Cellular neural network: Difference between revisions

Revision as of 15:43, 7 February 2021

Revision as of 12:15, 29 January 2022

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There is an ongoing effort to incorporate CNN processors into sensory-computing-actuating machines as part of the emerging field of [[Cellular Machines]]. The basic premise is to create an integrated system that uses CNN processors for the sensory signal-processing and potentially the decision-making and control. The reason is that CNN processors can provide a low power, small size, and eventually low-cost computing and actuating system suited for Cellular Machines. These Cellular Machines will eventually create a Sensor-Actuator Network (SAN),<ref>M. Haenggi, "Mobile Sensor-Actuator Networks: Opportunities and Challenges", Int’l Workshop on Cellular Neural Networks and Their Applications, 2002.</ref> a type of Mobile Ad Hoc Networks (MANET) which can be used for military intelligence gathering, surveillance of inhospitable environments, maintenance of large areas, planetary exploration, etc.

CNN processors have been proven versatile enough for some control functions. They have been used to optimize function via a genetic algorithm,<ref>D. Balya and V. Galt, "Analogic Implementation of the Genetic Algorithm", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref> to measure distances, to perform optimal path-finding in a complex, dynamic environment, and theoretically can be used to learn and associate complex stimuli. They have also been used to create antonymous gaits and low-level motors for robotic [[nematode]]s, spiders, and lamprey gaits using a Central Pattern Generator (CPG). They were able to function using only feedback from the environment, allowing for a robust, flexible, biologically inspired robot motor system. CNN-based systems were able to operate in different environments and still function if some of the processing units are disabled.<ref>{{cite arxivarXiv|last1=Asli|first1=A. E. Niaraki|last2=Roghair|first2=J.|last3=Jannesari|first3=A.|date=2020-03-11|title=Energy-aware Goal Selection and Path Planning of UAV Systems via Reinforcement Learning|class=eess.SP|eprint=1909.12217}}</ref><ref>I. Gavrilut, V. Tiponut, and A. Gacsadi, "Path Planning of Mobile Robots by Using Cellular Neural Networks", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref><ref>P. Arena, P. Crucitti, L. Fortuna, M. Frasca, D. Lombardo and L. Patane, "Perceptive Patterns For Mobile Robots via RD-CNN and Reinforcement Learning", Int’l Workshop on Cellular Neural Networks and Their Applications, 2006.</ref>

=== Communication systems ===