Intelligent assistance for older adults via an admittance-controlled wheeled mobile manipulator with task-dependent end-effectors. (August 2022)

• Record Type: Journal Article
• Authors: Ding, Liang; Xing, Hongjun; Torabi, Ali; Mehr, Javad K.; Sharifi, Mojtaba; Gao, Haibo; Mushahwar, Vivian K.; Tavakoli, Mahdi
• Is Part Of: Mechatronics. Volume 85(2022)
• Publication Date: 2022-08
• Subjects: Wheeled mobile manipulator (WMM) -- Mobility assistance -- Human–robot collaboration (HRC) -- Compliance control -- Learning from demonstration (lfD); Computer integrated manufacturing systems -- Periodicals; Flexible manufacturing systems -- Periodicals; Mechatronics -- Periodicals; Productique -- Périodiques; Fabrication, Systèmes flexibles de -- Périodiques; Mécatronique -- Périodiques; Computer integrated manufacturing systems; Flexible manufacturing systems; Mechatronics; Periodicals; 629.89
• DOI: 10.1016/j.mechatronics.2022.102821
• Languages: English
• ISSNs: 0957-4158

Abstract

Abstract: The increase in the ageing population worldwide poses a severe challenge in assisting older individuals to live independently, including the provision of mobility assistance and support in daily activities. In this paper, a practical robotic system is developed to provide intelligent support for older persons using a wheeled mobile manipulator (WMM), consisting of an omnidirectional mobile platform and a robotic arm. We focus on two critical needs: (1) mobility assistance, and (2) object manipulation support. The tasks are not executed simultaneously and each uses a task-dependent end-effector. Learning from demonstration, or kinesthetic teaching, is adopted to help the WMM to learn an elderly or disabled user's walking pattern or an able-bodied person's object manipulation skill. The robotic system can assist the user in conducting a number of daily operations. For mobility assistance, the WMM is reconfigured into a smart walker, where a novel variable admittance control is adopted to detect the user's walking intention. A learning approach based on dynamic movement primitives is implemented to capture and adapt the WMM to the user's walking pattern. For object manipulation support, a demonstrator first collaborates with an elderly user to conduct the task, and then the WMM takes the role of the demonstrator to assist the user. The Gaussian mixture model and Gaussian mixture regression are used to learn and reproduce the demonstrator's experience, respectively. The advantages and effectiveness of the proposed approach are experimentally demonstrated with a four-wheel omnidirectional WMM.