Nanorobotics control design for nanomedicine

The purpose of this thesis is to present a new paradigm for nanotechnology automation. Therefore, the work provides a computational methodology for control design of nanorobots with an application in medicine. The subject under study concentrates its main focus on the control design of nanorobots for biomolecular assembly manipulation and the use of evolutionary agents as a suitable way to achieve the adaptive features required for the proposed model. Furthermore the work presents the use of neural networks as the most practical technique for the problem of robot motion optimization using a sensor based system. Thus, the author proposes a useful approach within advanced graphics simulation for nano-assembly automation with its focus on an application for nanomedicine. The motivation for such a study is the fact that with the emerging era of molecular engineering, the development of methodologies that facilitate analytical and empirical investigation, should help in the system architecture analysis, improving the evaluation of new approaches for insightful comprehension of nano-worlds. Therefore, it should provide a great impact for effective design of control instrumentation, helping in the development of nanotechnology. The presented nanorobot model is required to survive and interact with a complex environment. Furthermore the nanorobot has to address a pre-defined set of tasks both in a competitive scenario and in a cooperative collective environment. In a three-dimensional environment our nanorobot monitors a determined number of organ inlets’ nutritional levels, capturing and assembling new biomolecules into proteins that have to be delivered to the organ inlets with higher priority during each moment of our dynamic simulation. The nanorobot 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 appropriate pre-programmed set of behaviours the nanorobot uses a local perception through simulated sensors to effectively interact with the surrounding workspace. Thereby this work addresses distinct aspects of the main techniques required to achieve a consistent nano-planning systems design through the analysis of numerical results. To provide a feasible design for the behaviour of a reactive nanorobot, the computational architecture adopted parallel processing as the natural way to achieve a modular design. This enables a functional orientation focused on each main aspect related to an intelligent sensor-based nanorobot's successful performance. For such an aim, it used feedback evolutionary decision control activation, neural motion control, and real time environment interaction methodologies. The application of stochastic models has provided an appropriate evolutionary agent behaviour, which was shown to be the most effective methodology for any situation when a more specific action description does not attend a large number of complex elements in a dynamic environment. The model includes stochastic techniques, addressing aspects inherent to quantum uncertainties present in the microscopic spaces. We have employed the proposed nanorobot in an evolved physically based simulated environment in a series of task-based non-trivial problems, and have studied the adaptive properties of distinct nanorobot behaviour with a design to address each environment with respective rules to trigger control activation for behavior activation and complexities. Thus the development of new concepts on nanomechatronics and automation theory is focused on the problem of molecular machine systems. A novel adaptive optimal methodology is described and the model validation is demonstrated successfully through the application of nanorobot control design for nanomedicine.