.1607551

Cited by 13 publications

(7 citation statements)

References 17 publications

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“…Finally, this method can be used to solve the problem of vibration during cooperative manipulation. In order to solve the problem that the identification of the inertia and damping matrix in the dynamic admittance model is very time-consuming, a fuzzy-based admittance control is proposed in [14]. The controller directly calculates the speed of the end effector through an external wrench (force/torque) and the power transmitted by the robot.…”

Section: Model-based Control and Model-free Controlmentioning

confidence: 99%

Model-Based and Model-Free Robot Control: A Review

Zhang

Liu

2021

Lecture Notes in Mechanical Engineering

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Robot control is one of the key aspects of robotics research. Models are essential tools in robotics, such as robot's own body dynamics and kinematics models, actuator/motor models, and the models of external controllable objects. In this paper, we review the latest advances in model-based and model-free approaches with a strong focus on robot control. Based on the designed search strategy, several prevailing control approaches are classified and discussed according to their control strategies. An insight into the gripper control is also explored. Then the research problems and applicability of the control methods are discussed by investigating their merits and demerits. Based on the discussion, we summarize the challenges and future research trends of robot control.

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Section: Model-based Control and Model-free Controlmentioning

confidence: 99%

Model-Based and Model-Free Robot Control: A Review

Zhang

Liu

2021

Lecture Notes in Mechanical Engineering

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“…In order to solve the problem of fine-tuning the parameters of the kinematic model, they chose to use a data-driven learning method to complete the task of fine-tuning the parameters. Also in ( Toan & Khoi, 2019 ), researchers used the pick and place task to test a fuzzy-based admittance controller. In Kang et al (2019) , it is also about the controller design of human-robot interaction.…”

Section: Related Workmentioning

confidence: 99%

“…In order to achieve the purpose of benchmarking the controller, the task of benchmarking is to simulate pick and place ( Nabat et al, 2005 ; Sugiarto & Conradt, 2017 ; Toan & Khoi, 2019 ). Four poses are designed in the experiment, three poses are used to simulate pick-and-place actions, and one initial pose is used to determine whether the controller can control the robotic arm to return to the initial state.…”

Section: Introductionmentioning

confidence: 99%

Control and benchmarking of a 7-DOF robotic arm using Gazebo and ROS

Zhang¹,

Liu²

2021

PeerJ Computer Science

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The robot controller plays an important role in controlling the robot. The controller mainly aims to eliminate or suppress the influence of uncertain factors on the control robot. Furthermore, there are many types of controllers, and different types of controllers have different features. To explore the differences between controllers of the same category, this article studies some controllers from basic controllers and advanced controllers. This article conducts the benchmarking of the selected controller through pre-set tests. The test task is the most commonly used pick and place. Furthermore, to complete the robustness test, a task of external force interference is also set to observe whether the controller can control the robot arm to return to a normal state. Subsequently, the accuracy, control efficiency, jitter and robustness of the robot arm controlled by the controller are analyzed by comparing the Position and Effort data. Finally, some future works of the benchmarking and reasonable improvement methods are discussed.

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“…Human intentions can also be assessed in the cooperative task space as presented by Sonny Tarbouriech et al (2019). Toan and Khoi (2019) present an alternative admittance controller based on inference mechanism of fuzzy logic to eliminate the identification of inertia and damping matrices during the process of controller formulation in which the end-effectors velocity is adaptively adjusted via external wrench and power transmitted by the robot. Learning skills from human demonstrations as presented by Yang et al (2018aYang et al ( , 2018b and Bian et al (2019) is another promising method for variable admittance control.…”

Section: Related Workmentioning

confidence: 99%

Dynamical system based variable admittance control for physical human-robot interaction

Bian

Ren²,

et al. 2020

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Purpose The purpose of this paper is to enable robots to intelligently adapt their damping characteristics and motions in a reactive fashion toward human inputs and task requirements during physical human–robot interaction. Design/methodology/approach This paper exploits a combination of the dynamical system and the admittance model to create robot behaviors. The reference trajectories are generated by dynamical systems while the admittance control enables robots to compliantly follow the reference trajectories. To determine how control is divided between the two models, a collaborative arbitration algorithm is presented to change their contributions to the robot motion based on the contact forces. In addition, the authors investigate to model the robot’s impedance characteristics as a function of the task requirements and build a novel artificial damping field (ADF) to represent the virtual damping at arbitrary robot states. Findings The authors evaluate their methods through experiments on an UR10 robot. The result shows promising performances for the robot to achieve complex tasks in collaboration with human partners. Originality/value The proposed method extends the dynamical system approach with an admittance control law to allow a robot motion being adjusted in real time. Besides, the authors propose a novel ADF method to model the robot’s impedance characteristics as a function of the task requirements.

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Fuzzy-based-admittance controller for safe natural human–robot interaction

Cited by 13 publications

References 17 publications

Model-Based and Model-Free Robot Control: A Review

Model-Based and Model-Free Robot Control: A Review

Control and benchmarking of a 7-DOF robotic arm using Gazebo and ROS

Dynamical system based variable admittance control for physical human-robot interaction

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