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An Integrated Decision Making Approach for Adaptive Shared Control of Mobility Assistance Robots

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International Journal of Social Robotics Aims and scope Submit manuscript

Abstract

Mobility assistance robots provide support to elderly or patients during walking. The design of a safe and intuitive assistance behavior is one of the major challenges in this context. We present an integrated approach for the context-specific, on-line adaptation of the assistance level of a rollator-type mobility assistance robot by gain-scheduling of low-level robot control parameters. A human-inspired decision-making model, the drift-diffusion Model, is introduced as the key principle to gain-schedule parameters and with this to adapt the provided robot assistance in order to achieve a human-like assistive behavior. The mobility assistance robot is designed to provide (a) cognitive assistance to help the user following a desired path towards a predefined destination as well as (b) sensorial assistance to avoid collisions with obstacles while allowing for an intentional approach of them. Further, the robot observes the user long-term performance and fatigue to adapt the overall level of (c) physical assistance provided. For each type of assistance a decision-making problem is formulated that affects different low-level control parameters. The effectiveness of the proposed approach is demonstrated in technical validation experiments. Moreover, the proposed approach is evaluated in a user study with 35 elderly persons. Obtained results indicate that the proposed gain-scheduling technique incorporating ideas of human decision-making models shows a general high potential for the application in adaptive shared control of mobility assistance robots.

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Notes

  1. Please note that in a holonomic system also the force component in sidewards direction is used for motion control.

  2. Please note that the natural definition of mental and physical fatigue are closely related and it is commonly known that physical fatigue impairs mental fatigue. However, [43] has only recently shown that mental fatigue can also imply physical fatigue. Therefore, we just consider the effect of physical fatigue since this is the most probable cause of fatigue in a mobility assistance scenario.

  3. The work performed by a human to maneuver the platform has not been considered in the computation of human fatigue for the sake of simplicity.

  4. Please note that emphasizing mostly on the orientation error in the overall task performance measure was assumed only for the sake of presentation. However, one may associate different values for the contribution of each of the terms to the overall task performance.

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Acknowledgments

This work is supported in part by the MOBOT project within the 7th Framework Programme of the European Union, under the grant agreement n. 600796 and the Institute of Advanced Studies of the Technische Universität München. The authors would like to thank George Moustris and Costas Tzafestas for making algorithms for localization, mapping and path planning available and their help in integrating them on the robotic platform.

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Authors and Affiliations

  1. Chair of Automatic Control Engineering, Technische Universität München, Munich, Germany

    Milad Geravand

  2. Agaplesion Bethanien-Hospital, Geriatric Centre, University of Heidelberg, Heidelberg, Germany

    Christian Werner & Klaus Hauer

  3. Bristol Robotics Laboratory, University of the West of England, Bristol, UK

    Angelika Peer

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  1. Milad Geravand
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Correspondence to Milad Geravand.

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Geravand, M., Werner, C., Hauer, K. et al. An Integrated Decision Making Approach for Adaptive Shared Control of Mobility Assistance Robots. Int J of Soc Robotics 8, 631–648 (2016). https://doi.org/10.1007/s12369-016-0353-z

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