International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 5, No. 9, 8 Aug. 2013
Cover page and Table of Contents: PDF (size: 967KB)
Formulation of FISPLAN: A Fuzzy Logic based Reactive Planner for AUVs towards Situation Aware Control
Full Text (PDF, 967KB), PP.47-57
Views: 0 Downloads: 0
Author(s)
Subhra Kanti Das
1,*
Dibyendu Pal
1
Index Terms
Planning, Reactive Architecture, Fuzzy, Situation Awareness, Escape
Abstract
The paper presents a detailed discussion on the structural organisation of a Fuzzy Inference System Planner (FISPLAN) for Autonomous Underwater Vehicles (AUVs), including elaboration of membership functions for the inputs as well as outputs. The inference mechanism is detailed with discussions on the rule base, which in essence incorporates the planning logic. In order to assess the effectiveness of the planner as a means of reactive escape under critical situations, a case study is studied with reference to a state of the art AUV. An approximate subsea current model is developed from field observations, and residual energy is estimated by referring to a typical Lithium-polymer cell discharge characteristic together with data recorded in actual field trials. Situations are simulated by considering different combinations of sea-currents as well as status of resident energy. Results reveal that the simulated system, by virtue of the planner, is capable of perceiving situations, thereby realizing their imminence and making a decisive action thereupon. In concise, the fuzzy planner may be considered to provide human-like perception of situations on the basis of crisp observations. Furthermore dynamics of the system are modelled with actual parameters, and subsequently controller responses for pitching and velocity correction are illustrated. Choice of planning interval is also expressed as a function of the controllers' response.
Cite This Paper
Subhra Kanti Das, Dibyendu Pal, "Formulation of FISPLAN: A Fuzzy Logic based Reactive Planner for AUVs towards Situation Aware Control", International Journal of Intelligent Systems and Applications(IJISA), vol.5, no.9, pp.47-57, 2013. DOI:10.5815/ijisa.2013.09.06
Reference
[1]J. YUH, Design and Control of Autonomous Underwater Robots: A Survey, Autonomous Robots 8, 7–24 (2000).
[2]Bellingham, J. G., Consi, T.R. and Beaton, R. M. Keeping layered control simple, Proceedings of the IEEE Symposium on Autonomous Underwater vehicle Technology, Washington D.C, USA, published as IEEE Catalog Nº 90 CH2856-3, pp. 3-8, 1990.
[3]Zheng, X. Layered Control of a Practical AUV. Proceedings of the IEEE Symposium on Autonomous Underwater Vehicles Technology, Washington D.C., USA, pp. 142-147, 1992.
[4]Payton D.W., Keirsey D., Kimble D., Krozel J. and Rosenblatt K. Do Whatever Works: A Robust Approach to Fault-Tolerant Autonomous Control., Journal of Applied Intelligence, Vol. 2, pp.225-250, 1992.
[5]Boswell A.J. and Leaney J.R., Using the subsumption architecture in a autonomous underwater robot: expostulations extensions and experiences, International Advanced Robotics program, Workshop on Mobile Robots for Subsea Environments, Monterey California, USA, 1994.
[6]Duane T. Davis, Thesis on Precision Control and Maneuvering of the Phoenix Autonomous Underwater Vehicle for entering a recovery tube, NAVAL POSTGRADUATE SCHOOL Monterey, California, 1996.
[7]Fujii T., and Ura T, Development of an Autonomous Underwater Robot ‘Twin-Burger’ for Testing Intelligent Behaviors in Realistic Environments, Autonomous Robots, Vol. 3, No. 3, pp. 285-296, 1996.
[8]Rosenblatt J., Williams S., Durrant-Whyte H., Behavior-Based Control for Autonomous Underwater Exploration, IEEE International Conference on Robotics and Automation, San Francisco, USA.
[9]P. Ridao, J. Yuh', J. Batlle, K. Sugiharat, On AUV Control Architecture, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2000.
[10]Conor McGann, Frederic Py, Kanna Rajan, John Ryan, Richard Henthorn, Adaptive Control for Autonomous Underwater Vehicles, http://www.aaaipress.org/Papers/AAAI/2008/AAAI08-209.pdf
[11]C. Lin, S. Ren, X. Feng, Y. Li and J. Xu, Autonomic Element Based Architecture for Unmanned Underwater Vehicles, Proceedings of OCEANS 2010 IEEE - Sydney, Page(s): 1 – 5.
[12]T. B. Koay, Y. T. Tan, Y. H. Eng., R. Gao, M. Chitre, J. L. Chew, N. Chandhavarkar, R. R. Khan, T. Taher, J. Koh, STARFISH-A small team of Autonomous Robotic Fish, Indian Jr. of Geo-Marine Sciences, Vol. 40(2), April 2011, pp. 157-167.
[13]Andres el-Fakdi, PhD Thesis on Gradient-Based Reinforcement Learning Techniques for Underwater Robotics Behavior Learning, University of Girona, 2010.
[14]ZEYN A SAIGOL, PhD Thesis on Automated Planning for Hydrothermal Vent Prospecting using AUVs, University of Birmingham, 2011.
[15]Emilio Miguela´n˜ ez, Pedro Patro´n, Keith E. Brown, Yvan R. Petillot, and David M. Lane, Semantic Knowledge-Based Framework to Improve the Situation Awareness of Autonomous Underwater Vehicles, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 23, NO. 5, MAY 2011.
[16]Subrata Das, High-Level Data Fusion, ARTECH HOUSE, INC. 2008, ISBN-13: 978-1-59693-281-4, pp: 6-7.
[17]L.A. Zadeh, Fuzzy sets, Information and Control 8 (1965) 338–353.
[18]E.H. Mamdani, S. Assilian, Application of fuzzy algorithms for control of simple dynamic plant, Proceedings of the Institute of Electrical Engineers 121 (1974) 1585–1588.