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Emerging Trends and Conclusion
Published in Sheila Anand, L. Priya, A Guide for Machine Vision in Quality Control, 2019
Another important application of 3D imaging is in the area of robotics and automation. For example, robots are used for bin picking, that is, locating parts from a bin and taking them out one by one as required. Robots require the components to be located or be positioned only in fixed spots, either in the bins as per example or elsewhere in the manufacturing process. Moveable parts need to be clamped down in a certain way for robots to always pick them up in the same way. Three-dimensional vision technologies can be adopted to enable robots to determine location so parts can be picked up even with variations. 3D vision technology can enable robots to behave like humans in plotting paths to avoid obstacles and prevent collusion and accidents. We will see the emergence of collaborative robots who will work with each other as well their human counterparts. The ultimate goal would be to use 3D imaging to allow robots to “see” the world just like humans do.
Computer Vision (CV) Technologies and Tools for Vision-based Cognitive IoT Systems
Published in Pethuru Raj, Anupama C. Raman, Harihara Subramanian, Cognitive Internet of Things, 2022
Pethuru Raj, Anupama C. Raman, Harihara Subramanian
We have advanced robots in the form of industrial and service robots. Collaborative robotics enabled with CV is capable of performing bin picking of pieces placed disorderly. The robot needs the CV capability to tell it what a piece is and where it is. This enables the robot to decide what is the best way to pick up the piece.
Modeling object arrangement patterns and picking arranged objects
Published in Advanced Robotics, 2021
Object picking is a fundamental task in the field of robotics. The most fundamental robotic manipulation task is bin picking, which refers to the robotic task of picking up an object placed randomly among other objects. Bin picking in a factory can use the computer aided design (CAD) models of the target object. Many previous studies have focused on vision guided bin picking, in which the 3D scan data of the object are matched to the CAD model, and the object pose is estimated from it [1–6]. Kirkegaard adopted this approach and used the harmonic shape contexts feature for pose estimation [2]. Buchholz used random sample matching (RANSAM) and iterative closest point (ICP) algorithm to identify object localization [5].