Human motion planning studies are of considerable importance in producing human-like trajectories for various industrial or clinical applications (e.g. assistive robots). In this case, the capability of Central Nervous System (CNS) in generating a large repertoire of actions can be inspirational to develop more efficient motion planning approaches. Here, inspired by structural and functional modularity in the CNS, a novel modular and hierarchical model is developed to plan the sit-to-stand (STS) transfer under varying environmental conditions. In this model, the planning process is distributed among several functionally simple modules. The cooperation of modules enables the model to plan the motion under a variety of conditions. The proposed model is assessed by planning the STS transfer under two types of environmental conditions: varying seat heights and varying base of support areas. The results revealed a suitable fit between the planned trajectories and the experimental trajectories for different conditions. It is demonstrated that a modular motion planner provides a higher accuracy and flexibility for the model to extend the planning process to various new conditions, yet still requires less computational complexity when compared with previous approaches. The proposed model is also supported by several behavioral and neurophysiological evidences.
A bio-inspired modular hierarchical structure to plan the sit-to-stand transfer under varying environmental conditions
EMADI ANDANI, Mehran;
2013-01-01
Abstract
Human motion planning studies are of considerable importance in producing human-like trajectories for various industrial or clinical applications (e.g. assistive robots). In this case, the capability of Central Nervous System (CNS) in generating a large repertoire of actions can be inspirational to develop more efficient motion planning approaches. Here, inspired by structural and functional modularity in the CNS, a novel modular and hierarchical model is developed to plan the sit-to-stand (STS) transfer under varying environmental conditions. In this model, the planning process is distributed among several functionally simple modules. The cooperation of modules enables the model to plan the motion under a variety of conditions. The proposed model is assessed by planning the STS transfer under two types of environmental conditions: varying seat heights and varying base of support areas. The results revealed a suitable fit between the planned trajectories and the experimental trajectories for different conditions. It is demonstrated that a modular motion planner provides a higher accuracy and flexibility for the model to extend the planning process to various new conditions, yet still requires less computational complexity when compared with previous approaches. The proposed model is also supported by several behavioral and neurophysiological evidences.
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