A study for optimal energy consumption in KUKA KR 16 articulated robot for pick-and-place task was introduce in this paper. In order to achieve the optimal energy consumption, an improve trajectory planning is required. Essentially, trajectory planning encompasses path planning in addition to planning how to move based on velocity, time and kinematics. Trajectory planning gives a path from a starting to a goal point by avoiding collisions in a 2D or 3D space. Therefore, this paper is focus on analyze the PTP motion and Linear motion in order to determine which is the best motion that can improve the trajectory planning. The optimal energy consumption to minimizing the movement based on three main axes where it used a big motors used to drive the axes. This method is much simpler in terms of development process and did not require any additional hardware to be install to the robot’s system. KUKA KR 16 is use to study optimal energy consumption and analyze PTP and Linear motion. The energy performance is measures with respect to two categories of movements known as Default and Optimal movement which do the same task repetitively within specific time. The result show that PTP motion consumed 6% more energy than Linear motion but completed 773 cycles within one hour whereas Linear motion only completed 492 cycles. Energy performance between Default and Optimal movement shows that Optimal movement recorded 21.8% less energy usage when compared to Default movement although the total cycles completed for both movement almost the same
Most of the ceramic tile industry still doing the quality control by manually. The accuracy of the manual inspection by human is lower due to the harsh industrial environment with noise, extreme temperature and humidity. A camera should replace the human eyes to recognise the defect tile effectively. Thus, a suitable method have to investigate for implementing this function. This project aim to design and develop an automated quality inspection in ceramic tile industry using vision system. The performance of the system is analysed. An Imaging Source CMOS industrial camera is use to capture the tile border. Image processing with edge detection technique is use to analyse the captured image of tile border and identify the defective tiles. The image filtering and intensity of the light are adjust to evaluate the performance of the system. The overall automation process involves image capturing, image processing, and decision making. The defect detection algorithms are develop to differentiate the defective tile based on the edge detection technique. The system using background subtraction method has achieved 50% accuracy in identify the status of tile since it consist of many limitation. By evaluate the gradient variation on the tile border edge, the accuracy of the defect detection has achieved 80% in identify the tile condition.
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