New Method for Controlling Jointed Arm Robots Using Integrated Computer Vision System

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A novel approach to control the Jointed Arm Robot by tracking the position of the moving object on a conveyor using Integrated Computer Vision system | Journal of Engineering Research
, Computer Vision, Object Tracking, Image Processing, Autonomous Systems, Manufacturing Industry.

In today's manufacturing industry, automation plays a crucial role in improving efficiency and reducing costs. Robots are increasingly being used for repetitive or dangerous tasks, and their performance depends on sophisticated control systems. One of the key challenges in controlling robots is to accurately track the position and movement of objects in the workspace. This is where computer vision technology can make a significant difference.

Computer vision is an interdisciplinary field that combines computer science, optics, and artificial intelligence to develop systems that can interpret and understand images or videos. Computer vision can be used to develop self-sustained autonomous systems that can work without human intervention. In this context, computer vision can be used to control jointed arm robots based on the position of the moving object using an integrated computer vision system.

The jointed arm robot is a type of robot that consists of multiple linked segments that can move in multiple axes. The jointed arm robot can perform complex tasks that require a high degree of accuracy and precision. Examples of applications of jointed arm robots include welding, painting, assembly, and material handling.

The approach proposed in this paper uses a simple web camera placed above the work cell to capture the continuous images of a conveyor and a jointed arm robot that connected to a microcontroller through the computer. The position of an object tracked, and its features are extracted from the captured image frame by subtracting its background using the Gaussian Mixture Model (GMM). The output images of GMM further processed by image processing techniques to extract the shape, color, center coordinates.

The extracted coordinates of the objects of interest used as input for the controller to activate the base rotation of a joint arm robot to perform different manipulations. The algorithm evaluated on an indigenously fabricated work cell integrated with a computer vision setup. The results show that the proposed approach is effective in controlling the jointed arm robot based on the position of the moving object.

Computer vision technology has several advantages that make it ideal for controlling robots. First, it can work in real-time, which is essential for controlling robots that need to operate in dynamic environments. Second, it can deal with complex and noisy data, such as images or videos, and extract relevant information. Third, it can learn and adapt to changing conditions, which is crucial for autonomous systems that must operate under different conditions.

In conclusion, the use of computer vision technology to control jointed arm robots based on the position of the moving object is a promising approach that can improve efficiency and reduce costs in the manufacturing industry. The proposed approach provides a simple and effective way to track the position of objects in the workspace and control the jointed arm robot accordingly. The use of computer vision technology in the manufacturing industry is likely to increase in the future as the technology matures and becomes more accessible.

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Expert Answers for 3D Printing Enthusiasts – 3D Printing Stack Exchange Q&A

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New Method for Controlling Jointed Arm Robots Using Integrated Computer Vision System

, Computer Vision, Object Tracking, Image Processing, Autonomous Systems, Manufacturing Industry.In today's manufacturing industry, automation plays a crucial role in improving efficiency and reducing costs. Robots are increasingly being used for repetitive or dangerous tasks, and their performance depends on sophisticated control systems. One of the key challenges in controlling robots is to accurately track the position and movement of objects in the workspace. This is where computer vision technology can make a significant difference.Computer vision is an interdisciplinary field that combines computer science, optics, and artificial intelligence to develop systems that can interpret and understand images or videos. Computer vision can be used to develop self-sustained autonomous systems that can work without human intervention. In this context, computer vision can be used to control jointed arm robots based on the position of the moving object using an integrated computer vision system.The jointed arm robot is a type of robot that consists of multiple linked segments that can move in multiple axes. The jointed arm robot can perform complex tasks that require a high degree of accuracy and precision. Examples of applications of jointed arm robots include welding, painting, assembly, and material handling.The approach proposed in this paper uses a simple web camera placed above the work cell to capture the continuous images of a conveyor and a jointed arm robot that connected to a microcontroller through the computer. The position of an object tracked, and its features are extracted from the captured image frame by subtracting its background using the Gaussian Mixture Model (GMM). The output images of GMM further processed by image processing techniques to extract the shape, color, center coordinates.The extracted coordinates of the objects of interest used as input for the controller to activate the base rotation of a joint arm robot to perform different manipulations. The algorithm evaluated on an indigenously fabricated work cell integrated with a computer vision setup. The results show that the proposed approach is effective in controlling the jointed arm robot based on the position of the moving object.Computer vision technology has several advantages that make it ideal for controlling robots. First, it can work in real-time, which is essential for controlling robots that need to operate in dynamic environments. Second, it can deal with complex and noisy data, such as images or videos, and extract relevant information. Third, it can learn and adapt to changing conditions, which is crucial for autonomous systems that must operate under different conditions.In conclusion, the use of computer vision technology to control jointed arm robots based on the position of the moving object is a promising approach that can improve efficiency and reduce costs in the manufacturing industry. The proposed approach provides a simple and effective way to track the position of objects in the workspace and control the jointed arm robot accordingly. The use of computer vision technology in the manufacturing industry is likely to increase in the future as the technology matures and becomes more accessible.

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