| dc.description.abstract | Robot localization has been a very challenging task in mobile robotics since it in essential for a broad range of mobile robot tasks. This thesis proposes a new vision-based robot localization and map-building algorithm. Via the proposed algorithm, a robot can automatically patrol the environment whose environment information has been learned or plan a shortest path to visit some particular locations pre-specifies by the user.
To learn a new environment, a robot must first proceed to the exploration procedure (EP). In EP, a robot uses its vision and an infrared sensor to build a map of the unknown environment. The map is represented as a graph which consists of vertexes and edge. When a robot in navigating, a vertex is generated whenever a distinct environment (e.g. intersections, blind alleys, etc.) is detected and an edge is used to connect these vertex. At each vertex or particular location, images of the environment will be stared in a training data set. After the robot have finished the navigation tour and go back to the original starting position, a two-layer perceptron is trained to memorize the environment using the collected training data set. After the robot has built the environment map after the end of the EP, it enters the operation procedure (OP).
In OP, the robot may automatically patrol the environment and transmit images to remote clients via a web browser or execute a particular patrolling task assigned by the user. During a navigation tour, the robot knows its location by contract a match between the observation and the expectation as derived from the database. The match is computed by feeding the observation to the trained MLP. Finally, the performance of the proposed algorithm is demonstrated by training a SONY AIBO to navigate a home environment. | en_US |