Abstract
Elderly and disabled people often require assistance in getting about with maximum freedom and control while maintaining overall safety. In this chapter, we develop a collaborative learning optimization strategy for shared control of an intelligent walking-aid robot for the purpose of assisting elderly and disabled people. The proposed architecture can adjust two user control weights dynamically by a learning algorithm according to user control habit and walking environment, allowing both human and robot to maintain control of the walking-aid robot. Finally, the experiment results illustrate the validity of the collaborative learning optimization strategy as part of a shared control algorithm.
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Acknowledgments
This work is supported by the Chinese national science and technology support project “Research and development of multi-functional walking-aid system” under Grant 2012BAI33B04, and the International Science & Technology Cooperation Program of China (Precision Manufacturing Technology and Equipment for Metal Parts under Grant No.2012DFG70640), and is also supported by International Science & Technology Cooperation Program of Hubei Province under Grant 2012IHA00601 “Joint Research on Green Smart Walking Assistance Rehabilitant Robot.”
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Xu, W., Huang, J., Wang, Y., Tao, C. (2014). A Collaborative Learning Optimization Strategy for Shared Control of Walking-Aid Robot. In: Liu, L., Zhu, Q., Cheng, L., Wang, Y., Zhao, D. (eds) Applied Methods and Techniques for Mechatronic Systems. Lecture Notes in Control and Information Sciences, vol 452. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36385-6_21
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DOI: https://doi.org/10.1007/978-3-642-36385-6_21
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