Medical robot vision using the conformai geometric algebra framework

This chapter outlines a number of research lines that, starting from the observation of nature, attempt to mimic human behavior in humanoid robots. Humanoid robotics is one of the most exciting proving grounds for the development of biologically inspired hardware and software—machines that try to recreate billions of years of evolution with some of the abilities and characteristics of living beings. Humanoids could be especially useful for their ability to “live” in human-populated environments, occupying the same physical space as people and using tools that have been designed for people. Natural human–robot interaction is also an important facet of humanoid research. Finally, learning and adapting from experience, the hallmark of human intelligence, may require some approximation to the human body in order to attain similar capacities to humans. This chapter focuses particularly on compliant actuation, soft robotics, biomimetic robot vision, robot touch, and brain-inspired motor control in the context of the iCub humanoid robot.

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CNN Training Using 3D Virtual Models for Assisted Assembly with Mixed Reality and Collaborative Robots

Applied Sciences ◽

10.3390/app11094269 ◽

2021 ◽

Vol 11 (9) ◽

pp. 4269

Author(s):

Kamil Židek ◽

Ján Piteľ ◽

Michal Balog ◽

Alexander Hošovský ◽

Vratislav Hladký ◽

...

Keyword(s):

Mixed Reality ◽

Current Trend ◽

Robot Vision ◽

Processing Unit ◽

Single Shot ◽

Vision Systems ◽

Collaborative Robots ◽

Training Time ◽

Position And Orientation ◽

Collaborative Robot

The assisted assembly of customized products supported by collaborative robots combined with mixed reality devices is the current trend in the Industry 4.0 concept. This article introduces an experimental work cell with the implementation of the assisted assembly process for customized cam switches as a case study. The research is aimed to design a methodology for this complex task with full digitalization and transformation data to digital twin models from all vision systems. Recognition of position and orientation of assembled parts during manual assembly are marked and checked by convolutional neural network (CNN) model. Training of CNN was based on a new approach using virtual training samples with single shot detection and instance segmentation. The trained CNN model was transferred to an embedded artificial processing unit with a high-resolution camera sensor. The embedded device redistributes data with parts detected position and orientation into mixed reality devices and collaborative robot. This approach to assisted assembly using mixed reality, collaborative robot, vision systems, and CNN models can significantly decrease assembly and training time in real production.

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