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Chaotic metaheuristic algorithms for learning and reproduction of robot motion trajectories

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Abstract

Most of today’s mobile robots operate in controlled environments prone to various unpredictable conditions. Programming or reprogramming of such systems is time-consuming and requires significant efforts by number of experts. One of the solutions to this problem is to enable the robot to learn from human teacher through demonstrations or observations. This paper presents novel approach that integrates Learning from Demonstrations methodology and chaotic bioinspired optimization algorithms for reproduction of desired motion trajectories. Demonstrations of the different trajectories to reproduce are gathered by human teacher while teleoperating the mobile robot in working environment. The learning (optimization) goal is to produce such sequence of mobile robot actuator commands that generate minimal error in the final robot pose. Four different chaotic methods are implemented, namely chaotic Bat Algorithm, chaotic Firefly Algorithm, chaotic Accelerated Particle Swarm Optimization and newly developed chaotic Grey Wolf Optimizer (CGWO). In order to determine the best map for CGWO, this algorithm is tested on ten benchmark problems using ten well-known chaotic maps. Simulations compare aforementioned algorithms in reproduction of two complex motion trajectories with different length and shape. Moreover, these tests include variation of population in swarm and demonstration examples. Real-world experiment on a nonholonomic mobile robot in indoor environment proves the applicability of the proposed approach.

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References

  1. Zhou C, Cao Z, Hou Z-G, Wang S, Tan M (2013) Backward swimming gaits for a carangiform robotic fish. Neural Comput Appl 23:2015–2021

    Article  Google Scholar 

  2. Franceschini N, Ruffier F, Serres J (2007) A bio-inspired flying robot sheds light on insect piloting abilities. Curr Biol 17:329–335

    Article  Google Scholar 

  3. Virágh C, Vásárhelyi G, Tarcai N, Szörényi T, Somorjai G, Nepusz T, Vicsek T (2014) Flocking algorithm for autonomous flying robots. Bioinspir Biomim 9:025012

    Article  Google Scholar 

  4. Miljković Z, Vuković N, Mitić M (2015) Neural extended Kalman filter for monocular SLAM in indoor environment. Proc IMechE Part C: J Mech Eng Sci 230:856–866

    Article  Google Scholar 

  5. Ayyıldız M, Çetinkaya K (2015) Comparison of four different heuristic optimization algorithms for the inverse kinematics solution of a real 4-DOF serial robot manipulator. Neural Comput Appl. doi:10.1007/s00521-015-1898-8

    Google Scholar 

  6. Miljković Z, Mitić M, Lazarević M, Babić B (2013) Neural network reinforcement learning for visual control of robot manipulators. Expert Syst Appl 40:1721–1736

    Article  Google Scholar 

  7. Miljković Z, Vuković N, Mitić M, Babić B (2013) New hybrid vision-based control approach for automated guided vehicles. Int J Adv Manuf Technol 66:231–249

    Article  Google Scholar 

  8. Argall BD, Browning B, Veloso M (2008) Learning robot motion control with demonstration and advice-operators. In: IEEE/RSJ international conference on intelligent robots and systems, pp 399–404

  9. Argall BD, Chernova S, Veloso M, Browning B (2009) A survey of robot learning from demonstration. Robot Auton Syst 57:469–483

    Article  Google Scholar 

  10. Mitić M, Miljković Z (2014) Neural network learning from demonstration and epipolar geometry for visual control of a nonholonomic mobile robot. Soft Comput 18:1011–1025

    Article  Google Scholar 

  11. Sweeney JD, Grupen R (2007) A model of shared grasp affordances from demonstration. In: IEEE international conference on humanoid robots, pp 27–35

  12. Vakanski A, Janabi-Sharifi F, Mantegh I, Irish A (2010) Trajectory learning based on conditional random field for robot programming by demonstration. In: IASTED international conference on robotics and applications, pp 401–408

  13. Vakanski A, Mantegh I, Irish A, Janabi-Sharifi F (2012) Trajectory learning for robot programming by demonstration using hidden Markov model and dynamic time warping. IEEE Trans Syst Man Cybern Part B Cybern 42:1039–1052

    Article  Google Scholar 

  14. Calinon S (2009) Robot programming by demonstration: a probabilistic approach. CRC Press, Boca Raton

    Google Scholar 

  15. Hartlan C, Bredeche N (2007) Using echo state networks for robot navigation behavior acquisition. In: IEEE international conference on robotics and biomimetics, pp 201–206

  16. Nehmzow U, Akanyeti O, Billings SA (2010) Towards modelling complex robot training tasks through system identification. Robot Auton Syst 58:265–275

    Article  Google Scholar 

  17. Nehmzow U, Akanyeti O, Weinrich C, Kyriacou T, Billings SA (2007) Robot programming by demonstration through system identification. In: IEEE/RSJ international conference on intelligent robots and systems, pp 801–806

  18. Lopes M, Santos-Victor J (2005) Visual learning by imitation with motor representations. IEEE Trans Syst Man Cybern Part B Cybern 35:438–449

    Article  Google Scholar 

  19. Narayanan KK, Posada LF, Hoffmann F, Bertram T (2013) Acquisition of behavioral dynamics for vision based mobile robot navigation from demonstrations. Mechatron Syst 1:37–44

    Google Scholar 

  20. Narayanan KK, Posada LF, Hoffmann F, Bertram T (2011) Situated learning of visual robot behaviors. In: Intelligent robotics and applications, pp 172–182

  21. Mitić M, Miljković Z (2015) Bio-inspired approach to learning robot motion trajectories and visual control commands. Expert Syst Appl 42:2624–2637

    Article  Google Scholar 

  22. Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B Cybern 26:29–41

    Article  Google Scholar 

  23. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  24. Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010), pp 65–74

  25. Yang XS (2009) Firefly algorithms for multimodal optimization. In: International symposium on stochastic algorithms, pp 169–178

  26. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE/RSJ international conference on neural networks, pp 1942–1948

  27. Terzi S, Serin S (2014) Planning maintenance works on pavements through ant colony optimization. Neural Comput Appl 25:143–153

    Article  Google Scholar 

  28. Madadi A, Motlagh MM (2014) Optimal control of dc motor using grey wolf optimizer algorithm. Techn J Eng Appl Sci 4:373–379

    Google Scholar 

  29. Karthikeyan S, Asokan P, Nickolas S (2014) A hybrid discrete firefly algorithm for multi-objective flexible job shop scheduling problem with limited resource constraints. Int J Adv Manuf Technol 72:1567–1579

    Article  Google Scholar 

  30. Komaki GM, Kayvanfar V (2015) Grey Wolf Optimizer algorithm for the two-stage assembly flow shop scheduling problem with release time. J Comput Sci 8:109–120

    Article  Google Scholar 

  31. Soltanpour MR, Khooban MH (2013) A particle swarm optimization approach for fuzzy sliding mode control for tracking the robot manipulator. Nonlinear Dyn 74:467–478

    Article  MathSciNet  MATH  Google Scholar 

  32. Gandomi AH, Yang XS (2014) Chaotic bat algorithm. J Comput Sci 5:224–232

    Article  MathSciNet  Google Scholar 

  33. Gandomi AH, Yang XS, Talatahari S, Alavi AH (2013) Firefly algorithm with chaos. Commun Nonlinear Sci 18:89–98

    Article  MathSciNet  MATH  Google Scholar 

  34. Gandomi AH, Yun GJ, Yang XS, Talatahari S (2013) Chaos-enhanced accelerated particle swarm optimization. Commun Nonlinear Sci 18:327–340

    Article  MathSciNet  MATH  Google Scholar 

  35. Fister I, Perc M, Kamal SM (2015) A review of chaos-based firefly algorithms: perspectives and research challenges. Appl Math Comput 252:155–165

    MathSciNet  MATH  Google Scholar 

  36. Coelho L, Mariani VC (2008) Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization. Expert Syst Appl 34:905–913

    Article  Google Scholar 

  37. Abbeel P, Coates A, Ng A (2010) Autonomous helicopter aerobatics through apprenticeship learning. Int J Robot Res 29:1608–1639

    Article  Google Scholar 

  38. Fister I, Fister I Jr, Yang XS, Brest J (2013) A comprehensive review of firefly algorithms. Swarm Evol Comput 13:34–46

    Article  Google Scholar 

  39. Yang XS (2010) Engineering optimization: an introduction with metaheuristic applications. Wiley, Hoboken

    Book  Google Scholar 

  40. Mirjalili S, Mirjalili SM, Lewis A (2014) Biogeography-based optimisation with chaos. Neural Comput Appl 25:1077–1097

    Article  Google Scholar 

  41. Gharooni-fard G, Moein-darbari F, Deldari H, Morvaridi A (2010) Scheduling of scientific workflows using a chaos-genetic algorithm. Procedia Comput Sci 1:1445–1454

    Article  Google Scholar 

  42. Alatas B (2010) Chaotic harmony search algorithms. Appl Math Comput 216:2687–2699

    MATH  Google Scholar 

  43. Gong W, Wang S (2009) Chaos ant colony optimization and application. In: Fourth international conference on internet computing for science and engineering, pp 301–303

  44. Alatas B (2010) Chaotic bee colony algorithms for global numerical optimization. Expert Syst Appl 37:5682–5687

    Article  Google Scholar 

  45. Mingjun J, Huanwen T (2004) Application of chaos in simulated annealing. Chaos Solitons Fract 21:933–941

    Article  MATH  Google Scholar 

  46. Saremi S, Mirjalili SM, Mirjalili S (2014) Chaotic krill herd optimization algorithm. Procedia Technol 12:180–185

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the Serbian Government—the Ministry of Education, Science and Technological Development—through the project TR35004 (2011–2015).

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Authors and Affiliations

  1. Production Engineering Department, Faculty of Mechanical Engineering, University of Belgrade, Kraljice Marije 16, 11120, Belgrade 35, Serbia

    Marko Mitić, Milica Petrović & Zoran Miljković

  2. Innovation Center, Faculty of Mechanical Engineering, University of Belgrade, Kraljice Marije 16, 11120, Belgrade 35, Serbia

    Najdan Vuković

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  1. Marko Mitić
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  3. Milica Petrović
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  4. Zoran Miljković
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Correspondence to Marko Mitić.

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Mitić, M., Vuković, N., Petrović, M. et al. Chaotic metaheuristic algorithms for learning and reproduction of robot motion trajectories. Neural Comput & Applic 30, 1065–1083 (2018). https://doi.org/10.1007/s00521-016-2717-6

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  • DOI: https://doi.org/10.1007/s00521-016-2717-6

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