| Resumo: |
A Sensor-Based Gait Generation method was introduced and an experimental system was built. Then, the system was implemented onto an original humanoid robot to evaluate operations and to demonstrate effectiveness of the proposed method. Experimental results exhibited successful gait selection corresponding to the road surface condition obtained from sensor information. Additionally, walking velocity and the energy efficiency are both enhanced without reducing the success rate of walking. The design approach for Gait Selector based on both ZMP and the angular momentum adopted in this study is a sufficiently general and valid one. The developed Gait Selector should be applicable to many gaits and humanoids. However, more conditional branchings based not only on ZMP and the angular momentum but also on some combinations of them may be necessary depending on such factors as robot hardware, types of gaits and criteria for robot motion evaluation. The fundamental reason for the lack of a fixed design method is that the selection of gait is inherently rooted in factors such as hardware specifications and characteristics of each gait. At present, therefore, we have to redesign the Gait Selector such as that in Fig.6 according to the procedure described in Section 3. Future studies should be targeted to simplify the design procedure of Gait Selector. The more gait modules and ground conditions are installed into the system, the more complicated parameter tuning must be required. One possibility of avoiding this problem would be to introduce simple learning capability for Gait Selector design. A discrimination method that only utilizes sensor value histories of 3-axis accelerometer to identify several ground conditions (Miyasita2006) was already reported. They employ simple decision tree constructed based on acceleration data that are obtained during several trial motions on each ground condition. There is a possibility of direct acquisition of transition rules by utilizing histories of ZMP and angular momentum with all combinations of a gait module and a ground condition. Apart from the improvement of the design of Gait Selector, there also is a room for improvements by adding new gait generation modules and improving the success rate of walk through the enhancement of the transition scheme for gait module changes. These are more straightforward tasks if the required additional computational power is available.
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