AI-enhanced perception systems for flexible assembly applications using mobile manipulators
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Date
2022-10-26
Authors
Πρέζας, Λουκάς
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Abstract
Assembly is one of the most common but also important processes in today’s manufacturing; thus, the need for efficient automated assembly systems has become an absolute necessity in the recent years. However, automated assembly systems come together with complex challenges such as positioning accuracy, stable gripping, and accommodation of several variants, prevention of error aggregation.
Although the use of predefined paths (offline data - waypoints) is a reliable and popular solution, the needs of the Industry 4.0 era for high performance and responsiveness to market demands, impose the use of perception sensors. Perception sensors can increase the robot application robustness regardless of the initial conditions of the operation, enable autonomous decision-making, adaptability, reduction of errors propagation, as well as safe human robot collaboration.
This Diploma Thesis focuses on a system implementation, based on vision sensors and Artificial Intelligence so as to create a smart application for high performance parts processing and onsite quality control. The system consists of a robotic manipulator which, equipped with a 3D camera, should use its end-effector for material dispensing and process quality evaluation. The application development is mainly based on OpenCV and PCL library and the communication between the sensors and the robotic manipulator will be achieved with the Robot Operating System (ROS). The initial problem is divided into sub-tasks that the perception system addresses in order to complete the procedure. Specifically, the vision system is responsible for the product recognition (machine learning-based, the correction of the end-effector positioning (defined by offline data) by 3D processing, and the quality inspection. Τhe approach will be tested and validated in a case study inspired for gluing processes in manufacturing.
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Keywords
Robotics, Computer vision, Machine learning, Robot operating system, Point clouds, Image processing, Smart manufacturing