Design and Implementation of Super Elman Neural Network for SDN Robotics System
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Abstract
Path planning in autonomous robotic systems (ARS) is challenging, especially in dynamic or uncertain environments. Many classical methods are computationally expensive and lack adaptability to real-world scenarios. In order to improve the overall path-planning capabilities of robots; this paper introduces a new smart robotic navigation system which uses Software Defined Network (SDN) and Multi-Spike Elman Neural Network (MS-ENN). The introduced system includes an innovative way to encode temporal information using multiple spikes which can capture much greater amounts of detail about changing environmental characteristics than conventional artificial neural networks. Additionally, it includes a spiking wave-front planner (SWP) to produce a preliminary set of paths and an MS-ENN that produces decisions on how to make changes to those paths based upon the environment. Results indicate that the proposed method was able to increase path-efficiency, decrease planning-time, and improve the success-rate within static environments. The proposed model implementation demonstrates the strengths of coupling SDN with more sophisticated spiking neural architectures for smart robotic navigation systems.
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