3D-curves and ruled surfaces for graphics and robotics using conformal geometric computing

2006 ◽  
Author(s):  
L. Falcon-Morales ◽  
E. Bayro-Corrochano
Keyword(s):  
Ruled Surfaces ◽  
Geometric Computing ◽  
And Robotics

Combining Deep Learning and Robotics for Automated Concrete Delamination Assessment

2019 ◽  
Author(s):  
Evan McLaughlin ◽  
Nicholas Charron ◽  
Sriram Narasimhan
Keyword(s):  
Deep Learning ◽  
And Robotics

Biohybrid robots are synthetic biology systems

2018 ◽  
Author(s):  
Joseph Ayers
Keyword(s):  
Nervous System ◽  
Synthetic Biology ◽  
Behavioral Control ◽  
Eukaryotic Cells ◽  
Chemical Senses ◽  
Biomimetic Robots ◽  
Control Schemes ◽  
Induced Pluripotent ◽  
And Control ◽  
And Robotics

This chapter describes how synthetic biology and organic electronics can integrate neurobiology and robotics to form a basis for biohybrid robots and synthetic neuroethology. Biomimetic robots capture the performance advantages of animal models by mimicking the behavioral control schemes evolved in nature, based on modularized devices that capture the biomechanics and control principles of the nervous system. However, current robots are blind to chemical senses, difficult to miniaturize, and require chemical batteries. These obstacles can be overcome by integration of living engineered cells. Synthetic biology seeks to build devices and systems from fungible gene parts (gene systems coding different proteins) integrated into a chassis (induced pluripotent eukaryotic cells, yeast, or bacteria) to produce devices with properties not found in nature. Biohybrid robots are examples of such systems (interacting sets of devices). A nascent literature describes genes that can mediate organ levels of organization. Such capabilities, applied to biohybrid systems, portend truly biological robots guided, controlled, and actuated solely by life processes.

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