Abstract
This paper proposes a new type of Multi-layered Artificial Neural Network (ANN) suitable for motion control of multi-joint robotic mechanisms with arbitrary Degrees of Freedom (DoF). Input layer classifies the incoming data using Auto Resonance Network (ARN) while higher levels implement Pathnet, a connection oriented neural network with Hebbian reinforcement learning capability. ARN networks grow with training input. Perturbation of ARN nodes allows the network to classify and recognize events with no previous history, Multilayer pathnets can recognize and recall temporal sequences. The network can memorize low cost paths and use parts of such segments in establishing new paths. We have used the system to control a multi segmented robotic system in R. Results of simulation presented in this paper encourage further explorations.
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Aparanji, V.M., Wali, U.V., Aparna, R. (2018). Pathnet: A Neuronal Model for Robotic Motion Planning. In: Nagabhushan, T., Aradhya, V.N.M., Jagadeesh, P., Shukla, S., M.L., C. (eds) Cognitive Computing and Information Processing. CCIP 2017. Communications in Computer and Information Science, vol 801. Springer, Singapore. https://doi.org/10.1007/978-981-10-9059-2_34
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DOI: https://doi.org/10.1007/978-981-10-9059-2_34
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