Implementation of machine vision on a collaborative robot
Abstract: This project is developed with the University of Skövde and Volvo GTO. Purpose of the project is to complement and facilitate the quality insurance when gluing the engine frame. Quality defects in today’s industry is a major concern due to how costly it is to fix them. With competition rising and quality demands increasing, companies are looking for new and more efficient ways to ensure quality. Collaborative robots is a rising and unexplored technology in most industries. It is an upcoming field with great flexibility that could solve many issues and can assist its processes that are difficult to automate. The project aims to investigate if it is possible and beneficial to implement a vision system on a collaborative robot which ensures quality. Also, investigate if the collaborative robot could work with other tasks as well. This project also includes training and learning an artificial network with CAD generated models and real-life prototypes. The project had a lot of challenges with both training the AI and how the robot would communicate with it. The final results stated that a collaborative robot more specific UR10e could work with machine vision. This solution was based on using a camera which was compatible with the built-in robot software. However, this does not mean that other type of cameras cannot be used for this type of functions as well. Using machine vision based on artificial intelligence is a valid solution but requires further development and training to get a software function working in industry. Working with collaborative robots could change the industry for the better in many ways. Implementing collaborative robots could ease the work for the operators to aid in heavy lifting and repetitive work. Being able to combine a collaborative robot with a vision system could increase productivity and economic benefits.
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