Abstract
This study presents the development of robotic arm with computer vision functionalities to recognise the objects with different colours, pick up the nearest target object and place it into particular location. In this paper, the overview of the robotic arm system is first presented. Then, the design of five-degrees of freedom (5-DOF) robotic arm is introduced, followed by the explanation of the image processing technique used to recognize the objects with different colours and obstacle detection. Next, the forward kinematic modelling of the robotic arm using Denavit-Hartenberg algorithm and solving the inverse kinematic of the robotic arm using modified flower pollination algorithm (MFPA) are interpreted. The result shows that the robotic arm can pick the target object accurately and place it in its particular place successfully. The concern on user safety is also been taken into consideration where the robotic arm will stop working when the user hand (obstacle) is detected and resume its process when there is no obstacle.
Highlights
Robots – machines that are programmed to perform repetitive, complex, dull, dirty and dangerous tasks, have been integrated with success in various forms, for instance, arc welding, pick and place in assembly line, handling toxic chemicals in manufacturing, and carrying radioactive materials in hazardous environments (Sajjad, Talpur, & Shaikh, 2012) (Zhang & Wang, 2004)
In order to embrace the Industry 4.0, where all processes within the industry are fully automated for mass customization, a robot that exhibits intelligent behaviour as human is needed. This is the motivation of this study in which a robotic arm that can automatically recognize and sort the objects into different classes is developed for a Pick and Place (PNP) operation, in conjunction with the integration of computer vision and evolutionary computation
The successful implementation of the computer vision in a robotic arm system available in literature has spurred the development of the robotic arm in this study, the computer vision is integrated for object sorting in a PNP operation
Summary
Robots – machines that are programmed to perform repetitive, complex, dull, dirty and dangerous tasks, have been integrated with success in various forms, for instance, arc welding, pick and place in assembly line, handling toxic chemicals in manufacturing, and carrying radioactive materials in hazardous environments (Sajjad, Talpur, & Shaikh, 2012) (Zhang & Wang, 2004). In order to embrace the Industry 4.0, where all processes within the industry are fully automated for mass customization, a robot that exhibits intelligent behaviour as human is needed This is the motivation of this study in which a robotic arm that can automatically recognize and sort the objects into different classes is developed for a PNP operation, in conjunction with the integration of computer vision and evolutionary computation. The image processing technique used to recognize the objects with different colours and detect obstacle is explained, followed by the explanation of the Denavit-Hartenberg algorithm used in modelling the forward kinematic of the robotic arm and modified flower pollination algorithm (MFPA) used in solving the inverse kinematic of the robotic arm in PNP operation. The 3D printing technology is chosen to build the robotic arm as it is fast and the material is light
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