Neural motion and evolutionary decision in robotic competition applied for molecular machine system design

A. Cavalcanti1
1Computer Science Department, Darmstadt University of Technology, Darmstadt, Germany

Tóm tắt

Summary form only given. The author presents a new approach within advanced graphics simulations for the problem of nano-assembly automation and its application for nanomedicine. The problem under study concentrates its main focus on the design of autonomous nanorobots for assembly manipulation and the use of evolutionary competitive agents as a suitable way to warranty the robustness of such proposed model. Furthermore, the work presents also the use of neural networks as the most practical approach for the problem of robot motion optimisation using a sensor based system. Thereby the paper addresses distinct aspects of the main techniques required to achieve a successful nano-planning system design and its simulation with a real time 3D visualization.

Từ khóa

#Robotics and automation #Graphics #Robotic assembly #Warranties #Robustness #Neural networks #Robot motion #Sensor systems #Real time systems #Visualization

Tài liệu tham khảo

10.1061/40337(205)46 10.1166/jnn.2001.029 stracke, 2000, Physical and Technica1 parameters determining the functioning of a knesin-based cell-free motor system, Nanotechnology 11 UK, 52, 10.1088/0957-4484/11/2/302 sitti, 1999, Teleoperated Nano Scale Object Manipulation, Recent Advances on Mechatronics, 172 10.1007/978-1-4757-3556-7 woo, 1999, OpenGL Programming Guide 10.1109/ROBOT.2000.844135 10.1002/anie.199522801 symon, 1971, Mechanics 10.1021/ar950209v drexler, 1992, Nanosystems: molecular machinery, manufacturing, and computation freitas, 1999, Nanomedicine, Basic Capabilities Landes Bioscience, 1 10.1017/CBO9780511608193 10.1145/280814.280816 hagiya, 2000, From Molecular Computing to Molecular Programming, Proc 6th DIMACS Workshop on DNA Based Computers held at the University of Leiden, 198 haile, 1992, Molecular Dynamics Simulations, Elementry Methods haykin, 1999, Neural Networks a Comprehensive Foundation chem, 1994, Computational Chemistry, Publication #HC40–00–03–00” Hypercube Inc Waterloo Ontario Canada kauffman, 1994, Random Chemistry, Perspectives in Drug Discovery and Design, 319 10.1016/S0006-3495(93)81129-9 10.1145/192161.192168 10.1145/199404.199436 10.1145/74333.74356 cavalcanti, 2002, A Virtual Environment for Evolutionary Autonomous Optimization of Real Time Stochastic Control Design, Proc of IEEE Int'l Conf on Information Decision and Control, 83 10.1109/IROS.1999.812792 10.1038/249077a0 cavalcanti, 2000, Using Genetic Algorithm and Simplex Method to Stabilize an Oil Treatment Plant Inlet Flow, Proc of Int'I Pipeline Conf 2000 ASME, 1459 cavalcanti, 2001, Parallel Processing Applied for Scientific Visualization of Industrial Adaptive Control Simulation, Proc of Int'l Conf on Computer Graphics and Imaging IASTED Honolulu USA, 258 asano, 1996, Dl-optimal motion for a rode, Proc 12th Symposium Computational Geometry, 252 czarn, 1998, From Nanotechnology to Nano-Planning, The 9th University of Western Australia Computer Science Research Conf, 73 adleman, 1995, On Constructing a Molecular Computer, DNA Based Computers 10.1109/ROBOT.1997.606804 kube, 1997, Collective Robotics: from Local Perception to Global Action kruglinski, 1998, Programming Microsoft Visual C++ 10.1109/ISATP.2001.929037 lin, 1993, Efficient Collision Detection for Animiation and Robotics 10.1088/0957-4484/8/2/001 martel, 1999, NanoWalker: a fully autonomous highly integrated miniature robot for nano-scale measurements, Proc of the European Optical Society and SPIE Int'l Symposium on Envirosense Microsystems Metro1ogy and Inspection, 3825, 64