In this study, we propose a scheme that enables a humanoid robot to perform cane-supported walking and to select the optimal cane usage depending on its condition. A reaction force is generated through the cane-ground...
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In this study, we propose a scheme that enables a humanoid robot to perform cane-supported walking and to select the optimal cane usage depending on its condition. A reaction force is generated through the cane-ground interaction. Although the optimal cane-ground contact position leads to the most effective reaction force from the viewpoint of the walking capability, the cane position is constrained during such contact. Thus, different cane utilities can be achieved based on the priority assigned to the reaction force and cane position. In this light, we propose cyclic, leg-like, and preventive usages, each of which has different priorities. These different priorities lead to different utilities such as the expansion of the support polygon, load distribution, and posture recovery. The robot selects the most suitable of these three cane usages using a selection algorithm for locomotion that is combined with Q-learning (Us-SAL). Through simulations, the robot is made to learn the affinities between these cane usages and falling factors as Q-values and to accordingly select the optimal cane usage. As a result, the robot can perform the desired walking by selecting the optimal cane usage depending on its condition. We conducted walking experiments for each cane usage and found that each improved the walking stability for a suitable falling factor. Furthermore, we experimentally validated Us-SAL;the robot selected the optimal cane usage depending on its condition and walked in a complex environment without falling. (C) 2015 Elsevier B.V. All rights reserved.
Humanoid robots are required to improve its walking ability (stability and efficiency) in order to adapt to various environments. Extension of the embodiment by a cane is effective as well as human like elderly. In th...
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ISBN:
(纸本)9781479971749
Humanoid robots are required to improve its walking ability (stability and efficiency) in order to adapt to various environments. Extension of the embodiment by a cane is effective as well as human like elderly. In this paper, we propose a scheme that enables the robot to perform cane-supported walking and select the optimal cane usage based on its condition so as to maximize the performance of cane support. For cane-supported walking, we propose cyclic and leg-like usages, which correspond to two-point and three-point gaits. Our strategy for selecting the optimal cane usages, which have respective utilities, utilizes falling factors in selection algorithm for locomotion (Us-SAL). We express affinities between the cane usages and the falling factors as Q-values, which are learned by Q-learning, and select the optimal usage based on the Q-values. Us-SAL was validated by an experiment;the robot selected the optimal usage for its condition, and walked efficiently without falling.
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