博士 / 國立交通大學 / 控制工程系 / 85 / An intelligent mobile robot should be able to navigate itself in
dynamic and unstructured environments. In such circumstances,
the motion of moving obstacles need to be estimated.
Due to the difficulties for present-day on-board
sensor system to get explicit motion information in real time,
we propose to construct an environment predictor to obtain
implicit motion information. Multiple sensors including
ultrasonic rangefinders and a CCD camera are used to
obtain motion prediction. The predictor is constructed by
artificial neural networks, which
are trained by a relative-error-backpropagation algorithm
developed in this dissertation. To handle multiple moving
obstacles, a reactive navigation method is
developed to work with the predictor based on virtual force
concept. Using the developed
bidirectional distance-transform path planner, the planned path
can be modified on-line based on a learned world model.
A method is proposed for absolute position estimation in an
indoor environment. Fused sensor data from encoders,
gyroscope, CCD camera, and ultrasonic
sensors are used to accomplish self-localization.
Simulation and experimental results are presented to verify the
proposed methods.
| Identifer | oai:union.ndltd.org:TW/085NCTU0327066 |
| Date | January 1997 |
| Creators | Chang, Charles!C., 張智超 |
| Contributors | Kai-Tai Song, 宋開泰 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 113 |
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