ReBeT: Architecture-based Self-adaptation of Robotic Systems through Behavior Trees

Elvin Alberts*, Ilias Gerostathopoulos, Vincenzo Stoico, Patricia Lago

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Robotics software needs to be self-adaptive. Self-adaptation in robotics can, among others, take the form of changing a robot's task plan or its software architecture at runtime. The latter has shown to be effective in satisfying quality requirements such as minimizing energy consumption and operating safely. However, most self-adaptive robotic systems perform architecture-based self-adaptation to meet the functional goal of completing an assigned mission. Additionally, the mechanisms to accomplish architectural adaptations are mostly adhoc and not oriented towards reuse. We in turn investigate how quality requirements and architecture-based self-adaptation can be facilitated in robotics software while integrating into existing practices to promote practitioners' adoption and reuse. To this end, we design and implement an extension to the Behavior Trees (BTs) task plan formalism which introduces an explicit consideration of quality requirements. Additionally, we implement a general architectural adaptation layer for ROS2 systems and an extension to BTs which showcases its utilization. Finally, we perform quantitative experiments to evaluate the effectiveness of our approach in satisfying quality requirements via architectural adaptation on a mobile terrestrial robot. We find our approach to be an effective means to address a variety of self-adaptation scenarios within the mission of the system.

Original languageEnglish
Title of host publication2024 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)
Subtitle of host publication[Proceedings]
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-10
Number of pages10
ISBN (Electronic)9798350363876
ISBN (Print)9798350363883
DOIs
Publication statusPublished - 2024
Event2024 IEEE International Conference on Autonomic Computing and Self-Organizing Systems, ACSOS 2024 - Aarhus, Denmark
Duration: 16 Sept 202420 Sept 2024

Conference

Conference2024 IEEE International Conference on Autonomic Computing and Self-Organizing Systems, ACSOS 2024
Country/TerritoryDenmark
CityAarhus
Period16/09/2420/09/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

  • behavior trees
  • quality
  • robotics
  • self-adaptation

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