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Automatic navigation of mobile robots in unknown environments

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Abstract

Online navigation with known target and unknown obstacles is an interesting problem in mobile robotics. This article presents a technique based on utilization of neural networks and reinforcement learning to enable a mobile robot to learn constructed environments on its own. The robot learns to generate efficient navigation rules automatically without initial settings of rules by experts. This is regarded as the main contribution of this work compared to traditional fuzzy models based on notion of artificial potential fields. The ability for generalization of rules has also been examined. The initial results qualitatively confirmed the efficiency of the model. More experiments showed at least 32 % of improvement in path planning from the first till the third path planning trial in a sample environment. Analysis of the results, limitations, and recommendations is included for future work.

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References

  1. Motlagh O, Tang SH, Ismail N (2009) Development of a new minimum avoidance system for a behavior-based mobile robot. Fuzzy Sets Syst 160(13):1929–1946

    Article  Google Scholar 

  2. Motlagh O, Tang SH, Ismail N, Ramli AR (2012) An expert fuzzy cognitive map for reactive navigation of mobile robots. Fuzzy Sets Syst 201:105–121

    Article  MathSciNet  Google Scholar 

  3. Zhu A, Yang SX (2004) A fuzzy logic approach to reactive navigation of behavior-based mobile robots. In: Proceedings of the 2004 IEEE international conference on robotics and automation, 5:5045–5050

  4. Hui NB, Mahendar V, Pratihar DK (2006) Time-optimal, collision-free navigation of a car-like mobile robot using neuro-fuzzy approaches. Fuzzy Sets Syst 157(16):2171–2204

    Article  MathSciNet  MATH  Google Scholar 

  5. Selekwa MF, Dunlap DD, Shi D, Collins EG Jr (2007) Robot navigation in very cluttered environments by preference-based fuzzy behaviors. Rob Auton Syst 56(3):231–246

    Article  Google Scholar 

  6. Krishna KM, Kalra PK (2001) Perception and remembrance of the environment during real-time navigation of a mobile robot. Rob Auton Syst 37:25–51

    Article  MATH  Google Scholar 

  7. Ordonez C, Collins EG Jr, Selekwa MF, Dunlap DD (2007) The virtual wall approach to limit cycle avoidance for unmanned ground vehicles. Rob Auton Syst 56(8):645–657

    Article  Google Scholar 

  8. Xu WL, Tso SK (1999) Sensor-based fuzzy reactive navigation for a mobile robot through local target switching. IEEE Trans Syst Man Cybern C Appl Rev 29(3):451–459

    Article  Google Scholar 

  9. Huq R, Mann GKI, Gosine RG (2008) Mobile robot navigation using motor schema and fuzzy context dependent behavior modulation. Appl Soft Comput 8(1):422–436

    Article  Google Scholar 

  10. Tu K-Y, Baltes J (2006) Fuzzy potential energy for a map approach to robot navigation. Rob Auton Syst 54(7):574–589

    Article  Google Scholar 

  11. Khatib O (1986) Real-time obstacle avoidance for manipulators and mobile robots. Int J Rob Res 5(1):90–98

    Article  MathSciNet  Google Scholar 

  12. Acar EU, Choset H (2002) Sensor-based coverage of unknown environments: incremental construction of morse decompositions. Int J Rob Res 21:345–366

    Article  Google Scholar 

  13. Fekete SP, Schmidt C (2010) Polygon exploration with time-discrete vision. Comput Geom 43:148–168

    Article  MathSciNet  MATH  Google Scholar 

  14. MobotSim Version 1.0 (2001) Fully functional trial version of MobotSim. Retrieved from http://www.mobotsoft.com/?page_id=70

  15. Stamova IM, Ilarionov R, Krustev K (2011) Asymptotic behavior of equilibriums of a class of impulsive bidirectional associative memory neural networks with time-varying delays. Neural Comput Appl 20(7):1111–1116

    Article  Google Scholar 

  16. Leung ACS, Pui FS, Ho K (2011) The effect of weight fault on associative networks. Neural Comput Appl 20(1):113–121

    Article  Google Scholar 

  17. Motlagh O, Tang SH, Ramli AR (2010) An FCM modeling for using a priori knowledge: application study in modeling quadruped walking. Neural Comput Appl 21(5):1007–1015

    Google Scholar 

  18. Motlagh O, Tang SH, Ismail N, Ramli AR, Samin R (2009) A new path estimation strategy for predicting blind persons’ motion in indoor environments. J Asian Arch Build Eng 8(2):371–377

    Google Scholar 

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Authors and Affiliations

  1. Department of Robotics and Automation, Faculty of Manufacturing Engineering, University Teknikal Malaysia Melaka (UTeM), 76100, Melaka, Malaysia

    Omid Motlagh

  2. Department of Mechanical and Manufacturing, Faculty of Engineering, University Putra Malaysia (UPM), 43400, Serdang, Malaysia

    Danial Nakhaeinia, Sai Hong Tang, Babak Karasfi & Weria Khaksar

Authors
  1. Omid Motlagh
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  2. Danial Nakhaeinia
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  3. Sai Hong Tang
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  4. Babak Karasfi
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  5. Weria Khaksar
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Correspondence to Omid Motlagh.

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Motlagh, O., Nakhaeinia, D., Tang, S.H. et al. Automatic navigation of mobile robots in unknown environments. Neural Comput & Applic 24, 1569–1581 (2014). https://doi.org/10.1007/s00521-013-1393-z

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  • DOI: https://doi.org/10.1007/s00521-013-1393-z

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