Robot Indoor Scenes Recognition Based on Autonomous Developmental Neural Network

A method of robot indoor scene recognition based on autonomous developmental neural network is proposed. 3-layer adaptive developmental neural network is used to build the brain-mind model. In developing phase, top-k competition is utilized to simulate the lateral inhibition of neurons, and the winner updates the synapse weight vector with the lobe component analysis (LCA) algorithm. The strengthened neurons can get thinking results according to current environment information, and indoor scenes can be recognized autonomously by mobile robots. Through human-like thinking, the learning results are stored as "knowledge", and the thinking results are derived from experience. Experimental results show that the model of autonomous developmental neural network proposed as the carrier of "knowledge", fully meets the need of indoor scenes recognition task, and realizes the autonomous learning, understanding and growth of robots based on vision.