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QoS metrics-in-the-loop for endowing runtime self-adaptation to robotic software architectures

  • 1188: Artificial Intelligence for Physical Agents
  • Published:
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Abstract

The design of robots capable of operating autonomously in changing and unstructured environments, requires using complex software architectures in which, typically, robot engineers manually hard-code adaptation mechanisms allowing the robot to deal with certain situations. As adaptation is closely related with context monitoring, deliberation and actuation, its implementation typically spreads across several architecture components. Therefore, fine-tuning or extending the adaptation logic (e.g., to cope with new contingencies not foreseen at design-time) results in a very expensive and cumbersome process. This paper proposes a novel approach to deal with self-adaptation based on modeling behavior variability at design-time so that the robot can configure it at runtime, according to the contextual information only then available. This approach is supported by a model-based framework allowing robotic engineers to specify (1) the robot behavior variation points (open decision space); (2) the internal and external contextual information available; and (3) the non-functional properties (e.g. safety, performance, or energy consumption) in terms of which the robot Quality-of-Service (QoS) will be measured. Then, from these models, the framework will automatically generate the runtime infrastructure allowing the robot to self-adapt its behavior to achieve the best QoS possible according to its current context. The framework has been validated in two scenarios using two different well-known robotic software architectures.

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Notes

  1. https://github.com/BehaviorTree/BehaviorTree.CPP

  2. In this paper, a task describes how a robot does something in an abstract and independent manner, and a skill provides access to functionalities realized by components for the usage within tasks.

  3. See https://www.behaviortree.dev/tutorial_02_basic_ports/ for details about the blackboard

  4. https://robmosys.eu/wiki/baseline:environment_tools:smartsoft:start

  5. http://www.mira-project.org/joomla-mira/

  6. http://www.esmera-project.eu/sa3ir/

  7. http://www.esmera-project.eu/welcome/

  8. https://robmosys.eu/wiki/baseline:environment_tools:roqme-plugins

  9. https://wiki.servicerobotik-ulm.de/about-smartsoft:robotic-behavior:smarttcl

  10. https://robocomp.github.io/web/

  11. https://github.com/AprilRobotics/apriltag

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Acknowledgements

This work has been partially funded by MIRoN (an Integrated Technical Project funded by EU H2020 RobMoSys Project under Grant Agreement 732410), SA3IR (an experiment funded by EU H2020 ESMERA Project under Grant Agreement 780265), and the project RTI2018-099522-B-C4X, funded by the Gobierno de España and FEDER funds.

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Authors and Affiliations

  1. Universidad de Málaga, Avda. Cervantes, 29071, Málaga, Spain

    A. Romero-Garcés, R. Marfil, J. Martínez & A. Bandera

  2. Rapyuta Robotics Co., Ltd., 4 Chome-10-5 Hirano, Koto City, Tokyo, 135-0023, Japan

    R. Salles De Freitas

  3. EPCC - Universidad de Extremadura, Avda. de la Universidad S/N, 10003, Cáceres, Spain

    C. Vicente-Chicote

  4. Biometric Vox, S.L. Galería Comercial “Los Collados”, 30100, Espinardo, Murcia, Spain

    J. F. Inglés-Romero

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  1. A. Romero-Garcés
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  2. R. Salles De Freitas
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  3. R. Marfil
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Correspondence to A. Bandera.

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Romero-Garcés, A., Salles De Freitas, R., Marfil, R. et al. QoS metrics-in-the-loop for endowing runtime self-adaptation to robotic software architectures. Multimed Tools Appl 81, 3603–3628 (2022). https://doi.org/10.1007/s11042-021-11603-7

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  • DOI: https://doi.org/10.1007/s11042-021-11603-7

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