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This research work proposes a robotized workstation for automatic disassembly of electric vehicle motors. A novel image processing algorithm is proposed for autonomous detection of motor screws, to automate the task of motor disassembly. Instead of having a database of templates for matching, the screws are detected based on their characteristics with respect to its grayscale, depth and HSV values. Furthermore, with frame iterations, the accuracy of the system is increased mean while reducing its runtime. The algorithm is successfully tested and implemented and yields highly accurate results

Industrial robots and cobots are widely deployed in most industrial sectors. However, robotic programming still needs a lot of time and effort in small batch sizes, and it demands specific expertise and special training, especially when various robotic platforms are required. Actual low-code or no-code robotic programming solutions are exorbitant and meager. This work proposes a novel approach for no-code robotic programming for end-users with adequate or no expertise in industrial robotic. The proposed method ensures intuitive and fast robotic programming by utilizing a finite state machine with three layers of natural interactions based on hand gesture, finger gesture, and voice recognition. The implemented system combines intelligent computer vision and voice control capabilities. Using a vision system, the human could transfer spatial information of a 3D point, lines, and trajectories using hand and finger gestures. The voice recognition system will assist the user in parametrizing robot parameters and interacting with the robot’s state machine. Furthermore, the proposed method will be validated and compared with state-of-the-art “Hand-Guiding” cobot devices within real-world experiments. The results obtained are auspicious, and indicate the capability of this novel approach for real-world deployment in an industrial context.

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