Development of a Neural Network Method to Estimate the Workspace of a Parallel Robot

Boantă, Cătălin; Brişan, Cornel

doi:10.1109/aqtr55203.2022.9801990

Cited by 2 publications

(4 citation statements)

References 19 publications

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“… General topology of a multilayer fully connected neural network (reprinted with permission from ref. [ 23 ]. 2022, IEEE Proceedings).…”

Section: Figurementioning

confidence: 99%

“… Parallel robot composed of 6 identical RUS open loops (adapted with permission from ref. [ 23 ]. 2022, IEEE Proceedings).…”

Section: Figurementioning

confidence: 99%

“… The fixed ( a ) and mobile ( b ) platforms of the parallel robot (adapted with permission from ref. [ 23 ]. 2022, IEEE Proceedings).…”

Section: Figurementioning

confidence: 99%

“…The work presented in this paper is an extended version of [ 23 ] and presents an artificial neural network-based method (a feedforward neural network) that is used to estimate the kinematics and the volume of the workspace of a robot, considering as inputs the parameters that describe the architecture of the robot and the pose of the end effector. The proposed framework is applied on a six-degrees-of-freedom parallel manipulator with rotational active joints.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of the Kinematics and Workspace of a Robot Using Artificial Neural Networks

Boantă

Brişan

2022

Sensors

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At present, in specific and complex industrial operations, robots have to respect certain requirements and criteria as high kinematic or dynamic performance, specific dimensions of the workspace, or limitation of the dimensions of the mobile elements of the robot. In order to respect these criteria, a proper design of the robots has to be achieved, which requires years of practice and a proper knowledge and experience of a human designer. In order to assist the human designer in the process of designing the robots, several methods (including optimization methods) have been developed. The scientific problem addressed in this paper is the development of an artificial intelligence method to estimate the size of the workspace and the kinematics of a robot using a feedforward neural network. The method is applied on a parallel robot composed of a base platform, a mobile platform and six kinematic rotational-universal-spherical open loops. The numerical results show that, with proper training and topology, a feedforward neural network is able to estimate properly values of the volume of the workspace and the values of the generalized coordinates based on the pose of the end effector.

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“… General topology of a multilayer fully connected neural network (reprinted with permission from ref. [ 23 ]. 2022, IEEE Proceedings).…”

Section: Figurementioning

confidence: 99%

“… Parallel robot composed of 6 identical RUS open loops (adapted with permission from ref. [ 23 ]. 2022, IEEE Proceedings).…”

Section: Figurementioning

confidence: 99%

“… The fixed ( a ) and mobile ( b ) platforms of the parallel robot (adapted with permission from ref. [ 23 ]. 2022, IEEE Proceedings).…”

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimation of the Kinematics and Workspace of a Robot Using Artificial Neural Networks

Boantă

Brişan

2022

Sensors

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Research trends on kinematics analysis of surgical robotics using machine learning techniques

Jeong,

2024

J Innovative Medical Technology

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Due to the remarkable development of artificial intelligence technology, a lot of efforts are being actively made to utilize it in robotics field. Medical robots are being developed in various types such as serial robots, parallel robots, and continuous robots, taking into account the patient's anatomy and surgical method. For the precise control, researches are being conducted on accurate kinematic modeling and advanced control algorithms. However, there are limitations in applying traditional numerical approaches to some robots due to their high nonlinearity. Recently, learning-based techniques for kinematics analysis have been reported, demonstrating their potential as a promising solution to overcome nonlinearity. In this paper, we analyze the scope of application and research trends of artificial intelligence technology used in serial robots, parallel robots, and continuous robots, and discuss the possibility of learning-based control in medical robots and future research directions.

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Development of a Neural Network Method to Estimate the Workspace of a Parallel Robot

Cited by 2 publications

References 19 publications

Estimation of the Kinematics and Workspace of a Robot Using Artificial Neural Networks

Estimation of the Kinematics and Workspace of a Robot Using Artificial Neural Networks

Research trends on kinematics analysis of surgical robotics using machine learning techniques

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