碩士 / 國立中興大學 / 電機工程學系所 / 105 / This thesis proposes a mixed neural network (NN) for a hexapod robot locomotion control. The mixed NN consists of a seven-node fully connected recurrent NN (FCRNN) controller for straight forward walking and a two-node sensor-feedback NN (SFNN) controller for obstacle boundary following (OBF). The seven-node FCRNN locomotion controller controls each hip joint of the robot by independent signals to improve the walking performance. The two-node SFNN OBF controller uses a few parameters to realize the function of detecting an obstacle and walking along its boundary. A left-right symmetric structure is adopted to reduce training cost and NN model size in building a complete collision-avoidance controller. A training environment and multi-objective functions are designed to perform evolutionary parameter learning of the two controllers using the non-dominated sorting genetic algorithm-II (NSGA-II). Besides, a proportional-integral-derivative (PID)-based target searching (TS) controller is used to merge with the mixed NN collision-avoidance controller to realize the hexapod robot navigation. In the end, simulation results in training and test environments verify the effectiveness of the control methods proposed in this thesis.
| Identifer | oai:union.ndltd.org:TW/105NCHU5441041 |
| Date | January 2017 |
| Creators | Yan-Ming Chen, 陳彥銘 |
| Contributors | Chia-Feng Juang, Wu-Chung Su, 莊家峰, 蘇武昌 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | en_US |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 50 |
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