This analysis evaluates the accuracy of robotic manipulators using demonstrative-kinesthetic teaching, indicating its advantages for programming tasks in manufacturing.
Industrial robots have been integrated into manufacturing activities to meet the dynamic nature of customer needs. As a solution to keeping a competitive edge on the competition, it has necessitated the adoption of newer and improved manufacturing practices. The technological advancement brought about by the overall development of cyber-physical systems has dramatically facilitated the needed technological growth. The adoption of robotics has led to smart manufacturing and encouraged the establishment of a collaborative work environment. The study aimed to program a robotic manipulator using demonstrative-kinesthetic teaching, assessing its accuracy to acquire desired target positions and validating using a palletising experiment. Using structured texts to perform the same experiment serves as a control experiment, and the demonstrative-kinesthetic teaching approach is compared against it in terms of joint values acquired while using both methods. The Dobot Magician robot manipulator was programmed with both approaches for the experimental tasks. The results showed a more accessible programming approach, especially considering workforces not accustomed to robotic programming and robot manipulator learning of demonstrated tasks. The high level of joint accuracy demonstrated the high flexibility the demonstrative kinesthetic teaching offers as a programming method, especially in programming and re programming in factory set-ups due to the fluctuating manufacturing demands.
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Mabong et al. (2025) studied this question.
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