Fuzzy Logic-Based Control for Autonomous Vehicle: A Survey

Fuzzy Logic-Based Control for Autonomous Vehicle: A Survey

Автор: Ishaya Emmanuel

Журнал: International Journal of Education and Management Engineering(IJEME) @ijeme

Статья в выпуске: 2 vol.7, 2017 года.

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Fuzzy set, since its advent has played an important role in control systems and many other area of applications. One of such area is the control of autonomous vehicle. There seem to be some difficulty however, for a new timer trying to get a clear picture of the autonomous navigation problem. To this end, this survey presents a panoramic view of the Intelligent Transportation Systems with some few example of the Advance Driver Assistance Systems and a good discussion on the autonomous systems with its eminent problems. More attention was focused on the fuzzy controllers designed for collision avoidance; as its performance has largely simplified and smoothens the collision avoidance process of an autonomous vehicular system.

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Fuzzy Logic, Autonomous Vehicle, Robot, Obstacle Detection, Collision avoidance

Короткий адрес: https://sciup.org/15014056

IDR: 15014056

Список литературы Fuzzy Logic-Based Control for Autonomous Vehicle: A Survey

  • Z. Hong, Y. Chao, and Z. Lei: A MRI guided robot for neurosurgery: Design and control. Proceedings of the IEEE International Conference on Information and Automation, 2008, 1, pp 759-764.
  • S. J. Song, Y. Moon, D. H. Lee, C.B. Ahn, Y. Jo and J. Choi: Comparative Study of Fuzzy PID Control Algorithms for Enhanced Position Control in Laparoscopic Surgery Robot, Journal of Medical and Biological Engineering February 2015, Volume 35, Issue 1, pp 34-44.
  • Qinjun, D.: Fuzzy PID control orienting medical robot in minimally invasive surgery. Proceedings of the IEEE International Conference on Automation and Logistics, 2007, 1, pp 2633-2638
  • N. Wakami: Fuzzy control and neural networks: Applications for consumer products, Fuzzy Logic and Fuzzy Control Volume 833 of the series Lecture Notes in Computer Science, August 2005, pp 97-105.
  • J. Borenstein and Y. Koren: Obstacle Avoidance with Ultrasonic Sensors. International Journal of Robotics and Automation, Vol 4, No.2 April 1988. pp 213-218.
  • C. Stiller, J. Hipp, C. Rossing and Ewald: Multisensor Obstacle Detection and Tracking. Image and Vision Computing 18 (2000). pp 389-396.
  • N. Kehtarnavaz, N. C. Griswold and J. S. Lee: Visual Control of an Autonomous Vehicle (BART) –The Vehicle-Following Problem, IEEE Transaction on Vehicular Technology, Vol. 40, No. 3, August 1991. Pp 654-622.
  • D. C. Carl, D. G. Armstrong, A. Maryum, S. Solanki, D. MacArthur, E. Zawodny, S. Gray, T Petroff, M. Grifis and C. Evans: Development of an Integrated Sensor System for Obstacle Detection and Terrain Evaluation to Unmanned Ground Vehicle. In Proc. of SPIE Vol. 5804, pp. 156-165.
  • A. M. Adil and U. F. Aziz: Sonar Based Obstacle Detection and Avoidance Algorithm, in IEEE International Conference on Signal Acquisition and Processing, 2009, pp 98-102.
  • S. S. Rode, S. Vijay, P. Goyal, P. Kulkami and K. Arya: Pothole Detection and Warning System: Infrastructure Support and System Design, in IEEE international conference on electronic computer technology, 2009, pp 286-290.
  • A. Vahidi and A. Eskandarian: Research advances in Intelligent Collision Avoidance and Adaptive Cruise Control. in IEEE Transactions on Intelligent Transportation Systems, Vol. 4, No. 3, September 2003. Pp 143-153.
  • A., Claudi, D., Accattoli, P., Sernani, P., Calvaresi and A. F., Dragoni: A Noise Robust Obstacle Detection Algorithm for Mobile Robots Using Active 3D Sensors, 56th International Symposium ELMAR-2014, Zadar, Croatia, September 2014, pp 91-94.
  • A. Touran, M. Brackstone, and M. McDonald, A collision model for safety evaluation of autonomous intelligent cruise control, Accident Analysis and Prevention, vol. 31, May 1999, pp. 567-578.
  • F. Bounini, D. Gingras, V. Lapointe and H. Pollart: Autonomous Vehicle and Real Time Road Lanes Detection and Tracking, in IEEE Proc. VVPC, 978-1-4673-7637-2/15.
  • G. A. Kiran and S. Muralis: Automatic Hump Detection and 3D View Generation from a Single Road Image. In Proc. IEEE international Conference on Advances in Computing, Communications and Informatics (ICACCI), 2014. pp. 2232-2283.
  • X. Yu and E. Salaris: Pavement Pothole Detection and Severity Measurement Using Laser Imagin text, (2011).
  • A. Mednis, G, Strazdins, R. Zviedris, G. Kanonirs and L. Selavo: Real Time Pothole Detection using Android Smartphones with Accelerometer, in IEEE of DCOSS, 2011.
  • A. D. Jarnea, R. Dobrescu, D. Popescu and L. Ichim: Advance Driver Assistance System for Overtaking Maneuver on a Highway, in IEEE 15th International Conference on System Theory Control and Computing, Romania, October 2015. pp 759-764.
  • R. Manduchi,A, Castano, A.Talukderand L. Matthies: Obstacle Detection and Terrain Classification for Autonomous Off-Road Navigation. Autonomous Robot 18, of Springer Science, Netherland, 2005. pp. 81-105.
  • Y. Cheng-peng and Y. Xian: Terrain Classification for Autonomous Navigation using Lasar Sensing. in IEEE of the 1st International Conference on Information Science and Engineering (ICISE) 2009, pp. 14571470.
  • M. Bajracharya, B. Tang, A. Howard, M. Turmon, and L. Matthies: Learning Long-Range Terrain Classification for Autonomous Navigation, in IEEE International Conference on Robotics and Automation, Pasadena, CA, USA, May 2008, pp 40184024.
  • M. S. Darms, P. E. Rybski, C. Baker, and C. Urmson: Obstacle Detection and Tracking for the Urban Challenge, in IEEE Transactions on Intelligent Transportation Systems, VOL. 10, NO. 3, September 2009, pp 475-484.
  • A. Talukder, R. Manduchi': A. Rankin and L. Matthies: Fast and Reliable Obstacle Detection and Segmentation for Cross-country Navigation, in IEEE of IVS, 2002, pp 610618.
  • Artificial Intelligence: Intelligent Systems, www.tutorialspoint.com.
  • Z. Zhang, R. Wiess and A. R. Hanson: Obstacle Detection Based on Qualitative and Quantitative 3D Reconstruction, in IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 1, 1997. pp 15-26.
  • D. Nair and J. K. Aggarwal: Moving Obstacle Detection from a Navigating Robot, IEEE Transactions on Robotics and Automation, VOL. 14, NO. 3, June 1998, pp 404-416.
  • K. Storjohann, Th. Zieke, H. A. Mallotand W. von Seelon: Visual Obstacle Detection for Automatically Guided Vehicle, IEEE1990, pp 761-766.
  • H. Zhang, Y. Jia, N. Xi and A. Song: Obstacle Avoidance for Mobile Manipulation by Real-Time Sensor-Based Redundancy Resolution, in Proc. of IEEE International Conference on Robotics and Biometrics December 2012, Guangzhou, China, pp 2369-74.
  • R. Sosa and G. Velazquez: Obstacle Detection and Collision Avoidance System Developed with Virtual Model, in Proc. in IEEE ICVES 2007, pp 1-8.
  • W. L. Xu, S. K. Tso, and Y. H. Fung: Sonar Based Reactive Navigation of a Mobile Robot through Local Target Switching, in IEEEICA, Montercy, 1997, pp 361-366.
  • J. Borenstein and Y. Koren: Real Time Obstacle Avoidance for Fast Mobile Robot in Cluttered Environment, IEEE 1990, pp. 572577.
  • R. Lagisetty, N. K. Philip, R. Padhi and M. S. Bhat: Object Detection and Obstacle Avoidance for Mobile Robot Using Stereo Camera, in IEEE international conference on control Applications (CCA), Hyderabad, India, August 2013, pp. 605-610.
  • M. Cao and E. Hall: Fuzzy Logic Control for an Automated Guided Vehicle, Center for Robotics Research University of Cincinnati, Cincinnati, OH 45221.
  • H. R. Boem and H. S. Cho: A Sensor Based Obstacle Avoidance Controller for a Mobile Robot using Fuzzy Algorithm and Neural Network, IEEE 1992, pp. 1470-1475.
  • B. I. Hartman, Y. Kanayama and T. Smith: Model and Sensor Based Precide Navigation by an Autonomous Vehicle, Advance Robotics, Springer-Verlag Berlin Heidelberg, 1989, pp 98-109.
  • L. A. Zadeh: Fuzzy Sets. Information and Control, 1965, pp 338-353.
  • J. Alcala-Fdez and J. M. Alonso: A Survey of Fuzzy Systems Software: Taxonomy, Current Trends and Prospects, Article in IEEE Transactions of Fuzzy Systems, February 2016.
  • E. H. Mamdani and S. Assilian: An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller, International Journal of Man-Machine Studies July 1975, pp 1-13.
  • E. Cox: The Fuzzy Systems Handbook –A Practitioner's Guide to Building, Using, and Maintaining Fuzzy Systems, Academic Press, Incorporation 1994.
  • W.J.M. Kichert and E.H. Mamdani: Analysis of a fuzzy Control Systems, Fuzzy Sets and Systems, vol.1, no. 1, pg. 29-44, 1978
  • http://www.scielo.br/img/revistas/ca/v14n4/a05fig01.gif.
  • S. Thongchai and K. Kawamura: Application of Fuzzy Control to a Sonar-Based Obstacle Avoidance Mobile Robot, in Proc. of IEEE International Conference on Control Applications, Anchorage, Alaska, USA September 2000, pp 425-430.
  • L. P. Holmblad and J. J. Ostegard: Control of a Cement Fuzzy Kiln by Logic, in Fuzzy Information and Decision Processes, (M.M. Gupta and E. Sanchez, eds), North-Holland, Amsterdam, 1884.
  • Y. Luo, Z. Wang, G. Wei, B. Shen, X. He, H. Dong and J. Hu: Fuzzy-Logic-Based Control, Filtering, and Fault Detection for Networked Systems: A Survey. Mathematical Problems in Engineering, Hindawi Publishing Corporation, Vol 2015.
  • R. E. Precup and H. Hellendoorn: A Survey on Industrial Application of Fuzzy Control, Computers in Industries, 2011, Vol. 62 no. 3, pp 213-216.
  • N. Walia, H. Singh andA.Sharma: ANFIS: Adaptive Neuro-fuzzy Inference System –A Survey, International Journal of Computer Applications, Vol. 123 no. 13 August 2015. pp 32-38.
  • M. Norouzi, A. Karambaskhsh, M. Namazifar and B. Savkovic: Object Obstacle Based Navigation of Mobile Robot with Obstacle Avoidance using Fuzzy Controller, IEEE International Conference on Control and Automation, Christchurch, New Zealand, December 2009, pp.169-174.
  • N. E. Hodge and M. B. Trabia: Steering Fuzzy Logic Controller for an Autonomous Vehicle, Proc. of IEEE International Conference on Robotics and Automation, Detroit, Michigan, May 1999, pp 2482-88.
  • D. Gardeazeaball, V. Ponomaryov and I. Chairez: Fuzzy Control for Obstacle Avoiding in Mobile Robots Using Stereo Vision Algorithms, in ICEEE, 2011. pp xxx.
  • B. Baasandorj, R. Reyaz, P. J. Ho, C. W. Cheol, D. J. Lee and K. T. Chong: AMobile Robot Obstacle Avoidance Using Fuzzy Logic and Model Predictive Control. Applied Mechanics and Materials, Vols. 548-549 (2015) pp. 922-927.
  • W. Maslak and B. H. Butkiewicz: Autonomous Control with Fuzzy Logic, Proc. of IEEE SPS, 2013, pp xxx
  • T. Zoubir and T. Abdelouaheb: A Fast Edge Detection Using Fuzzy Rules, Faculty of Engineering Science, Computer Science Department, Badji Mokhtar University Annaba, Algeria.
  • T. M. Cabreira, G. P. Dimuro, and M. S. de Aguiar: An Evolutionary Learning Approach for Path Planning with Fuzzy Obstacle Detection and Avoidance in a Multi-Agent Environment, in IEEE third Brazilian Workshop on Social Simulation, Brazil, 2012. pp. 60-67.
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