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Background on Artificial Intelligence Algorithms for Global Path Planning

  • Anis Koubaa
  • Hachemi Bennaceur
  • Imen Chaari
  • Sahar Trigui
  • Adel Ammar
  • Mohamed-Foued Sriti
  • Maram Alajlan
  • Omar Cheikhrouhou
  • Yasir Javed
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 772)

Abstract

In the literature, numerous path planning algorithms have been proposed. Although the objective of these algorithms is to find the shortest path between two positions A and B in a particular environment, there are several algorithms based on a diversity of approaches to find a solution to this problem. The complexity of algorithms depends on the underlying techniques and on other external parameters, including the accuracy of the map and the number of obstacles. It is impossible to enumerate all these approaches in this chapter, but we will shed the light on the most used approaches in the literature.

References

  1. 1.
    Latombe, Jean claude. 1991. Robot motion planning. The Springer International Series in Engineering and Computer Science.Google Scholar
  2. 2.
    Šeda, Miloš. 2007. Roadmap methods versus cell decomposition in robot motion planning. In Proceedings of the 6th WSEAS international conference on signal processing, robotics and automation, 127–132. World Scientific and Engineering Academy and Society (WSEAS).Google Scholar
  3. 3.
    Yan, Zhi, Nicolas Jouandeau, and Arab Ali Cherif. 2013. Acs-prm: Adaptive cross sampling based probabilistic roadmap for multi-robot motion planning. In Intelligent autonomous systems 12, 843–851. Springer.Google Scholar
  4. 4.
    Nazif, Ali Nasri, Alireza Davoodi, and Philippe Pasquier. 2010. Multi-agent area coverage using a single query roadmap: A swarm intelligence approach. In Advances in practical multi-agent systems, 95–112. Springer.Google Scholar
  5. 5.
    Rosell, Jan, and Pedro Iniguez. 2005. Path planning using harmonic functions and probabilistic cell decomposition. In Proceedings of the 2005 IEEE international conference on robotics and automation. ICRA 2005, 1803–1808. IEEE.Google Scholar
  6. 6.
    Cosío, F., M.A.Padilla Arambula, and Castañeda. 2004. Autonomous robot navigation using adaptive potential fields. Mathematical and Computer Modelling 40 (9–10): 1141–1156.Google Scholar
  7. 7.
    Sfeir, Joe, Maarouf Saad, and Hamadou Saliah-Hassane. 2011. An improved artificial potential field approach to real-time mobile robot path planning in an unknown environment. In 2011 IEEE international symposium on robotic and sensors environments (ROSE), 208–213. IEEE.Google Scholar
  8. 8.
    Kim, Dong Hun. 2009. Escaping route method for a trap situation in local path planning. International Journal of Control, Automation and Systems 7 (3): 495–500.Google Scholar
  9. 9.
    Hart, Peter E., Nils J. Nilsson, and Bertram Raphael. 1968. A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics 4 (2): 100–107.Google Scholar
  10. 10.
    Choubey, Neha, and Mr. Bhupesh Kr. Gupta. 2013. Analysis of working of dijkstra and a* to obtain optimal path. International Journal of Computer Science and Management Research 2: 1898–1904.Google Scholar
  11. 11.
    Potamias, Michalis, Francesco Bonchi, Carlos Castillo, and Aristides Gionis. 2009. Fast shortest path distance estimation in large networks. In Proceedings of the 18th ACM conference on Information and knowledge management, 867–876. ACM.Google Scholar
  12. 12.
    Jigang, Wu, and Pingliang Han, George Rosario Jagadeesh, and Thambipillai Srikanthan. 2010. Practical algorithm for shortest path on large networks with time-dependent edge-length. In 2010 2nd international conference on computer engineering and technology (ICCET), vol. 2, 57–60. China: Chengdu.Google Scholar
  13. 13.
    Kanoulas, Evangelos, Yang Du, Tian Xia, and Donghui Zhang. 2006. Finding fastest paths on a road network with speed patterns. In Proceedings of the 22nd International Conference on Data Engineering, ICDE’06, 10. IEEE.Google Scholar
  14. 14.
    Dijkstra, Edsger W. 1959. A note on two problems in connexion with graphs. Numerische mathematik 1 (1): 269–271.MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Pearl Judea. 1984. Heuristics: Intelligent search strategies for computer problem solving.Google Scholar
  16. 16.
    Pohl, Ira. 1970. First results on the effect of error in heuristic search. Machine Intelligence 5: 219–236.MathSciNetzbMATHGoogle Scholar
  17. 17.
    Pohl, Ira. 1973. The avoidance of (relative) catastrophe, heuristic competence, genuine dynamic weighting and computational issues in heuristic problem solving. In Proceedings of the 3rd international joint conference on artificial intelligence, 12–17. Morgan Kaufmann Publishers Inc.Google Scholar
  18. 18.
    Köll, Andreas, and Hermann Kaindl. 1992. A new approach to dynamic weighting. In Proceedings of the tenth European conference on artificial intelligence (ECAI-92), 16–17, Vienna, Austria.Google Scholar
  19. 19.
    Harabor, Daniel Damir, Alban Grastien, et al. 2011. Online graph pruning for pathfinding on grid maps. In AAAI.Google Scholar
  20. 20.
    Cazenave, Tristan. 2006. Optimizations of data structures, heuristics and algorithms for path-finding on maps. In 2006 IEEE symposium on computational intelligence and games, 27–33. IEEE.Google Scholar
  21. 21.
    Korf, Richard E. 1985. Depth-first iterative-deepening: An optimal admissible tree search. Artificial Intelligence 27 (1): 97–109.MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Antsfeld, Leonid, Daniel Damir Harabor, Philip Kilby, and Toby Walsh. 2012. Transit routing on video game maps. In AIIDE, 2–7.Google Scholar
  23. 23.
    Botea, Adi, et al. Ultra-fast optimal path finding without runtime search. In AIIDE.Google Scholar
  24. 24.
    Likhachev, Maxim, Geoffrey J Gordon, and Sebastian Thrun. 2004. Ara*: Anytime a* with provable bounds on sub-optimality. In Advances in Neural Information Processing Systems, 767–774.Google Scholar
  25. 25.
    Likhachev, Maxim, David I Ferguson, Geoffrey J Gordon, Anthony Stentz, and Sebastian Thrun. 2005. Anytime dynamic a*: An anytime, replanning algorithm. In ICAPS, 262–271.Google Scholar
  26. 26.
    Koenig, Sven, and Maxim Likhachev. 2002. D* lite. In Proceedings of the eighteenth national conference on artificial intelligence (AAAI), 476–483.Google Scholar
  27. 27.
    Berg, Jur Van Den, Rajat Shah, Arthur Huang, and Ken Goldberg. 2011. Ana*: Anytime nonparametric a*. In Proceedings of twenty-fifth AAAI conference on artificial intelligence (AAAI-11).Google Scholar
  28. 28.
    Glover, Fred. 1986. Future paths for integer programming and links to artificial intelligence. Computers and Operations Research, 13 (5): 533 – 549. Applications of Integer Programming.Google Scholar
  29. 29.
    Osman, Ibrahim H., and Gilbert Laporte. 1996. Metaheuristics: A bibliography. Annals of Operations Research 63 (5): 511–623.CrossRefzbMATHGoogle Scholar
  30. 30.
    Voss, Stefan, Ibrahim H. Osman, and Catherine Roucairol (eds.). 1999. Meta-Heuristics: Advances and trends in local search paradigms for optimization. Norwell, MA, USA: Kluwer Academic Publishers.zbMATHGoogle Scholar
  31. 31.
    Mohanty, Prases K., and Dayal R. Parhi. 2013. Controlling the motion of an autonomous mobile robot using various techniques: A review. Journal of Advance of Mechanical Engineering 1 (1): 24–39.Google Scholar
  32. 32.
    Kirkpatrick, S., C.D. Gelatt, and M.P. Vecchi. 1983. Optimization by simulated annealing. Science 220 (4598): 671–680.MathSciNetCrossRefzbMATHGoogle Scholar
  33. 33.
    Glover, Fred. 1989. Tabu search-part i. ORSA Journal on Computing 1 (3): 90–206.CrossRefGoogle Scholar
  34. 34.
    Glover, Fred. 1990. Tabu search-part ii. ORSA Journal on Computing 2 (1): 4–32.CrossRefzbMATHGoogle Scholar
  35. 35.
    Masehian, Ellips, and MR Amin-Naseri. 2006. A tabu search-based approach for online motion planning. In IEEE International Conference on Industrial Technology. ICIT 2006, 2756–2761. IEEE.Google Scholar
  36. 36.
    Masehian, Ellips, and Mohammad Reza Amin-Naseri. 2008. Sensor-based robot motion planning-a tabu search approach. IEEE Robotics and Automation Magazine 15 (2):Google Scholar
  37. 37.
    Hussein, Ahmed, Heba Mostafa, Mohamed Badrel-din, Osama Sultan, and Alaa Khamis. 2012. Metaheuristic optimization approach to mobile robot path planning. In 2012 International Conference on Engineering and Technology (ICET), 1–6. IEEE.Google Scholar
  38. 38.
    Khaksar, Weria, Tang Sai Hong, Mansoor Khaksar, and Omid Reza Esmaeili Motlagh. 2012. Sampling-based tabu search approach for online path planning. Advanced Robotics 26 (8–9): 1013–1034.Google Scholar
  39. 39.
    Wei, Hongxing, Bin Wang, Yi Wang, Zili Shao, and Keith C.C. Chan. 2012. Staying-alive path planning with energy optimization for mobile robots. Expert Systems with Applications 39 (3): 3559–3571.CrossRefGoogle Scholar
  40. 40.
    Wang, Tianmiao, Bin Wang, Hongxing Wei, Yunan Cao, Meng Wang, and Zili Shao. 2008. Staying-alive and energy-efficient path planning for mobile robots. In American control conference, 868–873.Google Scholar
  41. 41.
    Tang, Kit-Sang, Kim-Fung Man, Sam Kwong, and Qun He. 1996. Genetic algorithms and their applications. IEEE Signal Processing Magazine 13 (6): 22–37.CrossRefGoogle Scholar
  42. 42.
    Yongnian, Zhou, Zheng Lifang, and Li Yongping. 2012. An improved genetic algorithm for mobile robotic path planning. In 2012 24th Chinese control and decision conference (CCDC), 3255–3260. IEEE.Google Scholar
  43. 43.
    Jianguo, Wang, Ding Biao, Miao Guijuan, Bao Jianwu, and Yang Xuedong. 2012. Path planning of mobile robot based on improving genetic algorithm. In Proceedings of the 2011 international conference on informatics, cybernetics, and computer engineering (ICCE2011) November 1920, 2011, Melbourne, Australia, vol. 112, ed. Liangzhong Jiang, 535–542. Advances in Intelligent and Soft Computing. Berlin: Springer.Google Scholar
  44. 44.
    Zhao, Jie, Lei Zhu, Gangfeng Liu, Gang Liu, and Zhenfeng Han. 2009. A modified genetic algorithm for global path planning of searching robot in mine disasters. In ICMA 2009 international conference on mechatronics and automation, 4936–4940.Google Scholar
  45. 45.
    Nearchou, Andreas C. 1998. Path planning of a mobile robot using genetic heuristics. Robotica 16: 575–588.CrossRefGoogle Scholar
  46. 46.
    Lee, J., B.-Y. Kang, and D.-W. Kim. 2013. Fast genetic algorithm for robot path planning. Electronics Letters 49 (23): 1449–1451.CrossRefGoogle Scholar
  47. 47.
    Sedighi, Kamran H., Theodore W. Manikas, Kaveh Ashenayi, and Roger L. Wainwright. 2009. A genetic algorithm for autonomous navigation using variable-monotone paths. International Journal of Robotics and Automation 24 (4): 367.Google Scholar
  48. 48.
    Karami, Amir Hossein, and Maryam Hasanzadeh. 2015. An adaptive genetic algorithm for robot motion planning in 2d complex environments. Computers and Electrical Engineering 43: 317–329.CrossRefGoogle Scholar
  49. 49.
    Liu, Shuhua, Yantao Tian, and Jinfang Liu. 2004. Multi mobile robot path planning based on genetic algorithm. In WCICA 2004 fifth world congress on intelligent control and automation, vol. 5, 4706–4709.Google Scholar
  50. 50.
    Rastogi, Shivanshu, and Vikas Kumar. 2011. An approach based on genetic algorithms to solve the path planning problem of mobile robot in static environment. MIT International Journal of computer science and information technology 1: 32–35.Google Scholar
  51. 51.
    Tamilselvi, D., S. Mercy Shalinie, A. Fathima Thasneem, and S. Gomathi Sundari. 2012. Optimal path selection for mobile robot navigation using genetic algorithm in an indoor environment. In Advanced Computing, Networking and Security, vol. 7135, ed. P. Santhi Thilagam, Pais AlwynRoshan, K. Chandrasekaran, and N. Balakrishnan, 263–269. Lecture Notes In Computer Science. Berlin: Springer.Google Scholar
  52. 52.
    Oleiwi, Bashra K., Hubert Roth, and Bahaa I. Kazem. 2014. Modified genetic algorithm based on a* algorithm of multi objective optimization for path planning. Jounal of Automation and Control Engineering 2 (4): 357–362.CrossRefGoogle Scholar
  53. 53.
    Oleiwi, Bashra Kadhim, Hubert Roth, and Bahaa I. Kazem. 2014. Multi objective optimization of path and trajectory planning for non-holonomic mobile robot using enhanced genetic algorithm. In Neural networks and artificial intelligence, vol. 440, ed. Vladimir Golovko, and Akira Imada, 50–62. Communications in Computer and Information Science: Springer International Publishing.CrossRefGoogle Scholar
  54. 54.
    Oleiwi, Bashra Kadhim, Rami Al-Jarrah, Hubert Roth, and Bahaa I. Kazem. 2014. Multi objective optimization of trajectory planning of non-holonomic mobile robot in dynamic environment using enhanced ga by fuzzy motion control and a*. In Neural Networks and Artificial Intelligence, eds. Vladimir Golovko and Akira Imada, vol. 440, 34–49. Communications in Computer and Information Science. Springer International Publishing.Google Scholar
  55. 55.
    Cabreira, T.M., G.P. Dimuro, and M.S. de Aguiar. 2012. An evolutionary learning approach for robot path planning with fuzzy obstacle detection and avoidance in a multi-agent environment. In 2012 third Brazilian workshop on social simulation (BWSS), 60–67.Google Scholar
  56. 56.
    Cabreira, T.M., M.S. de Aguiar, and G.P. Dimuro. 2013. An extended evolutionary learning approach for multiple robot path planning in a multi-agent environment. In 2013 IEEE congress on evolutionary computation (CEC), 3363–3370.Google Scholar
  57. 57.
    Xiao-Ting, Ji, Xie Hai-Bin, Zhou Li, and Jia Sheng-De. 2013. Flight path planning based on an improved genetic algorithm. In 2013 third international conference on intelligent system design and engineering applications (ISDEA), 775–778.Google Scholar
  58. 58.
    Rosenblatt, Frank. 1958. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review 65 (6): 386.CrossRefGoogle Scholar
  59. 59.
    Haykin, Simon. 1998. Neural networks: A comprehensive foundation, 2nd ed. Upper Saddle River, NJ, USA: Prentice Hall PTR.zbMATHGoogle Scholar
  60. 60.
    Kohonen, Teuvo (ed.). 2001. Self-organizing maps. Berlin: Springer.zbMATHGoogle Scholar
  61. 61.
    Thrun, Sebastian B. 1993. Exploration and model building in mobile robot domains. In IEEE international conference on neural networks, 175–180. IEEE.Google Scholar
  62. 62.
    Kim, Heon-Hui, Yun-Su Ha, and Gang-Gyoo Jin. 2003. A study on the environmental map building for a mobile robot using infrared range-finder sensors. In Proceedings of the 2003 IEEE/RSJ international conference on intelligent robots and systems, IROS 2003, vol. 1, 711–716. IEEE.Google Scholar
  63. 63.
    Zou, Anmin, Zengguang Hou, Lejie Zhang, and Min Tan. 2005. A neural network-based camera calibration method for mobile robot localization problems. In International symposium on neural networks, 277–284. Springer.Google Scholar
  64. 64.
    Silva, Catarina, Manuel Crisostomo, and Bernardete Ribeiro. 2000. Monoda: a neural modular architecture for obstacle avoidance without knowledge of the environment. In Proceedings of the IEEE-INNS-ENNS international joint conference on neural networks, IJCNN 2000, vol. 6, 334–339. IEEE.Google Scholar
  65. 65.
    Hu, Huosheng, and Dongbing Gu. 1999. Landmark-based navigation of mobile robots in manufacturing. In Proceedings of the 7th IEEE international conference on emerging technologies and factory automation, ETFA’99, vol. 1, 121–128. IEEE.Google Scholar
  66. 66.
    Ishii, Kazuo, Syuhei Nishida, Keisuke Watanabe, and Tamaki Ura. 2002. A collision avoidance system based on self-organizing map and its application to an underwater vehicle. In 7th international conference on control, automation, robotics and vision, ICARCV 2002, vol. 2, 602–607. IEEE.Google Scholar
  67. 67.
    Zou, An-Min, Zeng-Guang Hou, Fu Si-Yao, and Min Tan. 2006. Neural networks for mobile robot navigation: a survey. In Advances in Neural Networks-ISNN, 1218–1226.Google Scholar
  68. 68.
    Glasius, Roy, C.A.M. Andrzej Komoda, and Stan, and Gielen. 1995. Neural network dynamics for path planning and obstacle avoidance. Neural Networks 8 (1): 125–133.Google Scholar
  69. 69.
    Hopfield, John J. 1987. Neural networks and physical systems with emergent collective computational abilities. In Spin glass theory and beyond: An introduction to the replica method and its applications, 411–415. World Scientific.Google Scholar
  70. 70.
    Yang, Simon X. and Max Meng. 2001. Neural network approaches to dynamic collision-free trajectory generation. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 31 (3): 302–318.Google Scholar
  71. 71.
    Hodgkin, A.L., and A.F. Huxley. 1952. A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology 117 (4): 500–544.CrossRefGoogle Scholar
  72. 72.
    Chan, H.T., K.S. Tam, and N.K. Leung. 1993. A neural network approach for solving the path planning problem. In 1993 IEEE international symposium on circuits and systems, ISCAS’93, 2454–2457. IEEE.Google Scholar
  73. 73.
    Singh, Mukesh Kumar, and Dayal R. Parhi. 2009. Intelligent neuro-controller for navigation of mobile robot. In Proceedings of the international conference on advances in computing, communication and control, 123–128. ACM.Google Scholar
  74. 74.
    Pradhan, Saroj Kumar, Dayal Ramakrushna Parhi, and Anup Kumar Panda. 2009. Fuzzy logic techniques for navigation of several mobile robots. Applied Soft Computing 9 (1): 290–304.CrossRefGoogle Scholar
  75. 75.
    Chen, Yi-Wen, and Wei-Yu Chiu. 2015. Optimal robot path planning system by using a neural network-based approach. In 2015 international automatic control conference (CACS), 85–90. IEEE.Google Scholar
  76. 76.
    Sadati, Nasser and Javid Taheri. 2002. Solving robot motion planning problem using hopfield neural network in a fuzzified environment. In Proceedings of the 2002 IEEE international conference on fuzzy systems, FUZZ-IEEE’02, vol. 2, 1144–1149. IEEE.Google Scholar
  77. 77.
    Simon, X., and Yang and Max Meng. 2000. An efficient neural network approach to dynamic robot motion planning. Neural Networks 13: 143–148.Google Scholar
  78. 78.
    Cao, Yan, Xiaolan Zhou, Shuai Li, Feng Zhang, Xinwei Wu, Aomei Li, and Lei Sun. 2010. Design of path planning based cellular neural network. In 2010 8th world congress on intelligent control and automation (WCICA), 6539–6544. IEEE.Google Scholar
  79. 79.
    Hong, Qu, Simon X. Yang, Allan R. Willms, and Zhang Yi. 2009. Real-time robot path planning based on a modified pulse-coupled neural network model. IEEE Transactions on Neural Networks 20 (11): 1724–1739.CrossRefGoogle Scholar
  80. 80.
    Dorigo, Marco, Mauro Birattari, and Thomas Sttzle. 2006. Ant colony optimization-artificial ants as a computational intelligence technique. IEEE Computational Intelligence Magazine 1: 28–39.CrossRefGoogle Scholar
  81. 81.
    Dorigo, Marco, and Luca Maria Gambardella. 1997. Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on evolutionary computation 1 (1): 53–66.CrossRefGoogle Scholar
  82. 82.
    Dorigo, Thomas Sttzle, and Marco. 2004. Ant colony optimization. Cambridge, Massachusetts London, England: The MIT Press.Google Scholar
  83. 83.
    Fan, Xiaoping, Xiong Luo, Sheng Yi, Shengyue Yang, and Heng Zhang. 2003. Optimal path planning for mobile robots based on intensified ant colony optimization algorithm. In Proceedings of the 2003 IEEE international conference on robotics, intelligent systems and signal processing, vol. 1, 131–136. IEEE.Google Scholar
  84. 84.
    Lee, Joon-Woo, Young-Im Choy, Masanori Sugisakaz, and Ju-Jang Lee. 2010. Study of novel heterogeneous ant colony optimization algorithm for global path planning. In 2010 IEEE international symposium on industrial electronics (ISIE), 1961–1966. IEEE.Google Scholar
  85. 85.
    Porta Garcia, M.A., Oscar Montiel, Oscar Castillo, Roberto Sepúlveda, and Patricia Melin. 2009. Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation. Applied Soft Computing 9 (3): 1102–1110.CrossRefGoogle Scholar
  86. 86.
    Dong-Shu, Wang, and Yu Hua-Fang. 2011. Path planning of mobile robot in dynamic environments. In 2011 2nd international conference on intelligent control and information processing (ICICIP), vol. 2, 691–696. IEEE.Google Scholar
  87. 87.
    Zhang, Xiaoyong, Min Wu, Jun Peng, and Fu Jiang. 2009. A rescue robot path planning based on ant colony optimization algorithm. In International conference on information technology and computer science, ITCS 2009, vol. 2, 180–183. IEEE.Google Scholar
  88. 88.
    He, Yufeng, Qinghua Zeng, Jianye Liu, Guili Xu, and Xiaoyi Deng. 2013. Path planning for indoor uav based on ant colony optimization. In 2013 25th Chinese control and decision conference (CCDC), 2919–2923. IEEE.Google Scholar
  89. 89.
    Ganganath, Nuwan, and Chi-Tsun Cheng. 2013. A 2-dimensional aco-based path planner for off-line robot path planning. In 2013 international conference on cyber-enabled distributed computing and knowledge discovery (CyberC), 302–307. IEEE.Google Scholar
  90. 90.
    Yee, Zi Cong, and S.G. Ponnambalam. 2009. Mobile robot path planning using ant colony optimization. In IEEE/ASME international conference on advanced intelligent mechatronics, AIM 2009, 851–856. IEEE.Google Scholar
  91. 91.
    Ma, Yong-jie, and Wen-jing Hou. 2010. Path planning method based on hierarchical hybrid algorithm. In 2010 international conference on computer, mechatronics, control and electronic engineering (CMCE), vol. 1, 74–77. IEEE.Google Scholar
  92. 92.
    Qing, L.I., Wei Zhang, Yi-xin Yin, and Zhi-liang Wang. 2006. An improved genetic algorithm for optimal path planning. Journal of Information and Control, 444–447.Google Scholar
  93. 93.
    Xu, Jing-Rong, Yun Li, Hai-Tao Liu, and Pan Liu. 2008. Hybrid genetic ant colony algorithm for traveling salesman problem. Journal of Computer Applications, 2084–2112.Google Scholar
  94. 94.
    Gao, Meijuan, Jin Xu, and Jingwen Tian. 2008. Mobile robot global path planning based on improved augment ant colony algorithm. In Second international conference on genetic and evolutionary computing, WGEC’08, 273–276. IEEE.Google Scholar
  95. 95.
    Geetha, S., G. Muthu Chitra, and V. Jayalakshmi. 2011. Multi objective mobile robot path planning based on hybrid algorithm. In 2011 3rd international conference on electronics computer technology (ICECT), vol. 6, 251–255. IEEE.Google Scholar
  96. 96.
    Zhou, Wang, Zhang Yi, and Yang Ruimin. 2008. Mobile robot path planning based on genetic algorithm. Microcomputer Information 24 (26): 187–189.Google Scholar
  97. 97.
    Garro, Beatriz A., Humberto Sossa, and Roberto A. Vazquez. 2007. Evolving ant colony system for optimizing path planning in mobile robots. In Electronics, robotics and automotive mechanics conference, CERMA 2007, 444–449. IEEE.Google Scholar
  98. 98.
    Miao, Yun-Qian, Alaa Khamis, Fakhreddine Karray, and Mohamed Kamel. 2011. A novel approach to path planning for autonomous mobile robots. International Journal on Control and Intelligent Systems 39 (4): 1–27.MathSciNetzbMATHGoogle Scholar
  99. 99.
    Randria, Iadaloharivola, Mohamed Moncef Ben Khelifa, Moez Bouchouicha, and Patrick Abellard. 2007. A comparative study of six basic approaches for path planning towards an autonomous navigation. In 33rd annual conference of the IEEE industrial electronics society, IECON 2007, 2730–2735. IEEE.Google Scholar
  100. 100.
    Tisue, Seth, and Uri Wilensky. 2004. Netlogo: A simple environment for modeling complexity. In International conference on complex systems, vol. 21, 16–21. Boston, MA.Google Scholar
  101. 101.
    Sariff, Nohaidda Binti, and Norlida Buniyamin. 2009. Comparative study of genetic algorithm and ant colony optimization algorithm performances for robot path planning in global static environments of different complexities. In 2009 IEEE international symposium on computational intelligence in robotics and automation (CIRA), 132–137. IEEE.Google Scholar
  102. 102.
    Tewolde, Girma S., and Weihua Sheng. 2008. Robot path integration in manufacturing processes: Genetic algorithm versus ant colony optimization. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 38 (2): 278–287.CrossRefGoogle Scholar
  103. 103.
    Koceski, Saso, Stojanche Panov, and Natasa Koceska. 2014. Pierluigi Beomonte Zobel, and Francesco Durante. A novel quad harmony search algorithm for grid-based path finding. International Journal of Advanced Robotic Systems 11 (9): 144.Google Scholar
  104. 104.
    Gomez, Edwar Jacinto, Fernando Martinez Santa, and Fredy Hernan Martinez Sarmiento. 2013. A comparative study of geometric path planning methods for a mobile robot: potential field and voronoi diagrams. In 2013 II international congress of engineering mechatronics and automation (CIIMA), 1–6. IEEE.Google Scholar
  105. 105.
    Čikeš, Mijo, Marija akulović, and Ivan Petrović. 2011. The path planning algorithms for a mobile robot based on the occupancy grid map of the environment a comparative study. In 2011 XXIII international symposium on information, communication and automation technologies (ICAT), 1–8. IEEE.Google Scholar
  106. 106.
    Haro, Felipe, and Miguel Torres. 2006. A comparison of path planning algorithms for omni-directional robots in dynamic environments. In IEEE 3rd Latin American robotics symposium, LARS’06, 18–25. IEEE.Google Scholar
  107. 107.
    Eraghi, Nafiseh Osati, Femando Lopez-Colino, Angel De Castro, and Javier Garrido. Path length comparison in grid maps of planning algorithms: Hctnav, a and dijkstra. In 2014 Conference on design of circuits and integrated circuits (DCIS), 1–6. IEEE.Google Scholar
  108. 108.
    Pala, Marco, Nafiseh Osati Eraghi, Fernando López-Colino, Alberto Sanchez, Angel de Castro, and Javier Garrido. 2013. Hctnav: A path planning algorithm for low-cost autonomous robot navigation in indoor environments. ISPRS International Journal of Geo-Information 2 (3): 729–748.CrossRefGoogle Scholar
  109. 109.
    Duchoň, František, Peter Hubinskỳ, Andrej Babinec, Tomáš Fico, and Dominik Huňady. 2014. Real-time path planning for the robot in known environment. In 2014 23rd International Conference on robotics in Alpe-Adria-Danube region (RAAD), 1–8. IEEE.Google Scholar
  110. 110.
    Chiang, Chia Hsun, Po Jui Chiang, Jerry Chien-Chih Fei, and Jin Sin Liu. 2007. A comparative study of implementing fast marching method and a* search for mobile robot path planning in grid environment: Effect of map resolution. In IEEE workshop on advanced robotics and its social impacts, ARSO 2007, 1–6. IEEE.Google Scholar
  111. 111.
    Zaheer, Shyba, M. Jayaraju, and Tauseef Gulrez. 2015. Performance analysis of path planning techniques for autonomous mobile robots. In 2015 IEEE international conference on electrical, computer and communication technologies (ICECCT), 1–5. IEEE.Google Scholar
  112. 112.
    Al-Arif, S., A. Ferdous, and S. Nijami. 2012. Comparative study of different path plan-ning algorithms: A water based rescue system. International Journal of Computer Applications, 39.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Anis Koubaa
    • 1
  • Hachemi Bennaceur
    • 2
  • Imen Chaari
    • 3
  • Sahar Trigui
    • 3
  • Adel Ammar
    • 2
  • Mohamed-Foued Sriti
    • 2
  • Maram Alajlan
    • 2
  • Omar Cheikhrouhou
    • 4
  • Yasir Javed
    • 5
  1. 1.Prince Sultan UniversityRiyadhSaudi Arabia
  2. 2.College of Computer and Information SciencesAl Imam Mohammad Ibn Saud Islamic UniversityRiyadhSaudi Arabia
  3. 3.University Campus of ManoubaManoubaTunisia
  4. 4.College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia
  5. 5.College of Computer and Information SciencesPrince Sultan UniversityRiyadhSaudi Arabia

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