2020
DOI: 10.3390/app10051685
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Abstract: This study presents a method of controlling robots based on fuzzy logic to eliminate the effect of uncertainties that are generated by the cutting forces in milling process. The common method to control industrial robots is based on the robot dynamic model and the differential equations of motion to compute the control values. The quantities in the differential equations of the motion of robots are complex and difficult to determine fully and accurately. The interaction forces between the cutting tool and the … Show more

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Cited by 14 publications
(10 citation statements)
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“…Thus, the proposed robot often encounters sensor inaccuracies where the decisions have to be made while coping with the sensor uncertainties. Fuzzy logic is effective for decision making based on such inaccurate sensor measurements [ 29 , 30 ]; The environment inside a false ceiling is uncertain due to the inclusion of cluttered objects such as cable trunks, air ducts, and runners. Fuzzy logic has been proven to be effective in navigating such uncertain environments [ 31 , 32 ]; Expert knowledge can easily be modeled using fuzzy logic [ 33 , 34 ].…”
Section: Perimeter-following Controllermentioning
confidence: 99%
See 1 more Smart Citation
“…Thus, the proposed robot often encounters sensor inaccuracies where the decisions have to be made while coping with the sensor uncertainties. Fuzzy logic is effective for decision making based on such inaccurate sensor measurements [ 29 , 30 ]; The environment inside a false ceiling is uncertain due to the inclusion of cluttered objects such as cable trunks, air ducts, and runners. Fuzzy logic has been proven to be effective in navigating such uncertain environments [ 31 , 32 ]; Expert knowledge can easily be modeled using fuzzy logic [ 33 , 34 ].…”
Section: Perimeter-following Controllermentioning
confidence: 99%
“…Thus, the proposed robot often encounters sensor inaccuracies where the decisions have to be made while coping with the sensor uncertainties. Fuzzy logic is effective for decision making based on such inaccurate sensor measurements [ 29 , 30 ];…”
Section: Perimeter-following Controllermentioning
confidence: 99%
“…Fuzzy logic has a high power of cointensive precisiation, which is essential for the formalization of scientific concepts in human-centric fields [39]. In addition to that, fuzzy logic has proven to be effective in coping with dilemmas that consist of imprecise and incomplete process dynamics and data [40]- [42].…”
Section: Interpretation Of User Preferencementioning
confidence: 99%
“…Furthermore, the sensory information retrieved from the range sensors of the robot is imprecise due to sensor noise. On the contrary, fuzzy logic has proven to be effective at inferring control actions while coping with imprecise sensor information [ 54 , 55 ]. In addition to that, fuzzy logic has often been used for the navigation of robots in unknown environments [ 53 , 56 ].…”
Section: Fuzzy Logic System For Wall-following Behaviormentioning
confidence: 99%