碩士 / 國立虎尾科技大學 / 電機工程研究所 / 103 / This dissertation proposes novel behavior-based soft computing approaches for mobile robot navigation in an unknown environment. The behavior were divided three types, and each behavior was assigned a unique task. The first behavior was a multiple strategy artificial bee colony (MSABC) algorithm for designing a compensatory neuro-fuzzy controller (CNFC) to complete an actual mobile robot navigation task. For the second behavior, we designed a wall-following fuzzy logic controller for avoiding obstacles, third behavior was principle component analysis back propagation network(PCA-BPN) based fuzzy logic controller, which is diagnostic special environment for mobile robot escape from traps. During the navigation task, the CNFC inputs are the measured distance and angle between the mobile robot and a target, and the outputs of the CNFC are the robot''s left and right-wheel speeds. A fitness function was defined to evaluate the performance of the CNFC in the navigation task. The fitness function comprised the following three control factors (CF) : navigation time, the distance between start point and the target, and the distance between the mobile robot and target. The original artificial bee colony algorithm (ABC) simulates the intelligent foraging behavior of honey-bee swarms, which are effective for exploration but ineffective for exploitation. The proposed multiple strategy ABC algorithm(MSABC) adopts the mutation strategies of differential evolution to balance exploration and exploitation. The purpose of the wall-following obstacle-avoiding behavior is ensuring that when the mobile robot encounters an any obstacle, it can move along the wall and avoid the obstacle. The third behavior was designed to assist in evaluating special environment and creates virtual wall. If the mobile robot determine that it is in a special environment, it creates virtual wall and change to wall-following obstacle-avoiding behavior avoid virtual wall. The mobile robot''s second and third behaviors are designed to wall-following with avoid obstacles and virtual wall. To demonstrate the performance of the MSABC designed CNFC, the method was compared with other population-based algorithms with respect to the efficiency of the navigation task. To demonstrate the feasibility of the design, experiments carried out on an actual mobile robot (PIONEER 3-DX) are included in this research. In the propose method, we use novel behavior based soft computing approaches classify obstacle and create of the virtual wall make mobile robot more effectively avoid obstacle and reach the target.
| Identifer | oai:union.ndltd.org:TW/103NYPI5441019 |
| Date | January 2015 |
| Creators | Yao-Cheng Tsai, 蔡耀正 |
| Contributors | 陳政宏 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | en_US |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 79 |
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