MoBAr: a Hierarchical Action-Oriented Autonomous Control Architecture

  • Pablo Muñoz
  • María D. R-Moreno
  • David F. Barrero
  • Fernando Ropero
Article
First Online:
Received:
Accepted:
  • 21 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 
This is a preview of subscription content, log in to check access

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

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.

References

  1. 1.
    Bresina, J., Morris, P.: Mission operations planning: beyond MAPGEN. In: The 2nd IEEE International Conference on Space Mission Challenges for Information Technology. Pasadena, CA, USA (2006)Google Scholar
  2. 2.
    Cesta, A., Cortellessa, G., Fratini, S., Oddi, A., Denis, M., Donati, A., Policella, N., Rabenau, E., Schulster, J.: MEXAR2: AI solves mission planner problems. IEEE Intell. Syst. 22(4), 12–19 (2007)CrossRefGoogle Scholar
  3. 3.
    Gat, E.: On three-layer architectures. Mobile Robots and Artificial Intelligence 1, 195–210 (1998)Google Scholar
  4. 4.
    McDermott, D.: PDDL: the planning domain definitin language. AI Mag. 21(2), 35–55 (2000)Google Scholar
  5. 5.
    Verma, V., Jónsson, A., Pasareanu, C., Iatauro, M.: Universal executive and PLEXIL: engine and language for robust spacecraft control and operations. In: The American Institute of Aeronautics and Astronautics Space 2006 Conference. San Jose, CA, USA (2006)Google Scholar
  6. 6.
    Alami, R., Chatila, R., Fleury, S., Ghallab, M., Ingrand, F.: An architecture for autonomy. International Journal of Field Robotics, Special Issue on Integrated Architectures for Robot Control and Programming 17, 315–337 (1998)Google Scholar
  7. 7.
    Fleury, S., Herrb, M., Mallet, A.: GenoM: User’s Guide. CNRS; LAAS (2010)Google Scholar
  8. 8.
    Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software (2009)Google Scholar
  9. 9.
    Sanchez-Lopez, J.L., Molina, M., Bavle, H., Sampedro, C., Suárez Fernández, R.A., Campoy, P.: A multi-layered component-based approach for the development of aerial robotic systems: the aerostack framework. J. Intell. Robot. Syst. 88(2), 683–709 (2017)CrossRefGoogle Scholar
  10. 10.
    Brooks, R.A.: A robust layered control system for a mobile robot. IEEE J. Robot. Autom. 2, 14–23 (1986)CrossRefGoogle Scholar
  11. 11.
    Brooks, R.A.: How to Build Complete Creatures Rather than Isolated Cognitive Simulators. Lawrence Erlbaum Assosiates, Hillsdale (1991)Google Scholar
  12. 12.
    Firby, R.J.: An investigation into reactive planning in complex domains. In: The 6th National Conference on Artificial Intelligence (AAAI). Seattle, DC, USA, pp 202–206 (1987)Google Scholar
  13. 13.
    Dorigo, M., Tuci, E., Trianni, V., GroB, R., Nouyan, S., Ampatzis, C., Labella, T.H., O’Grady, R., Bonani, M., Mondada, F.: Swarm-Bot: Design and Implementation of Colonies of Self-Assembling Robots, ch. 6. Computational Intelligence: Principles and Practice, NY: IEEE Computational Intelligence Society, pp. 106–135 (2006)Google Scholar
  14. 14.
    Vernon, D., Metta, G., Sandini, G.: A survey of artificial cognitive systems: implications for the autonomous development of mental capabilities in computational agents. IEEE Trans. Evol. Comput. 11, 151–180 (2007)CrossRefGoogle Scholar
  15. 15.
    Milnes, B.G., Pelton, G., Doorenbos, R., Hucka, M., Laird, J.E., Rosenbloom, P., Newell, A.: A specification of the soar cognitive architecture in Z. Tech. Rep., School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA (1992)Google Scholar
  16. 16.
    Anderson, J.R., Bothell, D., Byrne, M.D., Douglass, S., Lebiere, C., Quin, Y.: An integrated theory of the mind. Psychol. Rev. 111(4), 1036–1060 (2004)CrossRefGoogle Scholar
  17. 17.
    Langley, P., Choi, D., Rogers, S.: Interleaving Learning, Problem Solving, and Execution in the ICARUS Architecture. Tech. Rep., Computational Learning Laboratory, Standford University (2005)Google Scholar
  18. 18.
    Langley, P., Choi, D.: A unified cognitive architecture for physical agents. In: The 21th National Conference on Artificial Intelligence (AAAI). Boston, MA, USA, pp 1469–1474 (2006)Google Scholar
  19. 19.
    Gat, E.: Integrating planning and reacting in a heterogeneous asynchronous architecture for controlling real-world mobile robots. In: The 10th National Conference on Artificial Intelligence (AAAI). San Jose, CA, USA, pp 809–815 (1992)Google Scholar
  20. 20.
    Konolige, K., Myers, K.: The saphira architecture for autonomous mobile robots. Tech. Rep., MIT Press (1996)Google Scholar
  21. 21.
    Muscettola, N., Nayak, P.P., Pell, B., Williams, B.C.: Remote agent: to boldly go where no AI system has gone before. Artif. Intell. 103, 5–48 (1998)CrossRefMATHGoogle Scholar
  22. 22.
    Ingrand, F., Lacroix, S., Lemai-Chenevier, S., Py, F.: Decisional autonomy of planetary rovers. Field Robotics 24(7), 559–580 (2007)CrossRefGoogle Scholar
  23. 23.
    Volpe, R., Nesnas, I., Estlin, T., Mutz, D., Petras, R., Das, H.: The claraty architecture for robotic autonomy. In: The IEEE Aeroespace Conference. Big Sky Montana (2001)Google Scholar
  24. 24.
    Nesnas, I., Simmons, R., Gaines, D., Kunz, C., Diaz-Calderon, A., Estlin, T., Madison, R., Guineau, J., McHenry, M., Shu, I.-H., Apfelbaum, D.: CLARAty: challenges and steps toward reusable robotic software. Advanced Robotic Systems 3(1), 23–30 (2006)Google Scholar
  25. 25.
    Tompkins, P., Stentz, A., Wettergreen, D.: Mission-level path planning and re-planning for rover exploration. Robotics and Autonomous Systems, Intelligent Autonomous Systems 54, 174–183 (2006)CrossRefGoogle Scholar
  26. 26.
    Kim, G., Chung, W., Kim, M., Lee, C.: Implementation of multi-functional service robots using tripodal schematic control architecture. In: The IEEE International Conference on Robotics and Automation. Barcelona, Spain, pp 4005–4010 (2004)Google Scholar
  27. 27.
    Kim, G., Chung, W.: Tripodal schematic control architecture for integration of multi-functional indoor service robots. IEEE Trans. Ind. Electron. 53(5), 1723–1736 (2006)CrossRefGoogle Scholar
  28. 28.
    Dias, M.B., Lemai, S., Muscettola, N.: A real-time rover executive based on model-based reactive planning. Tech. Rep. 169, Robotics Institute (2003)Google Scholar
  29. 29.
    R-Moreno, M.D., Brat, G., Muscettola, N., Rijsman, D.: Validation of a multi-agent architecture for planning and execution. In: The 18th International Workshop on Principles of Diagnosis. Nashville, TN, USA, pp 368–371 (2007)Google Scholar
  30. 30.
    Hsu, H.C.-H., Liu, A.: A flexible architecture for navigation control of a mobile robot. IEEE Trans. Syst. Man Cybern. 37, 310–318 (2007)CrossRefGoogle Scholar
  31. 31.
    Innocenti, B., Lopez, B., Salvi, J.: Design patterns for combining social and individual intelligences on modular-based agents. In: The 3rd International Workshop on Hybrid Artificial Intelligence Systems. Salamanca, Spain, pp 70–77 (2008)Google Scholar
  32. 32.
    McGann, C., Py, F., Rajan, K., Thomas, H., Henthorn, R., McEwen, R.: A Deliberative Architecture for AUV control. In: The IEEE International Conference on Robotics and Automation. Pasadena, California, USA (2008)Google Scholar
  33. 33.
    Ceballos, A., Bensalem, S., Cesta, A., Silva, L.D., Fratini, S., Ingrand, F., Ocon, J., Orlandini, A., Py, F., Rajan, K., Rasconi, R., Winnendael, M.V.: A goal-oriented autonomous controller for space exploration. In: The 11th Symposium on Advanced Space Technologies in Robotics and Automation. Noordwijk, The Netherlands (2011)Google Scholar
  34. 34.
    McDermott, D.: The PDDL Planning Domain Definition Language. The AIPS-98 Planning Competition Comitee. Pittsburgh, Pennsylvania, USA (1998)Google Scholar
  35. 35.
    Hoffmann, J., Nebel, B.: The FF planning system: fast plan generation through heuristic search. Artificial Intelligence Research 14, 253–302 (2001)MATHGoogle Scholar
  36. 36.
    Benton, J., Coles, A., Coles, A.: Temporal planning with preferences and time-dependent continuous costs. In: The 22th International Conference on Automated Planning and Scheduling (ICAPS). Sao Paulo, Brazil (2012)Google Scholar
  37. 37.
    Hsu, C., Wah, B.: The SGPlan planning system in IPC-6. In: The 6th International Planning Competition. Sydney, Australia (2008)Google Scholar
  38. 38.
    Muñoz, P., R-Moreno, M.D., Barrero, D.F.: Unified framework for path planning and task planning for autonomous robots. Robot. Auton. Syst. 82, 1–14 (2016)CrossRefGoogle Scholar
  39. 39.
    Fox, M., Long, D.: PDDL2.1: an extension to PDDL for expressing temporal planning domains. AI Research 20, 61–124 (2003)MATHGoogle Scholar
  40. 40.
    Gerevini, A., Long, D.: Plan Constraints and Preferences in PDDL3. In: The International Conference on Automated Planning & Scheduling, the Language of the 5th International Planning Competition. The English Lake District, Cambria, UK (2005)Google Scholar
  41. 41.
    Muñoz, P., Cesta, A., Orlandini, A., R-Moreno, M.D.: The on-ground autonomy test environment: OGATE. In: Proceedings of the 13th ESA Workshop on Advanced Space Technologies for Robotics and Automation. Noordwijk, The Netherlands (2015)Google Scholar
  42. 42.
    Muñoz, P., Cesta, A., Orlandini, A., R-Moreno, M.D.: Defining metrics for autonomous controllers assessment. In: Proceedings of the 6th International IEEE Conference on Space Mission Challenges for Information Technology. Alcalá De Henares, Spain (2017)Google Scholar
  43. 43.
    Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y.: Ros: an open-source robot operating system. In: ICRA Workshop on Open Source Software, vol. 3, p 5 (2009)Google Scholar
  44. 44.
    Gupta, N., Nau, D.S.: On the complexity of blocks-world planning. Artif. Intell. 56(2-3), 223–254 (1992)MathSciNetCrossRefMATHGoogle Scholar
  45. 45.
    Poulakis, P., Joudrier, L., Wailiez, S., Kapellos, K.: 3DROV a planetary rover system design, simulation and verification tool. In: International Symposium on Artificial Intelligence, Robotics and Automation in Space (I-SAIRAS). Hollywood, California, USA (2008)Google Scholar
  46. 46.
    Muñoz, P., R-Moreno, M.D.: S-Theta*: low steering path-planning algorithm. In: AI-2012: the 32th SGAI International Conference. Cambridge, UK, pp 109–121 (2012)Google Scholar
  47. 47.
    Muñoz, P., R-Moreno, M.D., Castaño, B.: 3Dana: a path planning algorithm for surface robotics. Eng. Appl. Artif. Intel. 60, 175–192 (2017)CrossRefGoogle Scholar
  48. 48.
    Ropero, F., Vaquerizo, D., Muñoz, P., R-Moreno, M.D.: An advanced teleassistance system to improve life quality in the elderly. In: Proceedings of the 30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems. Arras, France (2017)Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Departamento de AutomáticaUniversidad de AlcaláMadridSpain

Personalised recommendations