Experimental validation of manipulability optimization control of a 7‐DoF serial manipulator for robot‐assisted surgery
Purpose Both safety and accuracy are of vital importance for surgical operation procedures. An efficient way to avoid the singularity of the surgical robot concerning safety issues is to maximize its manipulability in robot‐assisted surgery. The goal of this work was to validate a dynamic neural net...
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Published in | The international journal of medical robotics + computer assisted surgery Vol. 17; no. 1; pp. 1 - 11 |
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Main Authors | , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
England
Wiley Subscription Services, Inc
01.02.2021
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Abstract | Purpose
Both safety and accuracy are of vital importance for surgical operation procedures. An efficient way to avoid the singularity of the surgical robot concerning safety issues is to maximize its manipulability in robot‐assisted surgery. The goal of this work was to validate a dynamic neural network optimization method for manipulability optimization control of a 7‐degree of freedom (DoF) robot in a surgical operation.
Methods
Three different paths, a circle, a sinusoid and a spiral were chosen to simulate typical surgical tasks. The dynamic neural network‐based manipulability optimization control was implemented on a 7‐DoF robot manipulator. During the surgical operation procedures, the manipulability of the robot manipulator and the accuracy of the surgical operation are recorded for performance validation.
Results
By comparison, the dynamic neural network‐based manipulability optimization control achieved optimized manipulability but with a loss of the accuracy of trajectory tracking (the global error was 1 mm compare to the 0.5 mm error of non‐optimized method).
Conclusions
The method validated in this work achieved optimized manipulability with a loss of error. Future works should be introduced to improve the accuracy of the surgical operation. |
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AbstractList | Abstract
Purpose
Both safety and accuracy are of vital importance for surgical operation procedures. An efficient way to avoid the singularity of the surgical robot concerning safety issues is to maximize its manipulability in robot‐assisted surgery. The goal of this work was to validate a dynamic neural network optimization method for manipulability optimization control of a 7‐degree of freedom (DoF) robot in a surgical operation.
Methods
Three different paths, a circle, a sinusoid and a spiral were chosen to simulate typical surgical tasks. The dynamic neural network‐based manipulability optimization control was implemented on a 7‐DoF robot manipulator. During the surgical operation procedures, the manipulability of the robot manipulator and the accuracy of the surgical operation are recorded for performance validation.
Results
By comparison, the dynamic neural network‐based manipulability optimization control achieved optimized manipulability but with a loss of the accuracy of trajectory tracking (the global error was 1
mm
compare to the 0.5
mm
error of non‐optimized method).
Conclusions
The method validated in this work achieved optimized manipulability with a loss of error. Future works should be introduced to improve the accuracy of the surgical operation. PurposeBoth safety and accuracy are of vital importance for surgical operation procedures. An efficient way to avoid the singularity of the surgical robot concerning safety issues is to maximize its manipulability in robot‐assisted surgery. The goal of this work was to validate a dynamic neural network optimization method for manipulability optimization control of a 7‐degree of freedom (DoF) robot in a surgical operation.MethodsThree different paths, a circle, a sinusoid and a spiral were chosen to simulate typical surgical tasks. The dynamic neural network‐based manipulability optimization control was implemented on a 7‐DoF robot manipulator. During the surgical operation procedures, the manipulability of the robot manipulator and the accuracy of the surgical operation are recorded for performance validation.ResultsBy comparison, the dynamic neural network‐based manipulability optimization control achieved optimized manipulability but with a loss of the accuracy of trajectory tracking (the global error was 1 mm compare to the 0.5 mm error of non‐optimized method).ConclusionsThe method validated in this work achieved optimized manipulability with a loss of error. Future works should be introduced to improve the accuracy of the surgical operation. Both safety and accuracy are of vital importance for surgical operation procedures. An efficient way to avoid the singularity of the surgical robot concerning safety issues is to maximize its manipulability in robot-assisted surgery. The goal of this work was to validate a dynamic neural network optimization method for manipulability optimization control of a 7-degree of freedom (DoF) robot in a surgical operation. Three different paths, a circle, a sinusoid and a spiral were chosen to simulate typical surgical tasks. The dynamic neural network-based manipulability optimization control was implemented on a 7-DoF robot manipulator. During the surgical operation procedures, the manipulability of the robot manipulator and the accuracy of the surgical operation are recorded for performance validation. By comparison, the dynamic neural network-based manipulability optimization control achieved optimized manipulability but with a loss of the accuracy of trajectory tracking (the global error was 1 mm compare to the 0.5 mm error of non-optimized method). The method validated in this work achieved optimized manipulability with a loss of error. Future works should be introduced to improve the accuracy of the surgical operation. Purpose Both safety and accuracy are of vital importance for surgical operation procedures. An efficient way to avoid the singularity of the surgical robot concerning safety issues is to maximize its manipulability in robot‐assisted surgery. The goal of this work was to validate a dynamic neural network optimization method for manipulability optimization control of a 7‐degree of freedom (DoF) robot in a surgical operation. Methods Three different paths, a circle, a sinusoid and a spiral were chosen to simulate typical surgical tasks. The dynamic neural network‐based manipulability optimization control was implemented on a 7‐DoF robot manipulator. During the surgical operation procedures, the manipulability of the robot manipulator and the accuracy of the surgical operation are recorded for performance validation. Results By comparison, the dynamic neural network‐based manipulability optimization control achieved optimized manipulability but with a loss of the accuracy of trajectory tracking (the global error was 1 mm compare to the 0.5 mm error of non‐optimized method). Conclusions The method validated in this work achieved optimized manipulability with a loss of error. Future works should be introduced to improve the accuracy of the surgical operation. |
Author | Mira, Robert Mihai Hu, Yingbai Ferrigno, Giancarlo Danioni, Andrea Li, Jiehao Ungari, Matteo De Momi, Elena Su, Hang Zhou, Xuanyi |
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Both safety and accuracy are of vital importance for surgical operation procedures. An efficient way to avoid the singularity of the surgical robot... Both safety and accuracy are of vital importance for surgical operation procedures. An efficient way to avoid the singularity of the surgical robot concerning... Abstract Purpose Both safety and accuracy are of vital importance for surgical operation procedures. An efficient way to avoid the singularity of the surgical... PurposeBoth safety and accuracy are of vital importance for surgical operation procedures. An efficient way to avoid the singularity of the surgical robot... PURPOSEBoth safety and accuracy are of vital importance for surgical operation procedures. An efficient way to avoid the singularity of the surgical robot... |
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SubjectTerms | Accuracy Degrees of freedom Errors manipulability Manipulators Network management systems Neural networks Optimization redundant robot Robot arms Robotic surgery Robots robot‐assisted surgery Safety Sine waves trajectory tracking |
Title | Experimental validation of manipulability optimization control of a 7‐DoF serial manipulator for robot‐assisted surgery |
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