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Mobile robot visual navigation based on fuzzy logic and optical flow approaches

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Abstract

This paper presents the design of mobile robot visual navigation system in indoor environment based on fuzzy logic controllers (FLC) and optical flow (OF) approach. The proposed control system contains two Takagi–Sugeno fuzzy logic controllers for obstacle avoidance and goal seeking based on video acquisition and image processing algorithm. The first steering controller uses OF values calculated by Horn–Schunck algorithm to detect and estimate the positions of the obstacles. To extract information about the environment, the image is divided into two parts. The second FLC is used to guide the robot to the direction of the final destination. The efficiency of the proposed approach is verified in simulation using Visual Reality Toolbox. Simulation results demonstrate that the visual based control system allows autonomous navigation without any collision with obstacles.

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References

  • Alenya G, Crowley JL (2009) Time to Contact for obstacle avoidance. In: Proceedings. European conference on mobile robotics, (ECMR 2009), KoREMA, pp 19–24

  • Arnspang J, Henriksen K, Stahr R (1995) Estimating time to contact with curves, avoiding calibration and aperture problem. In: 6th international conference on computer analysis of images and patterns, CAIP’95, London, UK, pp 856–861

  • Ayache N, Sander PT (1991) Artificial vision for mobile robots: stereo vision and multisensory perception. MIT Press, Cambridge

    Google Scholar 

  • Becerra HM, Sagüés C, Mezouar Y, Hayet J (2014) Visual navigation of wheeled mobile robots using direct feedback of a geometric constraint. Auton Robots 37(2):137–156

    Article  Google Scholar 

  • Benn W, Lauria S (2012) Robot navigation control based on monocular images: an image processing algorithm for obstacle avoidance decisions. Math Probl Eng. https://doi.org/10.1155/2012/240476

    Article  Google Scholar 

  • Boumehraz M, Habba Z, Hassani R (2018) Vision based tracking and interception of moving target by mobile robot using fuzzy control. J Appl Eng Sci Technol 4(2):159–165

    Google Scholar 

  • Chao H, Gu Y, Napolitano M (2014) A survey of optical flow techniques for robotics navigation applications. J Intell Robot Syst 73:361–372

    Article  Google Scholar 

  • Corso J (2014) Motion and optical flow. College of Engineering, University of Michigan, Ann Arbor

    Google Scholar 

  • Desouza GN, Kak AC (2002) Vision for mobile robot navigation. A survey. IEEE Trans Pattern Anal Mach Intell 24(2):237–267

    Article  Google Scholar 

  • Fantoni I, Sanahuja G (2014) Optic flow-based control and navigation of mini aerial vehicles. J Aerosp Lab 8:1–9

    Google Scholar 

  • Font FB, Ortiz A, Oliver G (2008) Visual navigation for mobile robots: a survey. J Intel Robot Syst 53:263–296

    Article  Google Scholar 

  • Freda L, Oriolo G (2007) Vision based interception of moving target with nonholonomic mobile robot. Robot Autom Syst 55(5):419–432

    Article  Google Scholar 

  • Gupta M, Uggirala B, Behera L (2008) Visual navigation of a mobile robot in a cluttered environment. In: 17th world congress of IFAC, Seoul, Korea

  • Guzel MS, Bicker R (2011) Vision based obstacle avoidance techniques. In: Recent advances in mobile robotics (InTech), pp 83–108

  • Hamissi A, Bazoula A (2008) Fuzzy visual path following by a mobile robot. In: 1st Mediterranean conference on intelligent systems and automation (CISA08), Annaba, Algeria

  • Horn BK, Schunck BG (1981) Determining optical flow. Artif Intell 17:185–203

    Article  Google Scholar 

  • Huang L (2009) Velocity planning for a mobile robot to track a moving target—a potential field approach. Robot Auton Syst 57(1):55–63

    Article  Google Scholar 

  • Kahlouch S, Achour K (2007) Optical Flow based robot obstacle avoidance. Int J Adv Robot Syst 4:13–16

    Article  Google Scholar 

  • Kim Y, Noh S (2012) Fuzzy visual navigation using behavior primitives for small humanoid robot. Robot Intell Technol Appl AISC 208:823–834

    Google Scholar 

  • Latombe J (1991) Robot motion planning. Kluwer Academic Publishers, Dordrecht

    Book  Google Scholar 

  • Lee D (1976) A theory of visual control of braking based on information about time-to-collision. Perception 5(4):437–459

    Article  Google Scholar 

  • Lucas BD, Kanade T (1981) An iterative image registration technique with an application to stereo vision. In: Proceedings: imaging understanding workshop, pp 121–130

  • Mai NA, Janschek K (2009) Bio-inspired optical flow interpretation with fuzzy logic for behavior-based robot control. In: 18th international workshop on robotics in Alpe-Adria-Danube Region, Brasov, Romania

  • Miguel A et al (2009) A Pan-Tilt camera fuzzy vision controller on an unmanned aerial vehicle. In: The 2009 IEEE/RSJ international conference on intelligent robots and systems, USA

  • Miguel A, Pascual C (2015) Vision based fuzzy control approaches for unmanned aerial vehicles. In: 9th conference of the European society for fuzzy logic and technology (EUSFLAT)

  • Passino KM, Yurkovich S (1998) Fuzzy control. Addison Wesley, Menlo Park

    MATH  Google Scholar 

  • Saitoh T, Tada N, Konishi R (2009) Indoor mobile robot navigation by central following based on monocular vision. IEEJ Trans Electron Inf Syst 129:1576–1584

    Google Scholar 

  • Serres JR, Ruffier F (2017) Optic flow-based collision-free strategies: From insects to robots. Arthropod Struct Dev 46:703–717

    Article  Google Scholar 

  • Serres J, Ruffier F, Viollet S, Franceschini N (2006) Toward optic flow regulation for wall-following and centring behaviours. Int J Adv Robot Syst 3(2):147–154

    Article  Google Scholar 

  • Shuzhi SG, Lewis FL (2006) Autonomous mobile robots, sensing, control, decision, making and applications. CRC, Boca Raton

    MATH  Google Scholar 

  • Tajti F et al (2016) Optical flow based odometry for mobile robots supported by multiple sensors and sensor fusion. Automatica 57(1):201–211

    Article  Google Scholar 

  • Tasalatsanis A, Valavanis K, Yalcin Y (2007) Vision based target and collision avoidance for mobile robots. J Intell Robot Syst 48(2):285–304

    Article  Google Scholar 

  • Wang CW, Meng QH (2015) Obstacle avoidance for quadrotor using improved method based on optical flow. In: IEEE international conference on information and automation, Lijiang, China, pp 1674–1679

  • Zhang X, Wang Y, Fang Y (2016) Vision-based moving target Interception with a mobile robot based on motion prediction and online planning. In: IEEE international conference on real-time computing and robotics (RCAR), Angkor Wat, Cambodia, pp 17–21

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Authors and Affiliations

  1. Laboratory of Applied Automatic and Industrial Diagnostics (LAADI), University of Djelfa, 17000, Djelfa, Algeria

    Mohamed Nadour & Lakhmissi Cherroun

  2. Modeling of Energy Systems Laboratory (LMSE), University of Biskra, 07000, Biskra, Algeria

    Mohamed Nadour & Mohamed Boumehraz

  3. Institut de Robòtica i Informàtica Industrial, Polytechnic University of Catalonia (UPC), Barcelona, Spain

    Vicenç Puig

Authors
  1. Mohamed Nadour
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  2. Mohamed Boumehraz
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  3. Lakhmissi Cherroun
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  4. Vicenç Puig
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Correspondence to Lakhmissi Cherroun.

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Nadour, M., Boumehraz, M., Cherroun, L. et al. Mobile robot visual navigation based on fuzzy logic and optical flow approaches. Int J Syst Assur Eng Manag 10, 1654–1667 (2019). https://doi.org/10.1007/s13198-019-00918-2

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  • DOI: https://doi.org/10.1007/s13198-019-00918-2

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