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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yong Jiang Ming-Yang Zhao Hong-Guang Wang Li-Jin Fang |
| Copyright Year | 2005 |
| Description | Author affiliation: Robotics Lab., Chinese Acad. of Sci., Shenyang, China (Yong Jiang; Ming-Yang Zhao; Hong-Guang Wang; Li-Jin Fang) |
| Abstract | In this paper, a hybrid navigation method for the autonomous control of a miniature climbing robot is presented. The method of navigation blends the optimality of the trajectory planning algorithm with the capabilities in expressing knowledge and learning of the fuzzy neural network. The actual task environment of the climbing robot is both known and dynamic. Therefore the trajectory planning is used to search roughly the optimal trajectories towards the goal based the priori data. Meanwhile, by the multi-sensor data fusion process, the fuzzy neural network is employed in dealing properly with the uncertain and dynamic situations. The experiment platform of the miniature climbing robot is also described in the paper. The properties of the hybrid navigation method are verified by the computer simulation. |
| Sponsorship | IEEE Syst., Man and Cybernetics Tech. Comm. on Cybernetics, Hong Kong Polytechnic Univ. Hebei Univ. South China Univ. Chongqing Univ. Sun Yat-sen Univ. Harbin Inst. of Technol. and Int. Univ. in Germany |
| Starting Page | 1069 |
| Ending Page | 1075 |
| File Size | 561276 |
| Page Count | 7 |
| File Format | |
| ISBN | 0780390911 |
| DOI | 10.1109/ICMLC.2005.1527102 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2005-08-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Navigation Climbing robots Fuzzy neural networks Trajectory Fuzzy control Leg Robotics and automation Legged locomotion Foot Switches climbing robot Hybrid navigation multi-sensor data fusion fuzzy neural network |
| Content Type | Text |
| Resource Type | Article |
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