Personalized Variable Gain Control With Tremor Attenuation for Robot Teleoperation

Yang, Chenguang; Luo, Jing; Pan, Yongping; Liu, Zhi; Su, Chun‐Yi

doi:10.1109/tsmc.2017.2694020

Cited by 159 publications

(99 citation statements)

References 59 publications

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“…24,25 The FPGA is used in our system as a primary robot control system. This controller is responsible for handling all the control routines and algorithms which need real-time capability such as, Acquiring the robot internal parameter; Executing the trajectory algorithm for his current process; Detecting contact/collision; Executing collision/obstacle avoidance algorithms; Executing human-robot collaboration mode algorithm.…”

Section: Field Programmable Gate Arraymentioning

confidence: 99%

A universal algorithm for sensorless collision detection of robot actuator faults

Chen

Luo

2018

Advances in Mechanical Engineering

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When a robot is working properly, it is possible to collide with people or objects entering its working space. This research is different than usual control algorithm. It proposes a universal algorithm for sensorless collision detection of robot actuator faults to enhance the security of the robot. On the basis of the dynamic model, a classical friction model to ensure the accuracy of the whole dynamic model is introduced. This collision detection algorithm can conduct without any external sensors or acceleration and realize the real-time detection just needs to measure the motor current and the location information from the encoder of the robot joint. The value of external torque t ext was used to compare with the threshold to detect the collision. After using the proposed collision detection method, the two rotational (2R) planar manipulators can detect the slight collision reliably. The experimental results and performance comparisons show that this sensorless collision detection algorithm is simple and effective. It can be promoted to any other type of robot arm with more degrees of freedom.

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Section: Field Programmable Gate Arraymentioning

confidence: 99%

A universal algorithm for sensorless collision detection of robot actuator faults

Chen

Luo

2018

Advances in Mechanical Engineering

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“…According to the characteristics of information transmission, neural networks can be divided into feedforward and feedback. The BP neural networks are feedforward networks which are based on error backpropagation algorithm [5][6][7].…”

Section: The Bp Neuralmentioning

confidence: 99%

Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment

Song

Duan

2018

Complexity

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The centralized Kalman filter is always applied in the velocity and attitude matching of Transfer Alignment (TA). But the centralized Kalman has many disadvantages, such as large amount of calculation, poor real-time performance, and low reliability. In the paper, the federal Kalman filter (FKF) based on neural networks is used in the velocity and attitude matching of TA, the Kalman filter is adjusted by the neural networks in the two subfilters, the federal filter is used to fuse the information of the two subfilters, and the global suboptimal state estimation is obtained. The result of simulation shows that the federal Kalman filter based on neural networks is better in estimating the initial attitude misalignment angle of inertial navigation system (INS) when the system dynamic model and noise statistics characteristics of inertial navigation system are unclear, and the estimation error is smaller and the accuracy is higher.

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“…With the developments of commercial available robotic arms: iARM 25 (produced by Exact Dynamics Ò ) and JACO 26 (produced by Kinova Ò ), a lot of researchers both at home and abroad carried out a series of studies, such as the FRIEND series robots 27 in the University of Bremen, the WMRA-I and II 28 in the University of South Florida, the wheelchair-mounted robotic manipulators (WMRMs) in Purdue University, 29 and the WMRA system ''WIM'' 3,4 in Waseda University. At present, the researches on the WMRA mainly focus on the following categories: (1) develop a variety of human-robot interaction interfaces that can satisfy the motor ability of different users, particularly for the disables; 1,4,[30][31][32] (2) adopt the visual servo technology or learn from demonstration to control the motion of robotic arm, so as to reduce the physical and mental burden of the users; 27,33,34 (3) design a new structure of robotic arm in order to improve the safety of the user, for the users are in the working space of the robotic arm; 35 and (4) develop the integrated WMRA with two or more above characteristics. To develop the WMRA with the characteristics of high safety, high intelligence, and operational ease is the developing trend in the field of assistive robots.…”

Section: Wmramentioning

confidence: 99%

Learning motion primitives from demonstration

Chi

Yao

Liu

et al. 2017

Advances in Mechanical Engineering

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With the purpose to offer simple and convenient assistance for the elders with disabilities to take care of themselves in activities of daily living, we present a motion primitives learning method based on robot learning from demonstration to improve the intelligence and adaptability of the wheelchair-mounted robotic arm. This method adopts the beta process autoregressive hidden Markov model to segment the demonstrations of related task, acquire the contained motion primitives, and recognize the repeated motion primitives. After that, it adopts the dynamic movement primitives to adjust the related motion primitives according to the task instructions by the wheelchair-mounted robotic arm users, so as to replay the demonstrated task in a new environment. This learning framework is validated on a 6-degree-of-freedom JACO robotic arm, performing the tasks of drinking water from the bottle through a straw and pouring water from the bottle to the cup.

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Personalized Variable Gain Control With Tremor Attenuation for Robot Teleoperation

Cited by 159 publications

References 59 publications

A universal algorithm for sensorless collision detection of robot actuator faults

A universal algorithm for sensorless collision detection of robot actuator faults

Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment

Learning motion primitives from demonstration

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