Distributed visual navigation based on neural Q-learning for a mobile robot

Abstract

Distributed visual navigation based on neural Q-learning for a mobile robot is studied in this paper. First, a general distributed structure based on the multiple processors for visual navigation is established according to the decomposition of the mobile robot visual navigation task. Second, in terms of the general distributed structure, the local environment description method based on the Peer Group Filtering (PGF) and fuzzy technology is put forward. Third, in each local environment description, a controller based on neural Q-learning is designed to guide the mobile robot navigation. In the last part of this paper, experimental simulations are done to test the effectiveness of the presented distributed algorithm, including the image segmentation, environment description and navigation policy.