Learning indoor robot navigation using visual and sensorimotor map information.

ABSTRACT: As a fundamental research topic, autonomous indoor robot navigation continues to be a challenge in unconstrained real-world indoor environments. Although many models for map-building and planning exist, it is difficult to integrate them due to the high amount of noise, dynamics, and complexity. Addressing this challenge, this paper describes a neural model for environment mapping and robot navigation based on learning spatial knowledge. Considering that a person typically moves within a room without colliding with objects, this model learns the spatial knowledge by observing the person's movement using a ceiling-mounted camera. A robot can plan and navigate to any given position in the room based on the acquired map, and adapt it based on having identified possible obstacles. In addition,

SUBMITTER: Yan W 

PROVIDER: S-EPMC3791472 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature