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Titlebook: Robotics Research; The 13 International Makoto Kaneko,Yoshihiko Nakamura Conference proceedings 2011 Springer-Verlag Berlin Heidelberg 2011

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21#
發(fā)表于 2025-3-25 05:12:33 | 只看該作者
Optimization Design of a Stewart Platform Type Leg Mechanism for Biped Walking Vehicle,c simulation and a real-coded genetic algorithm. Using effective joint arrangement, the maximum RMS (root-mean-square) value of the current will be able to be reduced. A new prototype of a biped walking vehicle, WL-16RIV, was developed by using the optimal joint arrangement method. Weight saving in
22#
發(fā)表于 2025-3-25 09:43:51 | 只看該作者
23#
發(fā)表于 2025-3-25 12:20:36 | 只看該作者
Visual Object Tracking Using Positive and Negative Examples, use apparent features of target object as a model. Those algorithms detects objects by thresholding the similarity or the dissimilarity measure with the model. They sometimes fail to detect objects because of inadequate threshold. In this paper, we propose a method for detection and tracking using
24#
發(fā)表于 2025-3-25 19:52:01 | 只看該作者
Large-Scale Visual Odometry for Rough Terrain, using standard techniques. We present the results of several years of work on an integrated system to localize a mobile robot in rough outdoor terrain using visual odometry, with an increasing degree of precision. We discuss issues that are important for real-time, high-precision performance: choic
25#
發(fā)表于 2025-3-25 20:56:27 | 只看該作者
26#
發(fā)表于 2025-3-26 02:17:54 | 只看該作者
27#
發(fā)表于 2025-3-26 04:21:02 | 只看該作者
28#
發(fā)表于 2025-3-26 11:13:34 | 只看該作者
HAL: Hybrid Assistive Limb Based on Cybernics,ary Control System” and “Cybernic Autonomous Control System”. The application fields of HAL are medical welfare, heavy work support and entertainment etc. In this paper, the outline of HAL and some of the important algorithms and recent challenges are described.
29#
發(fā)表于 2025-3-26 15:18:30 | 只看該作者
30#
發(fā)表于 2025-3-26 20:35:43 | 只看該作者
Incremental Learning of Statistical Motion Patterns with Growing Hidden Markov Models,hed. In this paper, we present an approach which is able to learn new motion patterns incrementally, and in parallel with prediction. Our work is based on a novel extension to Hidden Markov Models called Growing Hidden Markov models.
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