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Weighted Shared-Autonomy with Assistance-to-Target and Collision Avoidance for Intelligent Assistive Robotics

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Intelligent Autonomous Systems 16 (IAS 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 412))

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Abstract

Intelligent Assistive Robotics (IAR) has been recently introduced as a branch of Service Robotics developing semi-autonomous robots helping people with physical disability in daily-living activities. Literature often focuses on the development of assistive robots with a single semi-autonomous behavior, while the integration of multiple assistance is rarely considered. In this paper, we propose a novel shared-autonomy controller integrating the contribution of two semi-autonomous behavioral modules: an assistance-to-target module, adjusting user’s input to simplify the target reaching, and a collision avoidance module, moving the robot away from trajectories leading to possible collisions with obstacles. An arbitration function based on the risk of collision is introduced to prevent conflicts between the two behaviors. The proposed controller has been successfully evaluated both offline and online in a reach-to-grasp task with a simulated robotic manipulator. Results show that the proposed methods significantly reduced not only the time to complete the task with respect to the pure teleoperation or controllers including just one semi-autonomous behavior, but also the user’s workload controlling the manipulator with a wearable interface. The context-awareness employed by the IAR may increase the reliability of the human-robot interaction, pushing forward the use of this technology in complex environments to assist disabled people at home.

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Notes

  1. 1.

    https://github.com/personalrobotics/ada_assistance_policy.

  2. 2.

    http://wiki.ros.org/safe_teleop_base.

  3. 3.

    https://developer.microsoft.com/it-it/windows/kinect.

  4. 4.

    https://www.universal-robots.com/it/.

  5. 5.

    https://robotiq.com/.

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Acknowledgements

This work has been supported by MIUR (Italian Minister for Education) under the initiative “Departments of Excellence” (Law 232/2016).

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Authors and Affiliations

  1. Department of Information Engineering (DEI), University of Padova, Padova, Italy

    Stefano Tortora, Roberto Sassi, Ruggero Carli & Emanuele Menegatti

Authors
  1. Stefano Tortora
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  2. Roberto Sassi
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  3. Ruggero Carli
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  4. Emanuele Menegatti
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Corresponding author

Correspondence to Stefano Tortora .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore

    Marcelo H. Ang Jr

  2. Department of Precision Engineering, University of Tokyo, Tokyo, Japan

    Hajime Asama

  3. Singapore Institute of Manufacturing Technology, Singapore, Singapore

    Wei Lin

  4. Engineering Product Development, Singapore University of Technology and Design, Singapore, Singapore

    Shaohui Foong

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Tortora, S., Sassi, R., Carli, R., Menegatti, E. (2022). Weighted Shared-Autonomy with Assistance-to-Target and Collision Avoidance for Intelligent Assistive Robotics. In: Ang Jr, M.H., Asama, H., Lin, W., Foong, S. (eds) Intelligent Autonomous Systems 16. IAS 2021. Lecture Notes in Networks and Systems, vol 412. Springer, Cham. https://doi.org/10.1007/978-3-030-95892-3_44

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