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Soft Computing in Robotics: A Decade Perspective

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Applications of Robotics in Industry Using Advanced Mechanisms (ARIAM 2019)

Abstract

As soft computing deals with development of approximate models in finding solutions to real world problems, it is considered as one of the emerging area of research in all fields of engineering and sciences. Because of rapid development in mechanization, vast research has also been carried out by the researchers in the field of robotics for the development of robots in various applications such as industry, medical, rehabilitation, agriculture, military etc. to assist human being. In this paper, a comprehensive analytical perspective of soft computing techniques and their application in robotics has been illustrated. Further, the analysis is a witness of the fact that problems emerging in the robotics can be solved aptly using soft computing techniques. Also, this paper sheds light on various issues and challenges of the discussed research area to demonstrate the dominance of soft computing techniques in the development of various applications in robotics.

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References

  1. Zadeh LA (1994) Fuzzy logic and soft computing: issues, contentions and perspectives. In: Proceedings of IIZUKA 1994: 3rd international conference on fuzzy logic, neural nets and soft computing, pp 1–2

    Google Scholar 

  2. Zadeh LA (1992) Proceedings of the second international conference on fuzzy logic and neural networks, Iizuka, Japan, pp Xiii–xiv

    Google Scholar 

  3. Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing, a computational approach to learning and machine intelligence. Prentice-Hall, Upper Saddle River

    Google Scholar 

  4. Onieva E et al (2009) Soft computing techniques for autonomous driving. Mathware Soft Comput 16(1):45–58

    MathSciNet  Google Scholar 

  5. Singh P (2016) Applications of soft computing in time series forcasting. Springer International Publishing, Cham, pp 1–2

    Google Scholar 

  6. Khan A, Ansari Z (2015) Soft computing based medical image mining: a survey. Int J Comput Trends Technol (IJCTT) 27(2), 76–79

    Google Scholar 

  7. Arreguin J (2008) Automation and robotics. I-Tech and Publishing, Vienna

    Google Scholar 

  8. Zunt D. Who did actually invent the word “robot” and what does it mean?. The Karel Capek website. Archived from the original on 23 January 2013. https://ebooks.adelaide.edu.au/c/capek/karel/rur/. Accessed 05 Feb 2017

  9. Margolius I. The robot of Prague’, newsletter, the friends of Czech Heritage no. 17, Autumn 2017, p. 3. https://czechfriends.net/images/RobotsMargoliusJul2017.pdf. Archived 11 Sept 2017 at the Wayback Machine

  10. Karel Capek – Who did actually invent the word “robot” and what does it mean? at capek.misto.cz [dead link]

  11. Kurfess TR (2005) Robotics and automation handbook. Taylor & Francis. ISBN 9780849318047. Archived from the original on 4 December 2016. Accessed 5 July 2016 – via Google Books

    Google Scholar 

  12. Nocks L (2007) The robot: the life story of a technology. Greenwood Publishing Group, Westport

    Google Scholar 

  13. Carne N (2019) Researchers make a million tiny robots. Cosmos Mag

    Google Scholar 

  14. Zadeh LA (1994) Fuzzy logic, neural networks, and soft computing. Commun ACM 37(3):77–84

    Google Scholar 

  15. Bain A (1873) Mind and body: the theories of their relation. D. Appleton and Company, New York

    Google Scholar 

  16. James W (1890) The principles of psychology. H. Holt and Company, New York

    Google Scholar 

  17. Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA 79(8):2554–2558. https://doi.org/10.1073/pnas.79.8.2554 PMC 346238

    Article  MathSciNet  MATH  Google Scholar 

  18. Bhardwaj A, Yogendra N, Vanraj P, Dutta M (2015) Sentiment analysis for indian stock market prediction using sensex and nifty. Procedia Comput Sci 70:85–91

    Google Scholar 

  19. Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution. Wiley, New York

    MATH  Google Scholar 

  20. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  21. Rechenberg I (1973) Evolutionstrategie: Optimierung Technischer Systeme nach Prinzipien des Biologischen Evolution. Fromman-Hozlboog Verlag, Stuttgart (in German)

    Google Scholar 

  22. Schwefel H-P (1981, 1995) Numerical optimization of computer models, 2nd edn. Wiley, New York

    Google Scholar 

  23. Beni G, Wang J (1993) Swarm intelligence in cellular robotic systems. In: Proceedings of NATO advanced workshop on robots and biological systems, Tuscany, Italy, 26–30 June 1989, pp 703–712. https://doi.org/10.1007/978-3-642-58069-7_38. ISBN 978-3-642-63461-1

    Google Scholar 

  24. Dorigo M, Gambardella LM (1997) Learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):214

    Google Scholar 

  25. Golbon-Haghighi MH, Saeidi-manesh H, Zhang G, Zhang Y (2018) Pattern synthesis for the cylindrical polarimetric phased array radar (CPPAR). Prog Electromagn Res M 66:87–98

    Google Scholar 

  26. Ishiguro A et al (1992) A neural network compensator for uncertainties of robotics manipulators. IEEE Trans Industr Electron 39(6):565–570

    MathSciNet  Google Scholar 

  27. Williams HAM et al (2019) Robotic kiwifruit harvesting using machine vision, convolutional neural networks, and robotic arms. Biosys Eng 181:140–156

    Google Scholar 

  28. Erol BA et al (2018) Improved deep neural network object tracking system for applications in home robotics. In: Pedrycz W, Chen SM (eds) Computational intelligence for pattern recognition. Springer, Cham, pp 369–395

    Google Scholar 

  29. Fang W et al (2019) A recurrent emotional CMAC neural network controller for vision-based mobile robots. Neurocomputing 334:227–238

    Google Scholar 

  30. Caltagirone L et al (2019) LIDAR–camera fusion for road detection using fully convolutional neural networks. Robot Auton Syst 111:125–131

    Google Scholar 

  31. Spielberg NA et al (2019) Neural network vehicle models for high-performance automated driving. Sci Robot 4(28):eaaw1975

    Google Scholar 

  32. Chame HF, Dos Santos MM, da Costa Botelho SS (2018) Neural network for black-box fusion of underwater robot localization under unmodeled noise. Robot Auton Syst 110:57–72

    Google Scholar 

  33. McCool C, Perez T, Upcroft B (2017) Mixtures of lightweight deep convolutional neural networks: applied to agricultural robotics. IEEE Robot Autom Lett 2(3):1344–1351

    Google Scholar 

  34. Kumra S, Kanan C (2017) Robotic grasp detection using deep convolutional neural networks. In: 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE

    Google Scholar 

  35. Sa I et al (2016) DeepFruits: a fruit detection system using deep neural networks. Sensors 16(8):1222

    Google Scholar 

  36. Janglová D (2004) Neural networks in mobile robot motion. Int J Adv Rob Syst 1(1):2

    Google Scholar 

  37. Walter JA, Schulten KI (1993) Implementation of self-organizing neural networks for visuo-motor control of an industrial robot. IEEE Trans Neural Networks 4(1):86–96

    Google Scholar 

  38. Miller WT (1989) Real-time application of neural networks for sensor-based control of robots with vision. IEEE Trans Syst Man Cybern 19(4):825–831

    Google Scholar 

  39. Lee S, Adams TM, Ryoo B-y (1997) A fuzzy navigation system for mobile construction robots. Autom Construction 6(2):97–107

    Google Scholar 

  40. Ali A et al (2018) Fuzzy PID controller for upper limb rehabilitation robotic system. In: 2018 IEEE international conference on innovative research and development (ICIRD). IEEE

    Google Scholar 

  41. Kumar N, Takács M, Vámossy Z (2017) Robot navigation in unknown environment using fuzzy logic. In: 2017 IEEE 15th international symposium on applied machine intelligence and informatics (SAMI). IEEE

    Google Scholar 

  42. Palm R, Chadalavada R, Lilienthal AJ (2019) Fuzzy modeling, control and prediction in human-robot systems. In: Merelo J et al (eds) Computational intelligence. Springer, Cham, pp 149–177

    Google Scholar 

  43. Deepak BBVL, Parhi DR (2019) New strategy for mobile robot navigation using fuzzy logic. In: Satapathy S, Bhateja V, Somanah R, Yang XS, Senkerik R (eds) Information systems design and intelligent applications. Springer, Singapore, pp 1–8

    Google Scholar 

  44. Castillo O, Aguilar LT (2018) Type-2 fuzzy logic in control of nonsmooth systems: theoretical concepts and applications, vol 373. Springer, Cham

    Google Scholar 

  45. Jamwal PK et al (2018) Tele-rehabilitation using in-house wearable ankle rehabilitation robot. Assistive Technol 30(1):24–33

    Google Scholar 

  46. Omrane H, Masmoudi MS, Masmoudi M (2016) Fuzzy logic based control for autonomous mobile robot navigation. Comput Intell Neurosci 2016:10. https://doi.org/10.1155/2016/9548482. Article ID 9548482

    Google Scholar 

  47. Lochan K, Roy BK (2015) Control of two-link 2-DOF robot manipulator using fuzzy logic techniques: a review. In: Proceedings of fourth international conference on soft computing for problem solving. Springer, New Delhi

    Google Scholar 

  48. Mendes N et al (2010) Fuzzy-PI force control for industrial robotics. In: Vadakkepat P et al (eds) FIRA RoboWorld congress. Springer, Heidelberg

    Google Scholar 

  49. Mailah M, Rahim NIA (2000) Intelligent active force control of a robot arm using fuzzy logic. In: 2000 TENCON proceedings. Intelligent systems and technologies for the new millennium (Cat. No. 00CH37119), vol 2. IEEE

    Google Scholar 

  50. Safiotti A (1997) Fuzzy logic in autonomous robotics: behavior coordination. In: Proceedings of 6th international fuzzy systems conference, vol 1. IEEE

    Google Scholar 

  51. Reignier P (1994) Fuzzy logic techniques for mobile robot obstacle avoidance. Robot Auton Syst 12(3-4):143–153

    Google Scholar 

  52. Zhang GQ, Li X, Boca R, Newkirk J, Zhang B, Fuhlbrigge TA (2014) Use of industrial robots in additive manufacturing– a survey and feasibility study. In: 41st international symposium on robotics, Munich, Germany, pp 1–6

    Google Scholar 

  53. Bogue R (2011) Robots in the nuclear industry: a review of technologies and applications. Int J Ind Robot 38:113–118

    Google Scholar 

  54. West C et al (2016) A genetic algorithm approach for parameter optimization of a 7DOF robotic manipulator. IFAC-PapersOnLine 49(12):1261–1266

    Google Scholar 

  55. Siegwart R, Nourbakhsh IR, Scaramuzza D (2011) Introduction to autonomous mobile robots, 2nd edn. MIT Press, Cambridge

    Google Scholar 

  56. Karami AH, Hasanzadeh M (2015) An adaptive genetic algorithm for robot motion planning in 2D complex environments. Comput Electr Eng 43:317–329

    Google Scholar 

  57. Datta R, Pradhan S, Bhattacharya B (2015) Analysis and design optimization of a robotic gripper using multiobjective genetic algorithm. IEEE Trans Syst Man Cybern Syst 46(1):16–26

    Google Scholar 

  58. Sharma P et al (2019) Black-hole gbest differential evolution algorithm for solving robot path planning problem. In: Yadav N, Yadav A, Bansal J, Deep K, Kim J (eds) Harmony search and nature inspired optimization algorithms. Springer, Singapore, pp 1009–1022

    Google Scholar 

  59. Kim Y-J, Park C-K, Kim KG (2019) Gain determination of feedback force for an ultrasound scanning robot using genetic algorithm. Int J Comput Assist Radiol Surg 14(5):797–807

    Google Scholar 

  60. Mane SB, Vhanale S (2019) Genetic algorithm approach for obstacle avoidance and path optimization of mobile robot. In: Iyer B, Nalbalwar S, Pathak N (eds) Computing, communication and signal processing. Springer, Singapore, pp 649–659

    Google Scholar 

  61. Orozco-Rosas U, Montiel O, Sepúlveda R (2019) Mobile robot path planning using membrane evolutionary artificial potential field. Appl Soft Comput 77:236–251

    Google Scholar 

  62. Cruz RSN, Zannatha JMI (2017) Efficient mechanical design and limit cycle stability for a humanoid robot: an application of genetic algorithms. Neurocomputing 233:72–80

    Google Scholar 

  63. Alnasser S, Bennaceur H (2016) An efficient genetic algorithm for the global robot path planning problem. In: 2016 sixth international conference on digital information and communication technology and its applications (DICTAP). IEEE

    Google Scholar 

  64. Panda RK, Choudhury BB (2015) An effective path planning of mobile robot using genetic algorithm. In: 2015 IEEE international conference on computational intelligence & communication technology. IEEE

    Google Scholar 

  65. Zacharia PT et al (2015) Planning the construction process of a robotic arm using a genetic algorithm. Int J Adv Manuf Technol 79(5-8):1293–1302

    Google Scholar 

  66. Baizid K et al (2015) Time scheduling and optimization of industrial robotized tasks based on genetic algorithms. Robot Comput-Integr Manuf 34:140–150

    Google Scholar 

  67. Sedighi KH et al (2004) Autonomous local path planning for a mobile robot using a genetic algorithm. In: Proceedings of the 2004 congress on evolutionary computation (IEEE Cat. No. 04TH8753), vol 2. IEEE

    Google Scholar 

  68. Wang H, Zhou Z (2019) A heuristic elastic particle swarm optimization algorithm for robot path planning. Information 10(3):99

    Google Scholar 

  69. Ezzat D et al (2019) A new nano-robots control strategy for killing cancer cells using quorum sensing technique and directed particle swarm optimization algorithm. In: International conference on advanced machine learning technologies and applications. Springer, Cham

    Google Scholar 

  70. Sahu C, Parhi DR, Kumar PB (2018) An approach to optimize the path of humanoids using adaptive ant colony optimization. J Bionic Eng 15(4):623–635

    Google Scholar 

  71. Dereli S, Köker R (2019) A meta-heuristic proposal for inverse kinematics solution of 7-DOF serial robotic manipulator: quantum behaved particle swarm algorithm. Artif Intell Rev 1–16

    Google Scholar 

  72. Havangi R (2019) Mobile robot localization based on PSO estimator. Asian J Control

    Google Scholar 

  73. Thabit S, Mohades A (2019) Multi-robot path planning based on multi-objective particle swarm optimization. IEEE Access 7:2138–2147

    Google Scholar 

  74. Wang L et al (2019) 3D path planning for the ground robot with improved ant colony optimization. Sensors 19(4):815

    Google Scholar 

  75. Chung S-J et al (2018) A survey on aerial swarm robotics. IEEE Trans Rob 34(4):837–855

    Google Scholar 

  76. Uriol R, Moran A (2017) Mobile robot path planning in complex environments using ant colony optimization algorithm. In: 2017 3rd international conference on control, automation and robotics (ICCAR). IEEE

    Google Scholar 

  77. Di Mario E, Navarro I, Martinoli A (2016) Distributed learning of cooperative robotic behaviors using particle swarm optimization. In: Hsieh M, Khatib O, Kumar V (eds) Experimental robotics. Springer, Cham

    Google Scholar 

  78. Lin C-J et al (2016) Integrated particle swarm optimization algorithm based obstacle avoidance control design for home service robot. Comput Electr Eng 56:748–762

    Google Scholar 

  79. Aghababa MP (2016) Optimal design of fractional-order PID controller for five bar linkage robot using a new particle swarm optimization algorithm. Soft Comput 20(10):4055–4067

    Google Scholar 

  80. Wang M, Luo J, Walter U (2015) Trajectory planning of free-floating space robot using Particle Swarm Optimization (PSO). Acta Astronaut 112:77–88

    Google Scholar 

  81. Yen C-T, Cheng M-F (2018) A study of fuzzy control with ant colony algorithm used in mobile robot for shortest path planning and obstacle avoidance. Microsyst Technol 24(1):125–135

    Google Scholar 

  82. Sathyan A, Ma O (2018) Collaborative control of multiple robots using genetic fuzzy systems approach. In: ASME 2018 dynamic systems and control conference. American Society of Mechanical Engineers

    Google Scholar 

  83. Venayagamoorthy GK, Grant LL, Doctor S (2009) Collective robotic search using hybrid techniques: fuzzy logic and swarm intelligence inspired by nature. Eng Appl Artif Intell 22(3):431–441

    Google Scholar 

  84. Flórez CAC, Rosário JM, Amaya D (2018) Control structure for a car-like robot using artificial neural networks and genetic algorithms. Neural Comput Appl 1–14. https://doi.org/10.1007/s00521-018-3514-1

  85. Juang C-F, Lin C-H, Bui TB (2018) Multiobjective rule-based cooperative continuous ant colony optimized fuzzy systems with a robot control application. IEEE Trans Cybern 1–14. https://doi.org/10.1109/TCYB.2018.2870981

  86. Likaj R, Bajrami X, Shala A, Pajaziti A (2017) Path finding for a mobile robot using fuzzy and genetic algorithms. Int J Mech Eng Technol (IJMET) 8(8):659–669

    Google Scholar 

  87. Wang X et al (2016) Double global optimum genetic algorithm–particle swarm optimization-based welding robot path planning. Eng Optim 48(2):299–316

    MathSciNet  Google Scholar 

  88. Bajrami X et al (2016) Genetic and fuzzy logic algorithms for robot path finding. In: 2016 5th Mediterranean conference on embedded computing (MECO). IEEE

    Google Scholar 

  89. Alves RMF, Lopes CR (2016) Obstacle avoidance for mobile robots: a hybrid intelligent system based on fuzzy logic and artificial neural network. In: 2016 IEEE international conference on fuzzy systems (FUZZ-IEEE). IEEE

    Google Scholar 

  90. Dutta S (2010) Obstacle avoidance of mobile robot using PSO-based neuro fuzzy technique. Int J Comput Sci Eng 2(2):301–304

    Google Scholar 

  91. Martinez-Soto R et al (2010) Fuzzy logic controllers optimization using genetic algorithms and particle swarm optimization. In: Mexican international conference on artificial intelligence. Springer, Heidelberg

    Google Scholar 

  92. Narvydas G, Simutis R, Raudonis V (2007) Autonomous mobile robot control using fuzzy logic and genetic algorithm. In: 2007 4th IEEE workshop on intelligent data acquisition and advanced computing systems: technology and applications. IEEE

    Google Scholar 

  93. Chatterjee A et al (2005) A particle-swarm-optimized fuzzy-neural network for voice-controlled robot systems. IEEE Trans Industr Electron 52(6):1478–1489

    Google Scholar 

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Correspondence to Janmenjoy Nayak .

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Swapna Rekha, H., Nayak, J., Naik, B., Pelusi, D. (2020). Soft Computing in Robotics: A Decade Perspective. In: Nayak, J., Balas, V., Favorskaya, M., Choudhury, B., Rao, S., Naik, B. (eds) Applications of Robotics in Industry Using Advanced Mechanisms. ARIAM 2019. Learning and Analytics in Intelligent Systems, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-030-30271-9_6

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