Diseño del software de control de un UUV para monitorización oceanográfica usando un modelo de componentes y framework con despliegue flexible

  1. Francisco Ortiz 1
  2. Antonio Guerrero 1
  3. Francisco Sánchez-Ledesma 1
  4. Francisco García-Córdova 1
  5. Diego Alonso 1
  6. Javier Gilabert 1
  1. 1 Universidad Politécnica de Cartagena
    info

    Universidad Politécnica de Cartagena

    Cartagena, España

    ROR https://ror.org/02k5kx966

Journal:
Revista iberoamericana de automática e informática industrial ( RIAI )

ISSN: 1697-7920

Year of publication: 2015

Volume: 12

Issue: 3

Pages: 325-337

Type: Article

DOI: 10.1016/J.RIAI.2015.06.003 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Revista iberoamericana de automática e informática industrial ( RIAI )

Abstract

Unmanned Underwater Vehicles (UUVs) explore different habitats with a view to protecting and managing them. They are developed to overcome scientific challenges and the engineering problems caused by the unstructured and hazardous underwater environment in which they operate. Their development bears the same difficulties as the rest of service robots (hardware heterogeneity, sensor uncertainty, software complexity, etc.) as well as other particular from the domain, like the underwater environment, energy constraints, and autonomy. This article describes the AEGIR UUV, used as a test bed for implementation of control strategies and oceanographic mission in the Mar Menor area in Spain, which is one of the largest coastal lagoons in Europe. It also describes the development of a tool chain that follows a model-driven approach, which has been used in the design of the vehicle control software as well as a component-based framework that provides the runtime support of the application and enables its flexible deployment in nodes, processes and threads and pre-verification of concurrent behavior.

Bibliographic References

  • Alonso, D., Pastor, J., Sánchez, P., Álvarez, B., Vicente-Chicote, C., 2012. Generación Automática de Software para Sistemas de Tiempo Real: Un Enfoque basado en Componentes, Modelos y Frameworks. Revista Iberoamericana de Automática e Informática Industrial RIAI. Vol, 9, Num. 2, págs 170–181, doi: 10.1016/j.riai.2012.02.010.
  • Alonso, D., Vicente-Chicote, C., Ortiz, F., Pastor, J., Álvarez, B., 2010. V3CMM: a 3-View Component Meta-Model for Model-Driven Robotic Software Development. Journal of Software Engineering for Robotics, Vol.1, no 1, pp. 3-17.
  • Antonelli, G., Chiaverini, S., Sarkar,N., West, M., 2001. Adaptive control of an autonomous underwater vehicle: experimental results on ODIN, IEEE Trans Control Syst. Technol., vol. 9, Issue: 5, Sep. 2001, pp. 756-765.
  • Auke Jan Ijspeert, 2008. Central pattern generators for locomotion control in animals and robots: A review. Neural Networks, Volume 21 Issue 4, pp. 642-653.
  • Ben-Ari, M., 2008. Principles of the Spin Model Checker. Springer-Verlag.
  • Bengtsson, J., Yi, W., 2004. Timed Automata: Semantics, Algorithms and Tools. In: Lectures on concurrency and Petri nets, Springer-Verlag, vol. 3098, pp. 87-124.
  • Behrmann, G., Larsen, K., Moller, O., David, A., Pettersson, P., Wang, Y., 2001. UPPAAL - present and future. Proc. of the 40th IEEE Conf. on Decision and Control.
  • Bruyninckx, H., 2008. Robotics Software: The Future Should Be Open. In: IEEE Robotics & Automation Magazine, Vol. 15, No. 1, pp. 9-11.
  • Carreras, M., Yuh, J., Batlle, J., Ridao, P., 2005. A behavior-based scheme Using Reinforcement Learning for Autonomous Underwater Vehicles. In: IEEE Journal of Oceanic Engineering, Vol. 30, No. 2.
  • Chang, C., and Gaudiano, P., 1998. Application of biological learning theories to mobile robot avoidance and approach behaviors. J. Complex Systems, vol. 1, pp. 79–114.
  • Eickstedt, D.P., Sideleau, S.R., 2009. The backseat control architecture for autonomous robotic vehicles: A case study with the Iver2 AUV. In: OCEANS 2009, MTS/IEEE Biloxi - Marine Technology for Our Future: Global and Local Challenges. 1-8.
  • Fossen, T., 1994. Guidance and control of ocean vehicles. John Wiley and Sons Ltd.
  • Fujii, T., Arai, Y., Asama, H., and Endo, I., 1998. Multilayered reinforcement learning for complicated collision avoidance problems. In: Proceedings IEEE International Conference on Robotics and Automation, vol. 3, pp. 2186-2191, Leuven, Belgium.
  • García-Córdova, F., 2007. A cortical network for control of voluntary movements in a robot finger. In: Neurocomputing, vol. 71, 2007, pp. 374- 391.
  • Gonzalez, J. et al, 2012. AUV Based Multi-vehicle Collaboration: Salinity Studies in Mar Menor Coastal Lagoon. In IFAC Workshop on Navigation, Guidance and Control of Underwater Vehicles (NGCUV).
  • Guerrero-González, A., García-Córdova, F., Ruz-Vila, F., 2010. A Solar Powered Autonomous Mobile Vehicle for Monitoring and Surveillance Missions of Long Duration. In: International Review of Electrical Engineering, Part A, vol. 5, n. 4, pp. 1580-1587.
  • Guerrero-González, A., García-Córdova, F., Gilabert, J., 2011. A Biologically inspired neural network for navigation with obstacle avoidance in autonomous underwater and surface vehicles. In: OCEANS 2011 IEEE. Doi. 10.1109/Oceans-Spain.2011.6003432
  • Medina, J., González-Harbour, M., and Drake, J., 2001. MAST real-time view: A graphic UML tool for modeling object-oriented real-time systems. Proc. of the 22nd IEEE Real-Time Systems Symposium (RTSS), December, pp. 245–256. IEEE.
  • Pérez-Ruzafa, A., Marcos, C., Gilabert, J., 2005. The Ecology of the Mar Menor coastal lagoon: a fast-changing ecosystem under human pressure. In: Coastal lagoons. Ecosystem processes and modelling for sustainable use and development. CRC press. pp.: 392-422.
  • Ridao, P., Yuh, J., Sugihara, K., Batlle, J., 2000. On AUV control architecture. In: Proc. Int. Conf. Robots and Systems.
  • Ritter, H., Martinez, T., Schulten, K., 1989. Topology-conserving maps for learning visuo-motor coordination, Neural Networks, vol. 2, 1989, pp. 159-168.
  • Schlegel, C., 2006. Communication patterns as key towards component-based robotics. In: International Journal on Advanced Robotics Systems 3 (1), 49–54.
  • Schlegel, C., Steck, C., Lotz, A., 2011. Model-driven software development in robotics: Communication patterns as key for a robotics component model, En: Introduction to Modern Robotics. iConcept Press (ed.on-line)
  • Stutters, L., Liu, H., Tiltman, C., Brown, D., 2008. Navigation technologies for autonomous underwater vehicles. In: IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews, Vol. 38, 581- 589.
  • OMG, 2009. UML profile for MARTE: Modeling and analysis of real-time embedded systems, formal/2009-11-02.
  • RoSta: Robot Standards and Reference Architectures, Coordination Action (CA) funded under the European Union’s Sixth Framework Programme (FP6), http://www.robot-standards.org/. Last accessed 05/2013.
  • Singhoff, F., Plantec, A., Dissaux, P., Legrand, J., 2009. Investigating the usability of real-time scheduling theory with the cheddar project. Journal of Real Time Systems, 43, 259–295.