Hieroglyphically Supervised Bioinspired Visioneural Controller. (29th May 2022) - Berco, Dan; Chiu, Chih-Hao; Ang, Diing Shenp
Abstract : Unlike an intelligent microcontroller, biologic neural networks maintain active control throughout their operation, while the former usually needs to be disengaged during maintenance or software updates. Ideally, artificial intelligent control systems incorporate an incremental learning capability, without having the need to be retrained offline. Moreover, some hardware‐based neural networks (HNNs) rely on complementary software simulators during training. Such approaches decrease their potential to implement real‐time adaption, in a similar manner to their biologic counterparts. Herein, an independent, bioinspired robotic vision system with an incremental learning ability is presented. The system is trained and operated by associating instructions with hieroglyphic symbols and used to maneuver a robotic vehicle. A neural processor, capable of incremental online learning, forms the basis for the decision‐making flow. The entire range of functionality, starting from training, data storage, and up to controlling, is demonstrated herein. Abstract : An intelligent visioneural controller is presented. It consists of a bioinspired vision module that captures hieroglyphic instructions that are then used to train a neural processor by incremental online learning. The system is used to maneuver a small robotic vehicle.