Design and Modeling of Nano-Robots Control in Medicine

Reza Soltani

Abstract


This study aimed to present a new model to develop and expand nanotechnology in particular in the field of medicine.  The subject under study focus on the control design of nano-robots for bio-molecular assembly manipulation, and use of evolutionary factors as a suitable method to gain the adaptive properties for proposed model is needed. Moreover, the study use of neural networks as the most practical method for the optimization problem of robot motion using a sensor based system. Thus, the study proposes a useful method within advanced graphics simulation for nano-assembly automation with its focus on an applied model for nano-medicine. Therefore, the study results should provide a great impact for effective design of control instrumentation, helping in the development of nanotechnology. The presented nano-robot model is required to survive and interact with a complex environment. Furthermore the nano-robot has to consider a pre-defined set of tasks both in a competitive scenario and in a collective environment. Nano-robot in a three-dimensional environment monitors organ inlets’ nutritional levels, and assembling new biomolecules into that have to be delivered to the organ inlets with higher priority during each moment of our dynamic simulation. The nano-robot must avoid fuzzy obstacles, and must with proper time and manner react in real time for an environment requiring continuous control. In order to achieve the most pre-programmed set of behaviors the nano-robot uses a local perception through simulated sensors to effectively interact with the surrounding environment. The development of new concepts on nano-mechatronics and automation theory is focused on the problem of molecular machine systems. Finally a novel adaptive optimal method is described and the model validation through the application of nano-robot control design for nano-medicine confirmed.


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DOI: https://doi.org/10.11114/set.v5i1.3406

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Studies in Engineering and Technology   ISSN 2330-2038 (Print)   ISSN 2330-2046 (Online)

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