Learning Robot Differential Movements Using a New Educational Robotics Simulation Tool

Autor/in	Gonzalez, Fernando
Titel	Learning Robot Differential Movements Using a New Educational Robotics Simulation Tool
Quelle	In: Education and Information Technologies, 28 (2023) 9, S.11423-11446 (24 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Gonzalez, Fernando)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-022-11433-6
Schlagwörter	Robotics; Educational Technology; Technology Uses in Education; Simulation; Algorithms; Computer Science Education; Programming; Motion; Introductory Courses; College Students; Academic Achievement; Test Results; Florida + Suchen Sie Ihr Suchwort? Robotertechnik; Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Simulation program; Simulationsprogramm; Algorithm; Algorithmus; Computer science lessons; Informatikunterricht; Programmierung; Bewegungsablauf; Einführungskurs; Collegestudent; Schulleistung
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01