Learning robot differential movements using a new educational robotics simulation tool

Abstract The study of robotics has become a popular course among many educational programs, especially as a technical elective. A significant part of this course involves having the students learn how to program the movement of a robotic arm by controlling the velocity of its individual joint motors, a topic referred to as joint programming. They must learn how to develop algorithms to move the end effector of the arm by controlling the instantaneous velocity or some similar aspect, of each joint motor. To support this learning activity, physical or virtual robotic arms are typically employed. Visual observation of the movement of the arm provides feedback to the correctness of the student’s joint programming algorithms. A problem arises with supporting the student in learning how to move the robotic arm with precise velocity along some path, a subtopic of joint programming referred to as differential movements. To develop this knowledge, the student must produce and test differential movement algorithms and have the capability to verify its correctness. Regardless of the type of arm used, physical or virtual, the human eye cannot notice the difference between a correct or incorrect movement of the end effector as this will involve noticing small differences in velocities. This study found that by simulating the process of spray painting on a virtual canvas, the correctness of a differential movement algorithm may be accessed by observing the resulting paint on the canvas as opposed to observing the movement of the arm. A model of a set of spray-painting equipment and a canvas was added to an existing virtual robotic arm educational tool and used in an Introduction to Robotics class offered at Florida Gulf Coast University in Spring 2019 and Spring 2020. The class offered in Spring 2019 used the virtual arm but without the spray-painting feature while the class offered in Spring 2020 used the new spray-painting feature that was added to the virtual arm. Exam results show that 59.4% of the students that used the new feature scored at least an 85% on the corresponding differential movements exam question compared to only 5.6% of the class that did not use the added spray-painting feature. The differential movement exam question simply asked the student to produce a differential movements algorithm to move the arm with a specified velocity alone a straight line. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Gonzalez, Fernando [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Robotics Education Differential movements Robot joint programming Virtual robot Robot programming Introduction to robotics course Robot simulation

Anmerkung:	© The Author(s) 2023

Übergeordnetes Werk:	Enthalten in: Education and information technologies - Dordrecht [u.a.] : Springer Science + Business Media B.V., 1996, 28(2023), 9 vom: 20. Feb., Seite 11423-11446
Übergeordnetes Werk:	volume:28 ; year:2023 ; number:9 ; day:20 ; month:02 ; pages:11423-11446

Links:	Volltext

DOI / URN:	10.1007/s10639-022-11433-6

Katalog-ID:	SPR052870235

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allfields	10.1007/s10639-022-11433-6 doi (DE-627)SPR052870235 (SPR)s10639-022-11433-6-e DE-627 ger DE-627 rakwb eng Gonzalez, Fernando verfasserin (orcid)0000-0002-1272-3463 aut Learning robot differential movements using a new educational robotics simulation tool 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract The study of robotics has become a popular course among many educational programs, especially as a technical elective. A significant part of this course involves having the students learn how to program the movement of a robotic arm by controlling the velocity of its individual joint motors, a topic referred to as joint programming. They must learn how to develop algorithms to move the end effector of the arm by controlling the instantaneous velocity or some similar aspect, of each joint motor. To support this learning activity, physical or virtual robotic arms are typically employed. Visual observation of the movement of the arm provides feedback to the correctness of the student’s joint programming algorithms. A problem arises with supporting the student in learning how to move the robotic arm with precise velocity along some path, a subtopic of joint programming referred to as differential movements. To develop this knowledge, the student must produce and test differential movement algorithms and have the capability to verify its correctness. Regardless of the type of arm used, physical or virtual, the human eye cannot notice the difference between a correct or incorrect movement of the end effector as this will involve noticing small differences in velocities. This study found that by simulating the process of spray painting on a virtual canvas, the correctness of a differential movement algorithm may be accessed by observing the resulting paint on the canvas as opposed to observing the movement of the arm. A model of a set of spray-painting equipment and a canvas was added to an existing virtual robotic arm educational tool and used in an Introduction to Robotics class offered at Florida Gulf Coast University in Spring 2019 and Spring 2020. The class offered in Spring 2019 used the virtual arm but without the spray-painting feature while the class offered in Spring 2020 used the new spray-painting feature that was added to the virtual arm. Exam results show that 59.4% of the students that used the new feature scored at least an 85% on the corresponding differential movements exam question compared to only 5.6% of the class that did not use the added spray-painting feature. The differential movement exam question simply asked the student to produce a differential movements algorithm to move the arm with a specified velocity alone a straight line. Robotics (dpeaa)DE-He213 Education (dpeaa)DE-He213 Differential movements (dpeaa)DE-He213 Robot joint programming (dpeaa)DE-He213 Virtual robot (dpeaa)DE-He213 Robot programming (dpeaa)DE-He213 Introduction to robotics course (dpeaa)DE-He213 Robot simulation (dpeaa)DE-He213 Enthalten in Education and information technologies Dordrecht [u.a.] : Springer Science + Business Media B.V., 1996 28(2023), 9 vom: 20. Feb., Seite 11423-11446 (DE-627)320415953 (DE-600)2001930-0 1573-7608 nnns volume:28 year:2023 number:9 day:20 month:02 pages:11423-11446 https://dx.doi.org/10.1007/s10639-022-11433-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2023 9 20 02 11423-11446
spelling	10.1007/s10639-022-11433-6 doi (DE-627)SPR052870235 (SPR)s10639-022-11433-6-e DE-627 ger DE-627 rakwb eng Gonzalez, Fernando verfasserin (orcid)0000-0002-1272-3463 aut Learning robot differential movements using a new educational robotics simulation tool 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract The study of robotics has become a popular course among many educational programs, especially as a technical elective. A significant part of this course involves having the students learn how to program the movement of a robotic arm by controlling the velocity of its individual joint motors, a topic referred to as joint programming. They must learn how to develop algorithms to move the end effector of the arm by controlling the instantaneous velocity or some similar aspect, of each joint motor. To support this learning activity, physical or virtual robotic arms are typically employed. Visual observation of the movement of the arm provides feedback to the correctness of the student’s joint programming algorithms. A problem arises with supporting the student in learning how to move the robotic arm with precise velocity along some path, a subtopic of joint programming referred to as differential movements. To develop this knowledge, the student must produce and test differential movement algorithms and have the capability to verify its correctness. Regardless of the type of arm used, physical or virtual, the human eye cannot notice the difference between a correct or incorrect movement of the end effector as this will involve noticing small differences in velocities. This study found that by simulating the process of spray painting on a virtual canvas, the correctness of a differential movement algorithm may be accessed by observing the resulting paint on the canvas as opposed to observing the movement of the arm. A model of a set of spray-painting equipment and a canvas was added to an existing virtual robotic arm educational tool and used in an Introduction to Robotics class offered at Florida Gulf Coast University in Spring 2019 and Spring 2020. The class offered in Spring 2019 used the virtual arm but without the spray-painting feature while the class offered in Spring 2020 used the new spray-painting feature that was added to the virtual arm. Exam results show that 59.4% of the students that used the new feature scored at least an 85% on the corresponding differential movements exam question compared to only 5.6% of the class that did not use the added spray-painting feature. The differential movement exam question simply asked the student to produce a differential movements algorithm to move the arm with a specified velocity alone a straight line. Robotics (dpeaa)DE-He213 Education (dpeaa)DE-He213 Differential movements (dpeaa)DE-He213 Robot joint programming (dpeaa)DE-He213 Virtual robot (dpeaa)DE-He213 Robot programming (dpeaa)DE-He213 Introduction to robotics course (dpeaa)DE-He213 Robot simulation (dpeaa)DE-He213 Enthalten in Education and information technologies Dordrecht [u.a.] : Springer Science + Business Media B.V., 1996 28(2023), 9 vom: 20. Feb., Seite 11423-11446 (DE-627)320415953 (DE-600)2001930-0 1573-7608 nnns volume:28 year:2023 number:9 day:20 month:02 pages:11423-11446 https://dx.doi.org/10.1007/s10639-022-11433-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2023 9 20 02 11423-11446
allfields_unstemmed	10.1007/s10639-022-11433-6 doi (DE-627)SPR052870235 (SPR)s10639-022-11433-6-e DE-627 ger DE-627 rakwb eng Gonzalez, Fernando verfasserin (orcid)0000-0002-1272-3463 aut Learning robot differential movements using a new educational robotics simulation tool 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract The study of robotics has become a popular course among many educational programs, especially as a technical elective. A significant part of this course involves having the students learn how to program the movement of a robotic arm by controlling the velocity of its individual joint motors, a topic referred to as joint programming. They must learn how to develop algorithms to move the end effector of the arm by controlling the instantaneous velocity or some similar aspect, of each joint motor. To support this learning activity, physical or virtual robotic arms are typically employed. Visual observation of the movement of the arm provides feedback to the correctness of the student’s joint programming algorithms. A problem arises with supporting the student in learning how to move the robotic arm with precise velocity along some path, a subtopic of joint programming referred to as differential movements. To develop this knowledge, the student must produce and test differential movement algorithms and have the capability to verify its correctness. Regardless of the type of arm used, physical or virtual, the human eye cannot notice the difference between a correct or incorrect movement of the end effector as this will involve noticing small differences in velocities. This study found that by simulating the process of spray painting on a virtual canvas, the correctness of a differential movement algorithm may be accessed by observing the resulting paint on the canvas as opposed to observing the movement of the arm. A model of a set of spray-painting equipment and a canvas was added to an existing virtual robotic arm educational tool and used in an Introduction to Robotics class offered at Florida Gulf Coast University in Spring 2019 and Spring 2020. The class offered in Spring 2019 used the virtual arm but without the spray-painting feature while the class offered in Spring 2020 used the new spray-painting feature that was added to the virtual arm. Exam results show that 59.4% of the students that used the new feature scored at least an 85% on the corresponding differential movements exam question compared to only 5.6% of the class that did not use the added spray-painting feature. The differential movement exam question simply asked the student to produce a differential movements algorithm to move the arm with a specified velocity alone a straight line. Robotics (dpeaa)DE-He213 Education (dpeaa)DE-He213 Differential movements (dpeaa)DE-He213 Robot joint programming (dpeaa)DE-He213 Virtual robot (dpeaa)DE-He213 Robot programming (dpeaa)DE-He213 Introduction to robotics course (dpeaa)DE-He213 Robot simulation (dpeaa)DE-He213 Enthalten in Education and information technologies Dordrecht [u.a.] : Springer Science + Business Media B.V., 1996 28(2023), 9 vom: 20. Feb., Seite 11423-11446 (DE-627)320415953 (DE-600)2001930-0 1573-7608 nnns volume:28 year:2023 number:9 day:20 month:02 pages:11423-11446 https://dx.doi.org/10.1007/s10639-022-11433-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2023 9 20 02 11423-11446
allfieldsGer	10.1007/s10639-022-11433-6 doi (DE-627)SPR052870235 (SPR)s10639-022-11433-6-e DE-627 ger DE-627 rakwb eng Gonzalez, Fernando verfasserin (orcid)0000-0002-1272-3463 aut Learning robot differential movements using a new educational robotics simulation tool 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract The study of robotics has become a popular course among many educational programs, especially as a technical elective. A significant part of this course involves having the students learn how to program the movement of a robotic arm by controlling the velocity of its individual joint motors, a topic referred to as joint programming. They must learn how to develop algorithms to move the end effector of the arm by controlling the instantaneous velocity or some similar aspect, of each joint motor. To support this learning activity, physical or virtual robotic arms are typically employed. Visual observation of the movement of the arm provides feedback to the correctness of the student’s joint programming algorithms. A problem arises with supporting the student in learning how to move the robotic arm with precise velocity along some path, a subtopic of joint programming referred to as differential movements. To develop this knowledge, the student must produce and test differential movement algorithms and have the capability to verify its correctness. Regardless of the type of arm used, physical or virtual, the human eye cannot notice the difference between a correct or incorrect movement of the end effector as this will involve noticing small differences in velocities. This study found that by simulating the process of spray painting on a virtual canvas, the correctness of a differential movement algorithm may be accessed by observing the resulting paint on the canvas as opposed to observing the movement of the arm. A model of a set of spray-painting equipment and a canvas was added to an existing virtual robotic arm educational tool and used in an Introduction to Robotics class offered at Florida Gulf Coast University in Spring 2019 and Spring 2020. The class offered in Spring 2019 used the virtual arm but without the spray-painting feature while the class offered in Spring 2020 used the new spray-painting feature that was added to the virtual arm. Exam results show that 59.4% of the students that used the new feature scored at least an 85% on the corresponding differential movements exam question compared to only 5.6% of the class that did not use the added spray-painting feature. The differential movement exam question simply asked the student to produce a differential movements algorithm to move the arm with a specified velocity alone a straight line. Robotics (dpeaa)DE-He213 Education (dpeaa)DE-He213 Differential movements (dpeaa)DE-He213 Robot joint programming (dpeaa)DE-He213 Virtual robot (dpeaa)DE-He213 Robot programming (dpeaa)DE-He213 Introduction to robotics course (dpeaa)DE-He213 Robot simulation (dpeaa)DE-He213 Enthalten in Education and information technologies Dordrecht [u.a.] : Springer Science + Business Media B.V., 1996 28(2023), 9 vom: 20. Feb., Seite 11423-11446 (DE-627)320415953 (DE-600)2001930-0 1573-7608 nnns volume:28 year:2023 number:9 day:20 month:02 pages:11423-11446 https://dx.doi.org/10.1007/s10639-022-11433-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2023 9 20 02 11423-11446
allfieldsSound	10.1007/s10639-022-11433-6 doi (DE-627)SPR052870235 (SPR)s10639-022-11433-6-e DE-627 ger DE-627 rakwb eng Gonzalez, Fernando verfasserin (orcid)0000-0002-1272-3463 aut Learning robot differential movements using a new educational robotics simulation tool 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract The study of robotics has become a popular course among many educational programs, especially as a technical elective. A significant part of this course involves having the students learn how to program the movement of a robotic arm by controlling the velocity of its individual joint motors, a topic referred to as joint programming. They must learn how to develop algorithms to move the end effector of the arm by controlling the instantaneous velocity or some similar aspect, of each joint motor. To support this learning activity, physical or virtual robotic arms are typically employed. Visual observation of the movement of the arm provides feedback to the correctness of the student’s joint programming algorithms. A problem arises with supporting the student in learning how to move the robotic arm with precise velocity along some path, a subtopic of joint programming referred to as differential movements. To develop this knowledge, the student must produce and test differential movement algorithms and have the capability to verify its correctness. Regardless of the type of arm used, physical or virtual, the human eye cannot notice the difference between a correct or incorrect movement of the end effector as this will involve noticing small differences in velocities. This study found that by simulating the process of spray painting on a virtual canvas, the correctness of a differential movement algorithm may be accessed by observing the resulting paint on the canvas as opposed to observing the movement of the arm. A model of a set of spray-painting equipment and a canvas was added to an existing virtual robotic arm educational tool and used in an Introduction to Robotics class offered at Florida Gulf Coast University in Spring 2019 and Spring 2020. The class offered in Spring 2019 used the virtual arm but without the spray-painting feature while the class offered in Spring 2020 used the new spray-painting feature that was added to the virtual arm. Exam results show that 59.4% of the students that used the new feature scored at least an 85% on the corresponding differential movements exam question compared to only 5.6% of the class that did not use the added spray-painting feature. The differential movement exam question simply asked the student to produce a differential movements algorithm to move the arm with a specified velocity alone a straight line. Robotics (dpeaa)DE-He213 Education (dpeaa)DE-He213 Differential movements (dpeaa)DE-He213 Robot joint programming (dpeaa)DE-He213 Virtual robot (dpeaa)DE-He213 Robot programming (dpeaa)DE-He213 Introduction to robotics course (dpeaa)DE-He213 Robot simulation (dpeaa)DE-He213 Enthalten in Education and information technologies Dordrecht [u.a.] : Springer Science + Business Media B.V., 1996 28(2023), 9 vom: 20. Feb., Seite 11423-11446 (DE-627)320415953 (DE-600)2001930-0 1573-7608 nnns volume:28 year:2023 number:9 day:20 month:02 pages:11423-11446 https://dx.doi.org/10.1007/s10639-022-11433-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2023 9 20 02 11423-11446
language	English
source	Enthalten in Education and information technologies 28(2023), 9 vom: 20. Feb., Seite 11423-11446 volume:28 year:2023 number:9 day:20 month:02 pages:11423-11446
sourceStr	Enthalten in Education and information technologies 28(2023), 9 vom: 20. Feb., Seite 11423-11446 volume:28 year:2023 number:9 day:20 month:02 pages:11423-11446
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Robotics Education Differential movements Robot joint programming Virtual robot Robot programming Introduction to robotics course Robot simulation
isfreeaccess_bool	true
container_title	Education and information technologies
authorswithroles_txt_mv	Gonzalez, Fernando @@aut@@
publishDateDaySort_date	2023-02-20T00:00:00Z
hierarchy_top_id	320415953
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Regardless of the type of arm used, physical or virtual, the human eye cannot notice the difference between a correct or incorrect movement of the end effector as this will involve noticing small differences in velocities. This study found that by simulating the process of spray painting on a virtual canvas, the correctness of a differential movement algorithm may be accessed by observing the resulting paint on the canvas as opposed to observing the movement of the arm. A model of a set of spray-painting equipment and a canvas was added to an existing virtual robotic arm educational tool and used in an Introduction to Robotics class offered at Florida Gulf Coast University in Spring 2019 and Spring 2020. The class offered in Spring 2019 used the virtual arm but without the spray-painting feature while the class offered in Spring 2020 used the new spray-painting feature that was added to the virtual arm. 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author	Gonzalez, Fernando
spellingShingle	Gonzalez, Fernando misc Robotics misc Education misc Differential movements misc Robot joint programming misc Virtual robot misc Robot programming misc Introduction to robotics course misc Robot simulation Learning robot differential movements using a new educational robotics simulation tool
authorStr	Gonzalez, Fernando
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topic_title	Learning robot differential movements using a new educational robotics simulation tool Robotics (dpeaa)DE-He213 Education (dpeaa)DE-He213 Differential movements (dpeaa)DE-He213 Robot joint programming (dpeaa)DE-He213 Virtual robot (dpeaa)DE-He213 Robot programming (dpeaa)DE-He213 Introduction to robotics course (dpeaa)DE-He213 Robot simulation (dpeaa)DE-He213
topic	misc Robotics misc Education misc Differential movements misc Robot joint programming misc Virtual robot misc Robot programming misc Introduction to robotics course misc Robot simulation
topic_unstemmed	misc Robotics misc Education misc Differential movements misc Robot joint programming misc Virtual robot misc Robot programming misc Introduction to robotics course misc Robot simulation
topic_browse	misc Robotics misc Education misc Differential movements misc Robot joint programming misc Virtual robot misc Robot programming misc Introduction to robotics course misc Robot simulation
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title	Learning robot differential movements using a new educational robotics simulation tool
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title_full	Learning robot differential movements using a new educational robotics simulation tool
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title_sort	learning robot differential movements using a new educational robotics simulation tool
title_auth	Learning robot differential movements using a new educational robotics simulation tool
abstract	Abstract The study of robotics has become a popular course among many educational programs, especially as a technical elective. A significant part of this course involves having the students learn how to program the movement of a robotic arm by controlling the velocity of its individual joint motors, a topic referred to as joint programming. They must learn how to develop algorithms to move the end effector of the arm by controlling the instantaneous velocity or some similar aspect, of each joint motor. To support this learning activity, physical or virtual robotic arms are typically employed. Visual observation of the movement of the arm provides feedback to the correctness of the student’s joint programming algorithms. A problem arises with supporting the student in learning how to move the robotic arm with precise velocity along some path, a subtopic of joint programming referred to as differential movements. To develop this knowledge, the student must produce and test differential movement algorithms and have the capability to verify its correctness. Regardless of the type of arm used, physical or virtual, the human eye cannot notice the difference between a correct or incorrect movement of the end effector as this will involve noticing small differences in velocities. This study found that by simulating the process of spray painting on a virtual canvas, the correctness of a differential movement algorithm may be accessed by observing the resulting paint on the canvas as opposed to observing the movement of the arm. A model of a set of spray-painting equipment and a canvas was added to an existing virtual robotic arm educational tool and used in an Introduction to Robotics class offered at Florida Gulf Coast University in Spring 2019 and Spring 2020. The class offered in Spring 2019 used the virtual arm but without the spray-painting feature while the class offered in Spring 2020 used the new spray-painting feature that was added to the virtual arm. Exam results show that 59.4% of the students that used the new feature scored at least an 85% on the corresponding differential movements exam question compared to only 5.6% of the class that did not use the added spray-painting feature. The differential movement exam question simply asked the student to produce a differential movements algorithm to move the arm with a specified velocity alone a straight line. © The Author(s) 2023
abstractGer	Abstract The study of robotics has become a popular course among many educational programs, especially as a technical elective. A significant part of this course involves having the students learn how to program the movement of a robotic arm by controlling the velocity of its individual joint motors, a topic referred to as joint programming. They must learn how to develop algorithms to move the end effector of the arm by controlling the instantaneous velocity or some similar aspect, of each joint motor. To support this learning activity, physical or virtual robotic arms are typically employed. Visual observation of the movement of the arm provides feedback to the correctness of the student’s joint programming algorithms. A problem arises with supporting the student in learning how to move the robotic arm with precise velocity along some path, a subtopic of joint programming referred to as differential movements. To develop this knowledge, the student must produce and test differential movement algorithms and have the capability to verify its correctness. Regardless of the type of arm used, physical or virtual, the human eye cannot notice the difference between a correct or incorrect movement of the end effector as this will involve noticing small differences in velocities. This study found that by simulating the process of spray painting on a virtual canvas, the correctness of a differential movement algorithm may be accessed by observing the resulting paint on the canvas as opposed to observing the movement of the arm. A model of a set of spray-painting equipment and a canvas was added to an existing virtual robotic arm educational tool and used in an Introduction to Robotics class offered at Florida Gulf Coast University in Spring 2019 and Spring 2020. The class offered in Spring 2019 used the virtual arm but without the spray-painting feature while the class offered in Spring 2020 used the new spray-painting feature that was added to the virtual arm. Exam results show that 59.4% of the students that used the new feature scored at least an 85% on the corresponding differential movements exam question compared to only 5.6% of the class that did not use the added spray-painting feature. The differential movement exam question simply asked the student to produce a differential movements algorithm to move the arm with a specified velocity alone a straight line. © The Author(s) 2023
abstract_unstemmed	Abstract The study of robotics has become a popular course among many educational programs, especially as a technical elective. A significant part of this course involves having the students learn how to program the movement of a robotic arm by controlling the velocity of its individual joint motors, a topic referred to as joint programming. They must learn how to develop algorithms to move the end effector of the arm by controlling the instantaneous velocity or some similar aspect, of each joint motor. To support this learning activity, physical or virtual robotic arms are typically employed. Visual observation of the movement of the arm provides feedback to the correctness of the student’s joint programming algorithms. A problem arises with supporting the student in learning how to move the robotic arm with precise velocity along some path, a subtopic of joint programming referred to as differential movements. To develop this knowledge, the student must produce and test differential movement algorithms and have the capability to verify its correctness. Regardless of the type of arm used, physical or virtual, the human eye cannot notice the difference between a correct or incorrect movement of the end effector as this will involve noticing small differences in velocities. This study found that by simulating the process of spray painting on a virtual canvas, the correctness of a differential movement algorithm may be accessed by observing the resulting paint on the canvas as opposed to observing the movement of the arm. A model of a set of spray-painting equipment and a canvas was added to an existing virtual robotic arm educational tool and used in an Introduction to Robotics class offered at Florida Gulf Coast University in Spring 2019 and Spring 2020. The class offered in Spring 2019 used the virtual arm but without the spray-painting feature while the class offered in Spring 2020 used the new spray-painting feature that was added to the virtual arm. Exam results show that 59.4% of the students that used the new feature scored at least an 85% on the corresponding differential movements exam question compared to only 5.6% of the class that did not use the added spray-painting feature. The differential movement exam question simply asked the student to produce a differential movements algorithm to move the arm with a specified velocity alone a straight line. © The Author(s) 2023
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container_issue	9
title_short	Learning robot differential movements using a new educational robotics simulation tool
url	https://dx.doi.org/10.1007/s10639-022-11433-6
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doi_str	10.1007/s10639-022-11433-6
up_date	2024-07-03T15:22:18.744Z
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