Quantitative Performance Review of Wheeled Mobile Robot Path Planning Algorithms

Year 2021, Volume: 34 Issue: 3, 765 - 784, 01.09.2021

Oluwaseun Martins , Adefemi Adeyemi Adekunle Samuel Babatope Adejuyıgbe Hezekiah Oluwole Adeyemi

https://doi.org/10.35378/gujs.792682

Cited By: 2

Abstract

Path planning evaluates and identifies an obstacle free path for a wheeled mobile robot (WMR) to traverse within its workspace. It emphasizes metric like, start and goal coordinate, static or dynamic workspace, static or dynamic obstacles, computational time and local minimum problem. Path planning play a significant role toward WMR effective traverse within it workspace like industrial, military, hospital, school and office. In this workspace, path planning is an optimal method to increase the productivity of WMR to achieve it specific task. Hence, in this paper, we present a review of path planning algorithms (classical algorithms, heuristics and intelligent algorithms, and machine learning algorithm) for mobile robot using statistical method. Regarding our objective, we use this statistical method to evaluate the success of these algorithms base on the following metrics: architecture (hybrid or standalone), algorithm sub-category (global or local or combine), workspace (static or dynamic), obstacle type (static or dynamic), number of obstacle (≤ 2, ≤ 5, > 5) and test workspace (virtual or real-world). Research materials are sourced from recognized databases where relevant research articles are obtained and analyzed. Result shows hybrid of machine learning approach with heuristic and intelligent algorithm has superior performance where they are applied compare to other hybrid. Also, in complex workspace Q-learning algorithm outperforms other algorithms. To conclude future research is discussed to provide reference for hybrid of Q-learning algorithm with Cuckoo Search, Shuffled Frog Leaping and Artificial Bee Colony algorithm to improve its performance in complex workspace. 

Keywords

WMR, Path planning, Robot, Workspace, Algorithms

References

• [1] Shijin, C., Udayakumar, K., “Speed Control of wheeled mobile robots using PID with Dynamic and Kinematic Modelling”, In International Conference on Innovations in Information Embedded and Communication Systems (ICIIECS), 1-7, (2017).
• [2] Meng, J., Liu, A., Yang, Y., Wu, Z., Xu, Q., “Two-wheeled robot platform based on PID control”, In 5th International Conference on Information Science and Control Engineering, 1011-1014, (2018).
• [3] Martins, O. O., Adekunle, A. A., Adejuyigbe, S. B., Arowolo, M. O., Adeyemi, H. O., “Prototype line following automatic guided vehicle (AGV) for unit load dispatch in an office workspace”, FUDMA Journal of Sciences (FJS), 3(3): 415 - 423, (2019).
• [4] Tzafestas, S., “Mobile robot control and navigation: a global overview”, Journal of Intelligent and Robotic Systems, 1-24, (2018).
• [5] Qing, G., Zhang, Z., Yue, X., “Path-planning of automated guided vehicle based on improved dijkstra algorithm”, In 29th Chinese Control and Decision Conference (CCDC), 7138-7143, (2017).
• [6] Nikranjbar, A., Haidari, M., Atai, A. A., “Adaptive sliding mode tracking control of mobile robot in dynamic workspace using artificial potential fields”, Journal of Computer and Robotics, 1-2, (2018).
• [7] Nguyen, H. T., Xuan, H. L., “Path planning and obstacle avoidance approaches for mobile robot”, International Journal of Computer Science Issues, 3-4, (2016).
• [8] Zhang, H.-M., Li, M.-l., “Rapid path planning algorithm for mobile robot in dynamic workspace”, Advances in Mechanical Engineering, 1-12, (2017).
• [9] Shamsinejad, P., Saraee, M., Sheikholeslam, F., “A new path planner for autonomous mobile robots based on genetic algorithm”, In 3rd International Conference on Computer Science and Information Technology, 115-116, (2010).
• [10] Engedy, I., Horvath, G., “Artificial neural network based local motion planning of a wheeled mobile robot”, In International Symposium on Computational Intelligence and Informatics (CINTI), 213-218, (2010).
• [11] Miao, H., Tian, Y., “Dynamic robot path planning using an enhanced simulated annealing approach”, Applied Mathematics and Computation, Elsevier, 420-437, (2013).
• [12] Patle, B. K., Dayal, P., Jagadeesh, A., Sahu, O., “Real time navigation approach for mobile robot”, Journal of Computers, 135-142, (2017).
• [13] Alakshendra, V., Chiddarwar, S., “Adaptive robust control of mecanum- wheeled mobile robot with uncertainties”, Nonlinear Dynamics, 2147-2169, (2016).
• [14] Pandey, A., Pandey, S., Parhi, D. R., “Mobile Robot Navigation and Obstacle Avoidance Techniques A Review”, International Robotics Automation Journal, 2(3): 1-12, (2017).
• [15] Hmeyda, F., Bouani, F., “Camera-based autonomous mobile robot path planning and trajectory tracking using PSO algorithm and PID Controller”, International Conference on Control, Automation and Diagnosis (ICCAD'17), 203-208, (2017).
• [16] Tatiya, P.T., Andi, S., Nur Mukhammad, Z. R., Ibnu, F., Imam, P., Iswanto, I., “Pursuit algorithm for robot trash can based on fuzzy-cell decomposition”, International Journal of Electrical and Computer Engineering, 6(6): 2863-2869, (2016).
• [17] Gonzalez, R., Kloetzer, M., Mahulea, C., “Comparative study of trajectories resulted from cell decomposition path planning approaches”, In 21st International Conference System Theory, Control and Computing (ICSTCC), 49-54, (2016).
• [18] Masehian, E., Naseri, A., “Mobile Robot Online Motion Planning Using Generalized Voronoi Graphs”, Journal of Industrial Engineering, 5: 1-15, (2010).
• [19] Masehian, E., Sedighizadeh, D., “A multi-objective PSO-based algorithm for robot path planning”, In International Conference on Industrial Technology (ICIT), 465-470, (2010).
• [20] Silva-Ortigoza, R., Márquez-Sánchez, C., Carrizosa-Corral, F., Hernández-Guzmán, V., García-Sánchez, J., Taud, H., Álvarez-Cedillo, J., “Obstacle avoidance task for a wheeled mobile robot - A Matlab-Simulink-based didactic application”, In MATLAB Applications for the Practical Engineer, 80-100, (2014).
• [21] Rekha, R., Ashish, D., Venkatesh, K. S., “New potential field method for rough terrain path planning using genetic algorithm for a 6-wheel rover”, Robotics and Autonomous Systems, (2015).
• [22] Oscar, M., Ulises, O-R., Roberto, S., “Path planning for mobile robots using Bacterial Potential Field for avoiding static and dynamic obstacles. Expert Systems with Applications”, 42(12): 5177-5191, (2015).
• [23] Orozco-Rosas, U., Montiel, O., Sepúlveda, R., “Parallel bacterial potential field algorithm for path planning in mobile robots. a GPU implementation”, Studies in Computational Intelligence, 749: 207-222, (2018).
• [24] Contreras-Cruz, M.A., Ayala-Ramirez, V., Hernandez-Belmonte, U.H., “Mobile robot path planning using artificial bee colony and evolutionary programming”, Applied Soft Computing, 30: 319–328, (2015).
• [25] Liang, J.-H., Lee, C.-H., “Efficient collision-free path-planning of multiple mobile robots' system”, Advances in Engineering Software, 79: 47-56, (2015).
• [26] Mohanty, P. K., Parhi, D. R., “Optimal path planning for a mobile robot using cuckoo search algorithm”, Journal of Experimental & Theoretical Artificial Intelligence, (2016).
• [27] Saraswathi, M.B.L., Muralib, G. B., Deepakc, B.B.V.L., “Optimal Path Planning of Mobile Robot Using Hybrid Cuckoo Search-Bat Algorithm”, In International Conference on Robotics & Smart Manufuring (RoSMa2018). Procedia Computer Science, 133: 510–517, (2018).
• [28] Lamini, C., Benhlimab, S., Elbekri, A., “Genetic Algorithm Based Approach for Autonomous Mobile Robot Path Planning”, Procedia Computer Science, 127: 180–189, (2018).
• [29] Liu, F., Liang, S., Xian, X., “Optimal robot path planning for multiple goals visiting based on Tailored genetic algorithm”, International Journal of Computational Intelligence Systems, 7(6): 1109-1122, (2014).
• [30] Nash, A., Daniel, K., Koenig, S., Felner, A., “Theta*, Any-angle path planning on grids”, Journal of Artificial Intelligence Research, 39: 533–579, (2010).
• [31] Hidalgo-Paniagua, A., Vega-Rodriguez, M. A., Ferruz, J., Pavón, N., “MOSFLA-MR, Multi-Objective Shuffled Frog-Leaping Algorithm alied to Mobile Robot Path Planning”, Engineering Applications of Artificial Intelligence, 44: 123–136, (2015).
• [32] Ni, J., Yin, X., Chen, J., Li, X., “An improved shuffled frog leaping algorithm for robot path planning”, In 10th IEEE International Conference Natural Comptation (ICNC), 545-549, (2014).
• [33] Duchon, F., Babineca, A., Kajana, M., BeĖoa, P., Floreka, M., Ficoa, T., Jurišica, L., “Path Planning with Modified A Star Algorithm for a Mobile Robot”, Modeling of Mechanical & Mechnic Systems MMaMS 2014 In Procedia Engineering, 96: 59 – 69, (2014).
• [34] Abu Baker, A., “A Novel Mobile Robot Navigation System Using Neuro-Fuzzy Rule-Based Optimization Technique”, Research Journal of Applied Sciences, Engineering and Technology, 4(15): 2577-2583, (2012).
• [35] Quinonez, Y., Ramirez, M., Lizarraga, C., Tostado, I., Bekios, J., “Autonomous robot navigation based on pattern recognition techniques and artificial neural networks”, Advanced Intelligent Computing, (2015).
• [36] Medina-Santiago, A., Campus-Anzueto, J.L., Vazquez-Feijoo, J.A., Hernandez-deLeon, H.R., Mota-Grajales, R., “Neural control systems in obstacle avoidance in mobile robots using ultrasonic sensors”, Journal of Applied Research and Technology, 2: 104-110, (2014).
• [37] Pothal, J., Parhi, D., “Navigation of multiple mobile robots in a highly clutter terrains using adaptive neuro-fuzzy inference system”, Elsevier Robotics and Autonomous Systems, 72: 48-58, (2015).
• [38] Hidalgo-Paniagua, A., Miguel, A., Rodriguez, V., Ferruz, J., Pavon, N., “Solving the multi-objective path planning problem in mobile robotics with a firefly based approach”, Soft Computing, 1-16, (2015).
• [39] Patle, B. K., Dayal, P., Jagadeesh, A., Sahu, O., “Real time navigation approach for mobile robot”, Journal of Computers, 135-142, (2017).
• [40] Brand, M., Xiao-Hua, Yu., “Autonomous robot path optimisation using firefly algorithm”, International Conference on Machine Learning and Cybernetics, 1028-1030, (2013).
• [41] Patle, B.K., Pandey, A., Jagadeesh, A., Parhi, D.R.K., “Path planning in uncertain environment by using firefly algorithm”, Defence Technology, (2018).
• [42] Liu, W., Niu, B., Chen, Zhu, H., “Robot path planning using bacterial foraging algorithm”, Journal of Computational and Theoretical Nanoscience, 10(12): 2890-2896, (2013).
• [43] Liang, X., Li, L., Wu, J., Chen, H., “Mobile robot path planning based on adaptive bacterial foraging algorithm”, Journal of Central South University, 20(12): 3391-3400, (2013).
• [44] Abbas, N.H., Ali, F.M., “Path planning of an autonomous mobile robot using enhanced bacterial foraging optimization algorithm”, Al-Khwarizmi Engineering Journal, 12(4): 26- 35, (2017).
• [45] Abdessemed, F., Faisal, M., Hedjar, R., “A hierarchical fuzzy control design for indoor mobile robot”, International Journal of Advanced Robotic Systems, 11(1): 1-16, (2014).
• [46] Castillo, O., Martinez-Marroquin, R., Melin, P., Valdez, F., Soria, J., “Comparative study of bio-inspired algorithms applied to the optimization of type-1 and type-2 fuzzy controllers for an autonomous mobile robot”, Elsevier Information sciences, 192: 19-38, (2012).
• [47] Rajput, U., Kumari., M., “Mobile robot path planning with modified ant colony optimization”, International Journal of Bio-Inspired Computation, 9(2): 106-113, (2017).
• [48] Liu, J., Yang, J., Liu, H., Tian, X., Gao, M., “An improved ant colony algorithm for robot path planning”, Soft Computing, 21(19): 5829-5839, (2016).
• [49] Tang, B. W., Zhu, Z. X., Luo, J. J., “Hybridizing Particle Swarm Optimization and Differential Evolution for the Mobile Robot Global Path Planning”, International Journal of Advanced Robotic Systems 13(3): 1–17, (2016).
• [50] Li, G., Chou, W.S., “Path planning for mobile robot using self-adaptive learning particle swarm optimization”, Science China Information Sciences, 61: (2018).
• [51] Das, P.K., Behera, H.S., Panigrahi, B.K., “A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning”, Swarm and Evolutionary Computation, 28: 14-28, (2016).
• [52] Ren, H., Yin, R., Li, F., Wang, W., Huo, M., “Research on Q-ELM algorithm in robot path planning”, In 28th Chinese Control and Decision Conference (CCDC), IEEE, 5975-5979, (2016).
• [53] Xiaoyun, L., Qingrui, Z., Hailin, R., Changhao, S., “Reinforcement learning for robot navigation in nondeterministic workspaces”, Proceedings of International Conference on Cloud Computing and Intelligence Systems (CCIS2018), 615-619, (2018).
• [54] Watchanupaporn, O., Pudtuan, P., “Multi-robot target reaching using modified Q-learning and PSO”, In 2nd International Conference on Control, Automation and Robotics, 66-69, (2016).
• [55] Low, E.S., Ong, P., Cheah, K.C., “Solving the optimal path planning of a mobile robot using improved Q-learning”, Robotics and Autonomous Systems, 115: 143 – 161, (2019).
• [56] Batsaikhan, D., Soon-Geul, L., Donghan, K., Hyeong, K. J., Young, C. N., “Scan matching online cell decomposition for coverage path planning in an unknown environment”, International Journal of Precision Engineering and Manufacturing, 14(9): 1551-1558, (2013).
• [57] Li, P., Huang, X. H., Wang, M., “A novel hybrid method for mobile robot path planning in unknown dynamic environment based on hybrid DSm model grid map”, Journal of Experimental & Theoretical Artificial Intelligence, 23: 5–22, (2011).
• [58] Garrido, S., Moreno, L., Blanco, D., Jurewicz, P., “Path planning for mobile robot navigation using Voronoi diagram and fast, marching”, International Journal of Robotics and Automation (IJRA), 2(1): 42-64, (2011).
• [59] MandaI, P., Kumar, R., Maitra, M., Roy, S., “Path planning of autonomous mobile robot: A new approach”, In Proceedings of 7th International Conference on Intelligent Systems and Control (ISCO 2013), (2013).
• [60] Sfeir, J., Saad, M., Saliah-Hassane, H., “An improved artificial potential field approaches to real-time mobile robot path planning in an unknown environment”, In IEEE International Symposium on Robotic and Sensors Environments (ROSE), 208-213, (2011).
• [61] Mohammad, A. K. J., Mohammad, H. G., Eyad, A. F., “Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field”, Soft Computing, 16(1): 153-164, (2012).
• [62] Synodinos, A., Aspragathos, N., “Robot Path Planning of a mobile robot using Solid Modeling Techniques on Potential Field”, In International Conference on Mechatronics and Embedded Systems and Applications (MESA), 549-553, (2010).
• [63] Raja, P., Pugazhenthi, S., “Path planning for a mobile robot in dynamic workspaces”, International Journal of the Physical Sciences, 6(20): 4721-4731, (2011).
• [64] Li, W., Yang, C., Jiang, Y., Liu, X., Su, C.-Y., “Motion planning for omnidirectional wheeled mobile robot by potential field method”, Hindawi Journal of Advanced Transportation, 1-12, (2017).
• [65] Yun, S. C., Parasuraman, S., Ganapathy, V., “Dynamic path planning algorithm in mobile robot navigation”, In Symposium on Industrial Electronics and Applications (ISIEA2011), 364-369, (2011).
• [66] Kang, X., Yue, Y., Li, D., Maple, C., “Genetic algorithm based solution to dead problems in robot navigation”, International Journal of Computer Applications in Technology, 41(3/4): 177-184, (2011).
• [67] Shi, P., Cui, Y., “Dynamic path planning for mobile robot based on Genetic Algorithm in unknown environment”, In Proceedings of Chinese Control and Decision Conference, 4325-4329, (2010).
• [68] Jianjun, N., Kang, W., Haohao, H., Liuying, W., Chengming, L., “Robot path planning based on an improved genetic algorithm with variable length chromosome”, In 12th International Conference on Natural Computation, Fuzzy Systems & Knowledge Discovery (ICNC-FSKD), (2016).
• [69] Patle, B. K., Parhi, K. D. R., Jagadeesh, A., Kumar, K. S., “Matrix-binary codes based genetic algorithm for path planning of mobile robot”, Computers & Electrical Engineering 67: 708-728, (2017).
• [70] Mohanta, J., Parhi, D., Patel, S., “Path planning strategy for autonomous mobile robot navigation using petri-GA optimization”, Computers and Electrical Engineering, 37(6): 1058-1070, (2011).
• [71] Tuncer, A., Yildirim, M., “Dynamic path planning of mobile robots with improved genetic algorithm”, Elsevier Computers and Electrical Engineering, 38(6): 1564-1572, (2012).
• [72] Arora, T., Gigras, Y., Arora, V., “Robotic path planning using genetic algorithm in dynamic workspace”, International Journal of Computer Application, 89(11): 8-12, (2014).
• [73] Hussein, A., Mostafa, H., Badrel-din, M., Sultan, O., Khamis, A., “Metaheuristic optimization approach to mobile robot path planning”, International Conference on Engineering and Technology (ICET), 1-6, (2012).
• [74] Tazir, M. L., Azouaoui, O., Hazerchi, M., Brahimi, M., “Mobile robot Path Planning for Complex Dynamic Workspace”, In International Conference on Aerospace Robotics (ICAR), IEEE, (2015).
• [75] Xiao, K., Gao, C., Hu, X., Pan, H. W., “Improved Theta*, Improved any-angle path planning on girds”, Journal of Computer Information System, 10: 8881–8890, (2014).
• [76] Brahmi, H., Ammar, B., Alimi, A., “Intelligent path planning algorithm for autonomous robot based on recurrent neural networks”, International Conference on Advanced Logistics and Transport (ICALT), 199-204, (2013).
• [77] Singha, A., Ray, A. K., and Samaddar, A. B., “Navigation of mobile robot in a grid-based workspace using local and target weighted workspace using local and target weighted”, In 8th International Conference On Computing, Communication and Networking Technologies (ICCCNT) IIT, (2017).
• [78] Patle, B.K., Parhi, D.R., Jagadeesh, A., Kashyap, S.K., “On firefly algorithm. optimization and alication in mobile robot navigation”, World Journal of Engineering, 14(1): 1-17, (2017).
• [79] Hossain, M., Ferdousand, I., “Autonomous robot path planning in dynamic workspace using a new optimization technique inspired by bacterial foraging technique”, Robotics and Autonomous Systems, 64: 137-141, (2015).
• [80] Abiyev, R., Ibrahim, D., Erin, B., “Navigation of mobile robot in presence of obstacles”, Advances in Engineering Software, 41(10): 1179-1186, (2010).
• [81] Moustris, G. P., Tzafestas, S. G., “Switching fuzzy tracking control for mobile robots under curvature constraints”, Control Engineering Practice, 19(1): 45-53, (2011).
• [82] Jaradat, M., Garibeh, M., Feilat, E.A., “Autonomous mobile robot planning using hybrid fuzzy potential field”, Soft Computing, 16: 153–164, (2012).
• [83] Chih-T, Y., Ming-Feng, C., “A study of fuzzy control with ant colony algorithm used in mobile robot for shortest path planning and obstacle avoidance”, Microsystem Technologies, 24(1): 125-135, (2018).
• [84] Al-Mutib, K., Mattar, E., Alsulaiman, M., “Implementation of fuzzy decision based mobile robot navigation using stereo vision”, Procedia Computer Science, 62: 143-150, (2015).
• [85] Mo, H., Tang, Q., Meng, L., “Behavior-Based Fuzzy Control for Mobile Robot Navigation”, Mathematical Problems in Engineering, Hindawi Publishing Corporation, (2013).
• [86] Ren, L., Wang, W., Du, Z., “A new fuzzy intelligent obstacle avoidance control strategy for wheeled mobile robot”, In IEEE International Conference on Mechatronics and Automation (ICMA), 1732-1737, (2012).
• [87] Dongshu, W., Yusheng, Z., Wenjie, S., “Behavior-based hierarchical fuzzy control for mobile robot navigation in dynamic workspace”, International Conference on Control and Decision Conference (CCDC), 2419-2424, (2011).
• [88] Ayari, E., Hadouaj, S., Ghedira, K., “A fuzzy logic method for autonomous robot navigation in dynamic and uncertain workspace composed with complex traps”, In 5th International Multi-conference on Computing in the Global Information Technology, 18-23, (2010).
• [89] Algabri, M., Mathkour, H., Ramdane, H., “Mobile robot navigation and obstacle-avoidance using ANFIS in unknown workspace”, International Journal of Computer Applications, 91(14): 36-41, (2014).
• [90] Ganapathy, V., Jie, T., Parasuraman, S., “Improved ant colony optimization for robot navigation”, International Symposium on Mechatronics and its Applications (ISMA), 1-6, (2010).
• [91] Hsu, C., Hou, R., Wang, W., “Path planning for mobile robots based on improved ant colony optimization”, International Conference on Systems, Man, and Cybernetics (SMC), 2777 - 2782, (2013).
• [92] Ganganath, N., Cheng, C., Tse, C.K., “An ACO-based off-line path planner for non-holonomic mobile robots”, International Symposium on Circuits and Systems (ISCAS), 1038-1041, (2014).
• [93] Chen, X., Kong, Y., Fang, X., Wu, Q., “A fast two-stage ACO algorithm for robotic path planning”, Neural Computing and Applications, 22(2): 313-319, (2013).
• [94] Wang, H-J., Yong, F., Zhao, Z-O., Yue, Y-J., “An Improved Ant Colony Algorithm of Robot Path Planning for Obstacle Avoidance”, Hindawi Journal of Robotics, (2019).
• [95] Zhao, J., Cheng, D., Hao, C., “An Improved Ant Colony Algorithm for Solving the Path Planning Problem of the Omnidirectional Mobile Vehicle”, Hindawi Publishing Corpotation, Mathematical Problems in Engineering, (2016).
• [96] Purian, F.K., Sadeghian, E., “Mobile robots path planning using ant colony optimization and fuzzy logic algorithms in unknown dynamic workspaces”, International Conference on Control, Automation, Robotics and Embedded Systems (CARE), 1-6, (2013).
• [97] Brand, M., Masuda, M.C., Wehner, N., Yu, X., “Ant colony optimization algorithm for robot path planning”, In International Conference On Computer Design and Applications, 3: 436-440, (2010).
• [98] Zhang, Q., Ma, J., Liu, Q., “Path planning based quad tree representation for mobile robot using hybrid-simulated annealing and ant colony optimization algorithm”, Proceedings of the 10th World Congress on Intelligent Control and Automation, 2537-2542, (2012).
• [99] Li, Q., Tang, Y., Wang, L., Zhang, C., Yin, Y., “A specialized particle swarm optimization for global path planning of mobile robots”, In 3rd International Workshop on Advanced Computational Intelligence (IWACI), 271-276, (2010).
• [100] Wang, X. Y., Zhang, G. X., Zhao, J. B., Rong, H., Ipate, F., Lefticaru, R., “A Modified Membrane-Inspired Algorithm Based on Particle Swarm Optimization for Mobile Robot Path Planning”, International Journal of Computers, Communications & Control (IJCCC), 10(6): 732–745, (2015).
• [101] Algabri, M., Hassan, M., Hedjar, R., Alsulaiman, M., “Comparative study of soft computing technique for mobile robot navigation in an environment”, Computers in Human Behavior, 50: 42-56, (2015).
• [102] Dewanga, H. S., Mohantya, P. K., Kundu, S., “A Robust Path Planning for Mobile Robot Using Smart Particle Swarm Optimization”, Procedia Computer Science, 133: 290-297, (2018).
• [103] Ahmadzadeh, S., Ghanavati, M., “Navigation of mobile robot using the PSO particle swarm optimization”, Journal of Academic and Applied Studies (JAAS), 2(1): 32-38, (2012).
• [104] Zhang, Y., Gong, D., Zhang, J., “Robot path planning in uncertain workspace using multi-objective particle swarm optimization”, Elsevier Neuro-computing, 103: 172-185, (2013).
• [105] Shiltagh, N., Jalal, L., “Optimal path planning for intelligent mobile robot navigation using modified particle swarm optimization”, International Journal of Engineering and Advanced Technology, 2(4): 260-267, (2013).
• [106] Chung, H., Hou, C., Liu, S., “Automatic navigation of a wheeled mobile robot using particle swarm optimization and fuzzy control”, International Symposium on Industrial Electronics (ISIE), 1-6, (2013).
• [107] Juang, C., Chang, Y., “Evolutionary-group-based particle swarm optimized fuzzy controller with application to mobile-robot navigation in unknown workspaces”, Transactions on Fuzzy Systems, 19(2): 379 - 392, (2011).
• [108] Allawi, Z., Abdalla, T., “PSO-optimized type-2 fuzzy logic controller for navigation of multiple mobile robots”, International Conference on Methods and Models in Automation and Robotics (MMAR), 33-39, (2014).
• [109] Mohajer, B., Kiani, K., Samiei, E., Sharifi, M., “A new online random particles optimization algorithm for mobile robot path planning in dynamic workspaces”, Hindawi Mathematical Problems in Engineering, 1-9, (2013).
• [110] Hoang, H. V., Sang, H. A., Tae, C. C., “Dyna-Q-based vector direction for path planning problem of autonomous mobile robots in unknown environments”, Journal Advanced Robotics, 3(27): 159–173, (2013).
• [111] Jaradat, M.A.K., Al-Rousan, M., Quadan, L., “Reinforcement based mobile robot navigation in dynamic environment”, Robotics and Computer-Integrated Manufacturing, 27(1): 135-149, (2011).
• [112] Ribeiro, T., Gonçalves, F., Garcia, I., Lopes, G., Ribeiro, A.F, “Q-learning for autonomous mobile robot obstacle avoidance”, IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), (2019).
• [113] Valiollahi, S., Ghaderi, R., Ebrahimzadeh, A., “A fuzzy Q-learning approach to navigation of an autonomous robot”, In 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012), 520-525, (2012).
• [114] Xu, K., Wuy, F., Zhaoz, J., “Simplified online Q-learning for LEGO EV3 robot”, IEEE International Conference on Control System, Computing and Engineering, 77-80, (2015).
• [115] Zheng, Y., Wei, X., Zhou, X., “Dynamic obstacle avoidance based on multi-sensor fusion and Q-learning”, In 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC 2019), IEEE, 1569-1573, (2019).
• [116] Ullah, Z., Xu, Z., Zhang, L., Zhang, L., Ullah,W., “RL and ANN Based Modular Path Planning Controller for Resource-Constrained Robots in the Indoor Complex Dynamic Environment”, In IEEE Access, 6: 74557-74568, (2018).
• [117] Lakhmi, C. J., Atulya, K. N., “A deterministic improved Q-learning for path planning of a mobile robot”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43(5): 1141-1153, (2013).
• [118] Sichkar, V. N., “Reinforcement learning algorithms in global path planning for mobile robot”, International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), IEEE, 1-5, (2019).
• [119] Jiang, J., Xin, J., “Path planning of a mobile robot in a free-space workspace using Q-learning”, Progress in Artificial Intelligence, 1-10, (2018).
• [120] Li, S., Xu, X., and Zuo, L., “Dynamic path planning of a mobile robot with improved Q-learning algorithm”, Proceeding of the 2015 IEEE International Conference on Information and Automation, 409-414, (2015).
• [121] Motlagh, O., Nakhaeinia, D., Tang, S., Karasfi, B., Khaksar, W., “Automatic navigation of mobile robots in unknown workspaces”, Neural Computing and Applications, 24(7): 1569-1581, (2014).
• [122] Dudarenko, D., Rubtsova, J., Kovalev, A., Sivchenko, O., “Reinforcement Learning Approach for Navigation of Ground Robotic Platform in Statically and Dynamically Generated Environments”, In IFAC Papers On-Line, 52(25): 445–450, (2019).
• [123] Duguleanaa, M., Mogen, G., “Neural networks based reinforcement learning for mobile robot’s obstacle avoidance”, Expert Systems with Applications, 62: 104-115, (2016).
• [124] Das, P.K., Beheraa, H.S., Panigrahi, B.K., “Intelligent-based multi-robot path planning inspired by improved classical Q-learning and improved particle swarm optimization with perturbed velocity”, Engineering Science and Technology, an International Journal, 19(1): 651-669, (2016).
• [125] Xu, C., Duan, H., Liu, F., “Chaotic Artificial Bee Colony Approach to Uninhabited Combat Air Vehicle (UCAV) path planning”, Aerodynamic Science and Technology, 14(8): 535-541, (2011).
• [126] Li, B., Chiong, R., Gong, LG., “Search-evasion path planning for submarines using the artificial bee colony algorithm”, In Evolutionary computation (CEC), IEEE congress, 528-535, (2014).
• [127] Wang, G., Guo, L., Duan, H., Wang, H., Liu, L., M Shao., “A hybrid metaheuristic DE/CS algorithm for UCAV three-dimension path planning”, Science World Journal, DOI: 10.1100/2012/583973, (2012).
• [128] Xie, C., Zheng, H., “Application of improved Cuckoo search algorithm to path planning unmanned aerial vehicle”, In International Conference on Intelligent Computing, Cham Springer, 722-729, (2016).
• [129] Pu, H., Zhen, Z., Wang, D., “Modified shuffled frog leaping algorithm for optimization of UAV flight controller”, International Journal of Intelligent Computing & Cybernetics, 4(1): 25-39, (2011).
• [130] Liang, B., Zhen, Z., Jiang, J., “Modified shuffled frog leaping algorithm optimized control for air-breathing hypersonic flight vehicle”, International Journal of Advance Robot System, 13(6), (2016).