Hybridization of genetic algorithm and fuzzy based  TOPSIS method to solve the robot path planning problem for multi objectives

K S. Suresh; K. R. Sekar; R. Venkatesan; S. Venugopal

doi:10.53730/ijhs.v6nS5.9059

Authors

Suresh K S.
kssuresh@cse.sastra.ac.in
SASTRA Deemed University, Tamilnadu, India
K.R. Sekar SASTRA Deemed University, Tamilnadu, India
R. Venkatesan SASTRA Deemed University, Tamilnadu, India
S. Venugopal Director, NIIT, Nagaland, India

Keywords:

Genetic algorithm, Mobile Robot Path Planning, multi-criteria decision-analysis, Technique for Order of Preference by Similarity to Ideal Solution, Preferences, multi objectives

Abstract

A variety of approaches are employed to solve the Mobile Robot Path Planning Problem (MRPP), which is the essential domain of research from industrial automation to domestic appliances. Since MRPP is categorized as an NP-hard problem, non-deterministic approaches are preferred to obtain optimal results. The Genetic Algorithm(GA) is the popular methodology for determining the optimal path for robot navigation. The complexity increases in finding the optimal path for the MRPP problem, if more than one objective is to be considered. To reduce the complexity, one of the multi-criteria decision analysis (MCDA) methodologies with fuzzy logic is hybridized to solve MRPP without losing the advantage of the GA approach. In this article, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is employed for determining the best path out of given paths. As a preprocessing of the population, a Genetic Algorithm (GA) is employed, which is having the advantage of exploring the population with crossover, mutation, and selection operators. The output of the GA is given to the Fuzzy TOPSIS method where the degree of preference is given as the linguistic variable.

Downloads

Download data is not yet available.

References

P. Raja, “Optimal path planning of mobile robots: A review,” Int. J. Phys. Sci., vol. 7, no. 9, pp. 1314–1320, 2012, doi: 10.5897/ijps11.1745.

P. Salgado, “Path planning by hybrid PSO-Splines algorithm,” no. 1, pp. 1–2, doi: 10.1109/INES.2017.8118542.obstacles.

P. K. Das, H. S. Behera, S. Das, H. K. Tripathy, B. K. Panigrahi, and S. K. Pradhan, “A hybrid improved PSO-DV algorithm for multi-robot path planning in a clutter environment,” Neurocomputing, vol. 207, pp. 735–753, Sep. 2016, doi: 10.1016/j.neucom.2016.05.057.

S. Geetha, G. M. Chitra, and V. Jayalakshmi, “Multi objective mobile robot path planning based on hybrid algorithm,” ICECT 2011 - 2011 3rd Int. Conf. Electron. Comput. Technol., vol. 6, pp. 251–255, 2011, doi: 10.1109/ICECTECH.2011.5942092.

B. K. Patle, G. Babu L, A. Pandey, D. R. K. Parhi, and A. Jagadeesh, “A review: On path planning strategies for navigation of mobile robot,” Def. Technol., vol. 15, no. 4, pp. 582–606, 2019, doi: 10.1016/j.dt.2019.04.011.

F. Kursawe, “A Variant of Evolution Strategies for Vector Optimization 1 Introduction 2 Shortcomings of Conventional Methods 3 Evolution Strategies ...,” Int. Conf. Parallel Probl. Solving from Nat., pp. 193–197, 1990.

M. S. Circiu and F. Leon, “Comparative Study of Multiobjective Genetic Algorithms,” Bull. Polytech. Inst. Iasi, tome LVI (LX), Sect. Autom. Control Comput. Sci., no. Lx, pp. 35–47, 2010, [Online]. Available: http://florinleon.byethost24.com/papers/1006.pdf?i=1.

A. Hafezalkotob, A. Hafezalkotob, H. Liao, and F. Herrera, “An overview of MULTIMOORA for multi-criteria decision-making: Theory, developments, applications, and challenges,” Inf. Fusion, vol. 51, pp. 145–177, 2019, doi: 10.1016/j.inffus.2018.12.002.

S. Leroy, J. P. Laumond, and T. Simeon, “Multiple path coordination for mobile robots: A geometric algorithm,” IJCAI Int. Jt. Conf. Artif. Intell., vol. 2, no. June, pp. 1118–1123, 1999.

K. D. Kalyanmoy Deb, “Multi-objective Optimization,” Search Methodol., no. ISBN : 978-1-4614-6939-1, pp. 403–449, 2013, doi: https://doi.org/10.1007/978-1-4614-6940-7_15.

R. K. Panda and B. B. Choudhury, “An effective path planning of mobile robot using genetic algorithm,” Proc. - 2015 IEEE Int. Conf. Comput. Intell. Commun. Technol. CICT 2015, vol. 2022, pp. 287–291, 2015, doi: 10.1109/CICT.2015.145.

T. Barr, “A mobile robot path planning using genetic algorithm in static environment,” Talent Dev. Excell., vol. 11, no. 1, pp. 10–17, 2019.

S. Khemaissia, “Multi-Objective Genetic Algorithm-Based Time Optimal Trajectory Planning and Robot Control,” vol. 4567700, 2011.

A. Hidalgo-Paniagua, M. A. Vega-Rodríguez, and J. Ferruz, “Applying the MOVNS (multi-objective variable neighborhood search) algorithm to solve the path planning problem in mobile robotics,” Expert Syst. Appl., vol. 58, pp. 20–35, 2016, doi: 10.1016/j.eswa.2016.03.035.

F. Ahmed and K. Deb, “Multi-objective optimal path planning using elitist non-dominated sorting genetic algorithms,” Soft Comput., vol. 17, no. 7, pp. 1283–1299, 2013, doi: 10.1007/s00500-012-0964-8.

Y. Xue and J.-Q. Sun, “Solving the Path Planning Problem in Mobile Robotics with the Multi-Objective Evolutionary Algorithm,” Appl. Sci., vol. 8, p. 1425, 2018, doi: 10.3390/app8091425.

T. Y. Chen and C. Y. Tsao, “The interval-valued fuzzy TOPSIS method and experimental analysis,” Fuzzy Sets Syst., vol. 159, no. 11, pp. 1410–1428, 2008, doi: 10.1016/j.fss.2007.11.004.

J. Wei, “TOPSIS method for multiple attribute decision making with incomplete weight information in linguistic setting,” J. Converg. Inf. Technol., vol. 5, no. 10, pp. 181–187, 2010, doi: 10.4156/jcit.vol5.issue10.23.

A. Kumar et al., “A review of multi criteria decision making (MCDM) towards sustainable renewable energy development,” Renew. Sustain. Energy Rev., vol. 69, no. November 2016, pp. 596–609, 2017, doi: 10.1016/j.rser.2016.11.191.

E. Cables, M. S. García-Cascales, and M. T. Lamata, “The LTOPSIS: An alternative to TOPSIS decision-making approach for linguistic variables,” Expert Syst. Appl., vol. 39, no. 2, pp. 2119–2126, 2012, doi: 10.1016/j.eswa.2011.07.119.

W. Siming, Z. Tiantian, and L. Weijie, “Mobile Robot Path Planning Based on Improved Artificial Potential Field Method,” 2018 IEEE Int. Conf. Intell. Robot. Control Eng., vol. 2, no. 1, pp. 29–33, 2018.

Y. Zhao and J. Gu, “Robot Path planning based on improved genetic algorithm,” 2013 IEEE Int. Conf. Robot. Biomimetics, ROBIO 2013, pp. 2515–2522, 2013, doi: 10.1109/ROBIO.2013.6739850.

A. Madkour, W. G. Aref, F. U. Rehman, M. A. Rahman, and S. Basalamah, “A Survey of Shortest-Path Algorithms,” pp. 1–26, 2017, [Online]. Available: http://arxiv.org/abs/1705.02044.

A. Bouri, J. M. Martel, and H. Chabchoub, “A multi-criterion approach for selecting attractive portfolio,” J. Multi-Criteria Decis. Anal., vol. 11, no. 4–5, pp. 269–277, 2002, doi: 10.1002/mcda.334.