A Control Scheme for Physical Human-Robot Interaction Coupled with an Environment of Unknown Stiffness

Li, Hsieh-Yu; Dharmawan, Audelia Gumarus; Paranawithana, Ishara; Yang, Limin; Tan, U-Xuan

doi:10.1007/s10846-020-01176-2

Cited by 23 publications

(11 citation statements)

References 51 publications

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“…Furthermore, variable admittance control can be adopted in human-robot cooperation tasks by means of learning Aspragathos, 2014, 2015). Finally, Li et al (2020) consider the robot to be in contact with both human and environment, similar to the condition considered in this paper.…”

Section: Related Workmentioning

confidence: 99%

Quick Setup of Force-Controlled Industrial Gluing Tasks Using Learning From Demonstration

2021

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This paper presents a framework for programming in-contact tasks using learning by demonstration. The framework is demonstrated on an industrial gluing task, showing that a high quality robot behavior can be programmed using a single demonstration. A unified controller structure is proposed for the demonstration and execution of in-contact tasks that eases the transition from admittance controller for demonstration to parallel force/position control for the execution. The proposed controller is adapted according to the geometry of the task constraints, which is estimated online during the demonstration. In addition, the controller gains are adapted to the human behavior during demonstration to improve the quality of the demonstration. The considered gluing task requires the robot to alternate between free motion and in-contact motion; hence, an approach for minimizing contact forces during the switching between the two situations is presented. We evaluate our proposed system in a series of experiments, where we show that we are able to estimate the geometry of a curved surface, that our adaptive controller for demonstration allows users to achieve higher accuracy in a shorter demonstration duration when compared to an off-the-shelf controller for teaching implemented on a collaborative robot, and that our execution controller is able to reduce impact forces and apply a constant process force while adapting to the surface geometry.

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Section: Related Workmentioning

confidence: 99%

Quick Setup of Force-Controlled Industrial Gluing Tasks Using Learning From Demonstration

2021

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“…According to equation (6), the mapping relationship between joints and MP can be obtained by the following expression…”

Section: Pose and Differential Kinematicsmentioning

confidence: 99%

“…3,4 In industry, collaborative robots are combined with control strategies and variable stiffness components to improve the robustness and safety of manufacturing and assembly processes. 5,6 In essence, in such applications, the bandwidth limitation can be overcome by adjusting the natural frequencies of the system, improving the regulating ability of the external environment. 7 The task of industrial robots requires large-load and high-precision, reducing vibration by natural frequencies modulation has great significance for improving both machining stability and product quality.…”

Section: Introductionmentioning

confidence: 99%

Natural frequency modulation of a kinematically redundant planar parallel robot

Xie

Zhang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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When a parallel robot is equipped with kinematic redundancy, it has sufficient capabilities of natural frequency modulation through adjusting geometric configuration. To reduce resonance of a mechanism, this paper investigates the natural frequency modulation of a kinematically redundant planar parallel robot. A double-threshold searching method is proposed for controlling the inverse kinematics solution and keeping the natural frequencies away from the excitation frequency. The effectiveness of modulating the natural frequencies is demonstrated by comparing it with a non-modulation method. The simulation results indicate that, in all directions, the responses are coupled, and every order should be taken into consideration during natural frequency modulation. Compared to the non-modulation method, the proposed method can reduce the resonance amplitude to a certain extent, and the effect of vibration suppression is remarkable.

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“…The impedance control has been further developed in many works. A variable admittance control method based on human intention, environmental force, and environment stiffness was proposed by Li et al to address pHRI which was coupled with an environment of unknown stiffness [10]. Losey et al used physical human interventions to locally or globally modify the trajectory in real time based on optimal control and gradient descent methods LQR control, which is robust to noisy and unexpected human corrections [11].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Active Control With Human Intention Detection of an Upper-Limb Cable-Driven Rehabilitation Robot

et al. 2020

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Rehabilitation robots play an increasingly important role in the recovery of motor function for stroke. To ensure a natural physical human-robot interaction (pHRI) and enhance the active participation of subjects, it is necessary for the robots to understand the human intention and cooperate actively with humanlike characteristics. This study proposed a hybrid active control algorithm with human motion intention detection. The motion intention was defined as the desired position and velocity, which were continuously estimated according to the human upper-limb model and minimum jerk model, respectively. The motion intention was then fed into a hybrid force and position controller of an upper-limb cable driven rehabilitation robot (CDRR). And a three-dimensional reaching task without predefined trajectory was employed to validate the effectiveness of the proposed control algorithm. Experimental results showed that the control algorithm could continuously recognize the human motion intention and enabled the robot better movement performance indicated as smaller offset error, smoother trajectory, and lower impact. The proposed method could guarantee a natural pHRI and improve the engagement of the subjects, which has great potential in clinical applications.

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A Control Scheme for Physical Human-Robot Interaction Coupled with an Environment of Unknown Stiffness

Cited by 23 publications

References 51 publications

Quick Setup of Force-Controlled Industrial Gluing Tasks Using Learning From Demonstration

Quick Setup of Force-Controlled Industrial Gluing Tasks Using Learning From Demonstration

Natural frequency modulation of a kinematically redundant planar parallel robot

Hybrid Active Control With Human Intention Detection of an Upper-Limb Cable-Driven Rehabilitation Robot

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