Application of improved ant colony optimization in mobile robot trajectory planning

Xue Li; Lei Wang; Xue Li; Lei Wang

doi:10.3934/mbe.2020352

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 6: 6756-6774. doi: 10.3934/mbe.2020352

Previous Article Next Article

Research article Special Issues

Application of improved ant colony optimization in mobile robot trajectory planning

Xue Li ,
Lei Wang ^,

School of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, China

Received: 27 July 2020 Accepted: 22 September 2020 Published: 30 September 2020

Under the condition of known static environment and dynamic environment, an improved ant colony optimization is proposed to solve the problem of slow convergence, easily falling into local optimal solution, deadlock phenomenon and other issues when the ant colony optimization is constructed. Based on the traditional ant colony optimization, the ant colony search ability at the initial moment is strengthened and the range is expanded to avoid falling into the local optimal solution by adaptively changing the volatility coefficient. Secondly, the roulette operation is used in the state transition rule which improves the quality of the solution and the convergence speed of the algorithm effectively. Finally, through the elite selection and the node crossover operation of the better path, the global search efficiency and convergence speed of the algorithm are effectively improved. Several experimental results have also been obtained by applying the improved ant colony optimization to obstacle avoidance. The experimental results demonstrate the feasibility and effectiveness of the algorithm.
- improved ant colony optimization,
- adaptive volatility coefficient,
- roulette operation,
- the elite selection,
- node crossover operation
Citation: Xue Li, Lei Wang. Application of improved ant colony optimization in mobile robot trajectory planning[J]. Mathematical Biosciences and Engineering, 2020, 17(6): 6756-6774. doi: 10.3934/mbe.2020352

Related Papers:

Abstract

Under the condition of known static environment and dynamic environment, an improved ant colony optimization is proposed to solve the problem of slow convergence, easily falling into local optimal solution, deadlock phenomenon and other issues when the ant colony optimization is constructed. Based on the traditional ant colony optimization, the ant colony search ability at the initial moment is strengthened and the range is expanded to avoid falling into the local optimal solution by adaptively changing the volatility coefficient. Secondly, the roulette operation is used in the state transition rule which improves the quality of the solution and the convergence speed of the algorithm effectively. Finally, through the elite selection and the node crossover operation of the better path, the global search efficiency and convergence speed of the algorithm are effectively improved. Several experimental results have also been obtained by applying the improved ant colony optimization to obstacle avoidance. The experimental results demonstrate the feasibility and effectiveness of the algorithm.

References

[1]	V. Azimirad, H. Shorakaei, Dual hierarchical genetic-optimal control: A new global optimal path planning method for robots, J. Manufacturing Syst., 33 (2014), 139-148. doi: 10.1016/j.jmsy.2013.09.006
[2]	A. Azzabi, K. Nouri, An advanced potential field method proposed for mobile robot path planning, Trans. Inst. Meas. Control, 41 (2019), 3132-3144. doi: 10.1177/0142331218824393
[3]	M. Nazarahari, E. Khanmirza, S. Doostie, Multi-objective multi-robot path planning in continuous environment using an enhanced Genetic Algorithm, Expert Syst. Appl., 115 (2019), 106-120. doi: 10.1016/j.eswa.2018.08.008
[4]	U. Rajput, M. Kumari, Mobile robot path planning with modified ant colony optimisation, Int. J. Bio-Inspired Comput., 9 (2017), 106-113. doi: 10.1504/IJBIC.2017.083133
[5]	Q. H. Wu, Y. Zhang, Z. M. Ma, Overview of ant colony algorithm, Microcomput. Infor., 29 (2011), 1-2.
[6]	Z. X. Huang, D. K. Zhang, Q. H. Li, A review of ant colony algorithm and its improved forms, Comput. Technol. Autom., 25 (2006), 35-38.
[7]	Z. B. Pei, X. B. Chen, Improved ant colony algorithm and its application in obstacle avoidance of robot, CAAI Trans. Intell. Syst., 10 (2015), 90-96.
[8]	X. F. Wan, W. Hu, W. Y. Fang, Robot path planning based on improved ant colony algorithm, Comput. Eng. Appl., 50 (2014), 63-66.
[9]	Z. G. Qu, C. Yang, Study on global trajectory planning of mobile robot based on improved ant colony algorithm, J. Nanjing Norm. Univ.: Nat. Sci. Ed., 38 (2015), 81-85.
[10]	C. C. Fang, P. M. Sun, Robot path planning based on improved ant colony algorithm, Meas. Control Tech., 37 (2018), 28-31.
[11]	U. Orozco-Rosas, O. Montiel, R. Sepulveda, Mobile robot path planning using membrane evolutionary artificial potential field, Appl. Soft Comput., 77 (2019), 236-251. doi: 10.1016/j.asoc.2019.01.036
[12]	M. A. P. Garcia, O. Montiel, O. Castillo, Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evolution, Appl. Soft Comput., 9 (2009), 1102-1110. doi: 10.1016/j.asoc.2009.02.014
[13]	X. J. Li, D. M. Yu, Study on an optimal path planning for a robot based on an improved ANT colony algorithm, Autom. Control Comput. Sci., 53 (2019), 236-243. doi: 10.3103/S0146411619030064
[14]	J. Cao, Robot global path planning based on an improved ant colony algorithm, J. Comput. Commun., 4 (2016), 11-19.
[15]	Y. Zhang, C. Chen, Q Liu, Mobile robot path planning using ant colony algorithm, Int. J. Control Autom., 9 (2016), 19-28.
[16]	H. Yang, J. Qi, Y. C. Miao, A new robot navigation algorithm based on a double-layer ant algorithm and trajectory optimization, IEEE Trans. Ind. Electron., 66 (2018), 8557-8566.
[17]	C. Imen, K. Anis, T. Sahar, B. Hachemi, A. Adel, A. S. Khaled, SmartPATH: An efficient hybrid ACO-GA algorithm for solving the global path planning problem of mobile robots, Int. J. Adv. Rob. Syst., 11 (2014), 1-15. doi: 10.5772/56810

Reader Comments

Your name:*

Email:*
© 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)