Exoskeleton robots are electrically, pneumatically, or hydraulically actuated devices that externally support the bones and cartilage of the human body while trying to mimic the human movement capabilities and augment muscle power. The lower extremity exoskeleton device may support specific human joints such as hip, knee, and ankle, or provide support to carry and balance the weight of the full upper body. Their assistive functionality for physically-abled and disabled humans is demanded in medical, industrial, military, safety applications, and other related fields. The vision of humans walking with an exoskeleton without external support is the prospect of the robotics and artificial intelligence working groups. This paper presents a survey on the design and control of lower extremity exoskeletons for bipedal walking. First, a historical view on the development of walking exoskeletons is presented and various lower body exoskeleton designs are categorized in different application areas. Then, these designs are studied from design, modeling, and control viewpoints. Finally, a discussion on future research directions is provided.
This paper shows that controlling only a small set of adequately selected tasks is sufficient to closely reproduce the human gait kinematics. To this aim, a hierarchical controller is applied to a whole-body model including 42 degrees of freedom with 3 hierarchical tasks. The analysis of the simulated gaits shows the emergence of significant human-like properties in walking. In order to validate our results, a comparison between joint rotations in the simulated motion and in human reference motions is conducted. Finally, a discussion is given to illustrate the interest of the approach in view of related works.
Bipedal gait is the natural means of human locomotion. Nonetheless, it is still unclear how the central nervous system coordinates the whole-body segments for gait generation. We address this question based on the wellknown hypothesis that the human motion is the result of an optimization process. We consider a reduced set of criteria taken from the observation of human walking and the study of the related literature, which seem to be optimized during the human gait. Differential Dynamic Programming is applied on these criteria with a 3D whole-body skeletal model involving 42 degrees of freedom to generate walking motions. Nine different skeletal models and gaits reconstructed from motion capture data are used to this end. The simulated walking motions are then analyzed and compared to the human reference to show the quality of the gait generation process. The interest of this optimization approach for human-like motion generation is finally discussed.
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