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MoBAr: a Hierarchical Action-Oriented Autonomous Control Architecture

  • Pablo Muñoz
  • María D. R-Moreno
  • David F. Barrero
  • Fernando Ropero
Article
  • 64 Downloads

Abstract

Autonomous control in robotics hold promising solutions for a broad number of applications. However, autonomous controllers require highly expertise on heterogeneous technologies, such as Artificial Intelligence Planning & Scheduling, behaviour modelling, intelligent execution and the hardware to control. Connecting these technologies entails several challenges to properly synchronize and verify the robot behaviours to deal with real scenarios. In this article, we present an autonomous controller based on high level modelling to easily enable adaptation of the controller to different robotics platforms and application domains. This controller, called MoBAr, allows on-board planning and replanning for goal oriented autonomy. It relies on technologies such as PLEXIL to model the execution behaviours, or the action oriented planning language PDDL for the domain definition and the planning process. Based on these technologies MoBAr enables an easier deployment of the autonomous controller for different robotics platforms. Moreover, MoBAr enables researching in planning systems applied to robotics domains, as it is possible to replace the PDDL planner and/or domain used without much effort. This fact is demonstrated in the experimental section, in which we demonstrate the adaptability and effectiveness of the controller in three different scenarios, i.e., a robotic arm, an office surveillance robot and an exploration rover while exploiting different planning systems.

Keywords

Autonomous control Robotics Planning & scheduling Planning & execution Autonomous exploration 

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Notes

Acknowledgments

This work is partially supported by the European Space Agency (ESA) under the Networking and Partnering Initiative “Cooperative Systems for Autonomous Exploration Missions” project 4000106544/12/NL/PA. Pablo Muñoz is supported by UAH grant 30400M000.541A. 640.17. María D. R-Moreno is supported by MINECO project EphemeCH TIN2014-56494-C4-4-P and UAH 2016/00351/001. Authors want to thanks Alejandro Mora Prieto and Diego López Pajares for their work with the experimental cases.

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Departamento de AutomáticaUniversidad de AlcaláMadridSpain

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