Multiobjective path optimization of an indoor AGV based on an improved ACO-DWA

Jinzhuang Xiao; Xuele Yu; Keke Sun; Zhen Zhou; Gang Zhou; Jinzhuang Xiao; Xuele Yu; Keke Sun; Zhen Zhou; Gang Zhou

doi:10.3934/mbe.2022585

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 12: 12532-12557. doi: 10.3934/mbe.2022585

Previous Article Next Article

Research article

Multiobjective path optimization of an indoor AGV based on an improved ACO-DWA

College of Electronic Information Engineering, Hebei University, Baoding 071000, China

Academic Editor: Vladimir Mityushev

Received: 03 July 2022 Revised: 07 August 2022 Accepted: 11 August 2022 Published: 26 August 2022

With their intelligence, flexibility, and other characteristics, automated guided vehicles (AGVs) have been popularized and promoted in traditional industrial markets and service industry markets. Compared with traditional transportation methods, AGVs can effectively reduce costs and improve the efficiency of problem solving in various application developments, but they also lead to serious path-planning problems. Especially in large-scale and complex map environments, it is difficult for a single algorithm to plan high-quality moving paths for AGVs, and the algorithm solution efficiency is constrained. This paper focuses on the indoor AGV path-planning problem in large-scale, complex environments and proposes an efficient path-planning algorithm (IACO-DWA) that incorporates the ant colony algorithm (ACO) and dynamic window approach (DWA) to achieve multiobjective path optimization. First, inspired by the biological population level, an improved ant colony algorithm (IACO) is proposed to plan a global path for AGVs that satisfies a shorter path and fewer turns. Then, local optimization is performed between adjacent key nodes by improving and extending the evaluation function of the traditional dynamic window method (IDWA), which further improves path security and smoothness. The results of simulation experiments with two maps of different scales show that the fusion algorithm shortens the path length by 9.9 and 14.1% and reduces the number of turns by 60.0 and 54.8%, respectively, based on ensuring the smoothness and safety of the global path. The advantages of this algorithm are verified. QBot2e is selected as the experimental platform to verify the practicability of the proposed algorithm in indoor AGV path planning.
- path planning,
- AGV,
- ant colony algorithm,
- dynamic window approach,
- multiobjective optimization
Citation: Jinzhuang Xiao, Xuele Yu, Keke Sun, Zhen Zhou, Gang Zhou. Multiobjective path optimization of an indoor AGV based on an improved ACO-DWA[J]. Mathematical Biosciences and Engineering, 2022, 19(12): 12532-12557. doi: 10.3934/mbe.2022585

Related Papers:

Abstract

With their intelligence, flexibility, and other characteristics, automated guided vehicles (AGVs) have been popularized and promoted in traditional industrial markets and service industry markets. Compared with traditional transportation methods, AGVs can effectively reduce costs and improve the efficiency of problem solving in various application developments, but they also lead to serious path-planning problems. Especially in large-scale and complex map environments, it is difficult for a single algorithm to plan high-quality moving paths for AGVs, and the algorithm solution efficiency is constrained. This paper focuses on the indoor AGV path-planning problem in large-scale, complex environments and proposes an efficient path-planning algorithm (IACO-DWA) that incorporates the ant colony algorithm (ACO) and dynamic window approach (DWA) to achieve multiobjective path optimization. First, inspired by the biological population level, an improved ant colony algorithm (IACO) is proposed to plan a global path for AGVs that satisfies a shorter path and fewer turns. Then, local optimization is performed between adjacent key nodes by improving and extending the evaluation function of the traditional dynamic window method (IDWA), which further improves path security and smoothness. The results of simulation experiments with two maps of different scales show that the fusion algorithm shortens the path length by 9.9 and 14.1% and reduces the number of turns by 60.0 and 54.8%, respectively, based on ensuring the smoothness and safety of the global path. The advantages of this algorithm are verified. QBot2e is selected as the experimental platform to verify the practicability of the proposed algorithm in indoor AGV path planning.

References

[1]	M. De Ryck, M. Versteyhe, F. Debrouwere, Automated guided vehicle systems, state-of-the-art control algorithms and techniques, J. Manuf. Syst., 54 (2020), 152-173. https://doi.org/10.1016/j.jmsy.2019.12.002 doi: 10.1016/j.jmsy.2019.12.002
[2]	K. Akka, F. Khaber, Mobile robot path planning using an improved ant colony optimization, Int. J. Adv. Robot. Syst., 15 (2018). https://doi.org/10.1177/1729881418774673
[3]	B. Patle, A. Pandey, D. Parhi, A. Jagadeesh, A review: on path planning strategies for navigation of mobile robot, Def. Technol., 15 (2019), 582-606. https://doi.org/10.1016/j.dt.2019.04.011 doi: 10.1016/j.dt.2019.04.011
[4]	C. Miao, G. Chen, C. Yan, Y. Wu, Path planning optimization of indoor mobile robot based on adaptive ant colony algorithm, Comput. Ind. Eng., 156 (2021), 107230. https://doi.org/10.1016/j.cie.2021.107230 doi: 10.1016/j.cie.2021.107230
[5]	S. Wu, Y. Du, Y. Zhang, Mobile robot path planning based on a generalized wavefront algorithm, Math. Probl. Eng., 2020 (2020), 1-12. https://doi.org/10.1155/2020/6798798 doi: 10.1155/2020/6798798
[6]	X. Xiong, H. Min, Y. Yu, P. Wang, Application improvement of A* algorithm in intelligent vehicle trajectory planning, Math. Biosci. Eng., 18 (2021), 1-21. https://doi.org/10.3934/mbe.2021001 doi: 10.3934/mbe.2021001
[7]	A. Ammar, H. Bennaceur, I. Châari, A. Koubâa, M. Alajlan, Relaxed Dijkstra and A* with linear complexity for robot path planning problems in large-scale grid environments, Soft Comput., 20 (2016), 4149-4171. https://doi.org/10.1007/s00500-015-1750-1 doi: 10.1007/s00500-015-1750-1
[8]	C. Xia, Y. Zhang, I. Chen, Learning sampling distribution for motion planning with local reconstruction-based self-organizing incremental neural network, Neural. Comput. Appl., 31 (2019), 9185-9205. https://doi.org/10.1007/s00521-019-04370-y doi: 10.1007/s00521-019-04370-y
[9]	R. K. Dewangan, A. Shukla, W. W. Godfrey, Three dimensional path planning using Grey wolf optimizer for UAVs, Appl. Intell., 49 (2019), 2201-2217. https://doi.org/10.1007/s10489-018-1384-y doi: 10.1007/s10489-018-1384-y
[10]	L. Zhang, Y. Zhang, Y. Li, Mobile robot path planning based on improved localized particle swarm optimization, IEEE Sens. J., 21 (2020), 6962-6972. https://doi.org/10.1109/JSEN.2020.3039275 doi: 10.1109/JSEN.2020.3039275
[11]	X. Tian, L. Liu, S. Liu, Z. Du, M. Pang, Path planning of mobile robot based on improved ant colony algorithm for logistics, Math. Biosci. Eng., 18 (2021), 3034-3045. https://doi.org/10.3934/mbe.2021152 doi: 10.3934/mbe.2021152
[12]	X. Li, L. Wang, L. Wang, Application of improved ant colony optimization in mobile robot trajectory planning, Math. Biosci. Eng., 17 (2020), 6756-6774. https://doi.org/10.3934/mbe.2020352 doi: 10.3934/mbe.2020352
[13]	H. Yang, J. Qi, Y. Miao, H. Sun, J. Li, A new robot navigation algorithm based on a double-layer ant algorithm and trajectory optimization, IEEE Trans. Ind. Electron., 66 (2019), 8557-8566. https://doi.org/10.1109/TIE.2018.2886798 doi: 10.1109/TIE.2018.2886798
[14]	Y. Zheng, Q. Luo, H. Wang, C. Wang, X. Chen, Path planning of mobile robot based on adaptive ant colony algorithm, J. Intell. Fuzzy Syst., 39 (2020), 5329-5338. https://doi.org/10.3233/JIFS-189018 doi: 10.3233/JIFS-189018
[15]	J. Chen, Y. Zhang, L. Wu, T. You, X. Ning, An adaptive clustering-based algorithm for automatic path planning of heterogeneous UAVs, IEEE Trans. Intell. Transp. Syst., 2021 (2021), 1-12. https://doi.org/10.1109/TITS.2021.3131473 doi: 10.1109/TITS.2021.3131473
[16]	J. Chen, C. Du, Y. Zhang, P. Han, W. Wei, A clustering-based coverage path planning method for autonomous heterogeneous UAVs, IEEE Trans. Intell. Transp. Syst., 2021 (2021), 1-11. https://doi.org/10.1109/TITS.2021.3066240 doi: 10.1109/TITS.2021.3066240
[17]	Z. Jiao, K. Ma, Y. Rong, H. Zhang, S. Wang, A path planning method using adaptive polymorphic ant colony algorithm for smart wheelchairs, J. Comput. Sci., 25 (2018), 50-57. https://doi.org/10.1016/j.jocs.2018.02.004 doi: 10.1016/j.jocs.2018.02.004
[18]	J. Chen, F. Ling, Y. Zhang, T. You, Y. Liu, X. Du, Coverage path planning of heterogeneous unmanned aerial vehicles based on ant colony system, Swarm Evol. Comput., 69 (2022), 101005. https://doi.org/10.1016/j.swevo.2021.101005 doi: 10.1016/j.swevo.2021.101005
[19]	S. Kumar, D. R. Parhi, M. K. Muni, K. K. Pandey, Optimal path search and control of mobile robot using hybridized sine-cosine algorithm and ant colony optimization technique, Ind. Rob., 47 (2020), 535-545. https://doi.org/10.1108/IR-12-2019-0248 doi: 10.1108/IR-12-2019-0248
[20]	W. Gao, Q. Tang, B. Ye, Y. Yang, J. Yao, An enhanced heuristic ant colony optimization for mobile robot path planning, Soft Comput., 24 (2020), 6139-6150. https://doi.org/10.1007/s00500-020-04749-3 doi: 10.1007/s00500-020-04749-3
[21]	Z. Zhou, J. Wang, Z. Zhu, D. Yang, J. Wu, Tangent navigated robot path planning strategyusing particle swarm optimized artificial potential field, Optik, 158 (2018), 639-651. https://doi.org/10.1016/j.ijleo.2017.12.169 doi: 10.1016/j.ijleo.2017.12.169
[22]	E. J. Molinos, A. Llamazares, M. Ocaña, Dynamic window based approaches for avoiding obstacles in moving, Rob. Auton. Syst., 118 (2019), 112-130. https://doi.org/10.1016/j.robot.2019.05.003 doi: 10.1016/j.robot.2019.05.003
[23]	X. Bai, H. Jiang, J. Cui, K. Lu, P. Chen, M. Zhang, UAV path planning based on improved A* and DWA algorithms, Int. J. Aerosp. Eng., 2021 (2021). https://doi.org/10.1155/2021/4511252 doi: 10.1155/2021/4511252
[24]	X. Ji, S. Feng, Q. Han, H. Yin, S. Yu, Improvement and fusion of A* algorithm and dynamic window approach considering complex environmental information, Arab. J. Sci. Eng., 46 (2021), 7445-7459. https://doi.org/10.1007/s13369-021-05445-6 doi: 10.1007/s13369-021-05445-6
[25]	Z. Lin, M. Yue, G. Chen, J. Sun, Path planning of mobile robot with PSO-based APF and fuzzy-based DWA subject to moving obstacles, Trans. Inst. Meas. Control., 44 (2022), 121-132. https://doi.org/10.1177/01423312211024798 doi: 10.1177/01423312211024798
[26]	X. Li, F. Liu, J. Liu, S. Liang, Obstacle avoidance for mobile robot based on improved dynamic window approach, Turk. J. Electr. Eng. Comput. Sci., 25 (2017), 666-676. https://doi.org/10.3906/elk-1504-194 doi: 10.3906/elk-1504-194
[27]	R. J. Mullen, D. Monekosso, S. Barman, P. Remagnino, A review of ant algorithms, Expert Syst. Appl., 36 (2009), 9608-9617. https://doi.org/10.1016/j.eswa.2009.01.020 doi: 10.1016/j.eswa.2009.01.020
[28]	Q. Luo, H. Wang, Y. Zheng, J. He, Research on path planning of mobile robot based on improved ant colony algorithm, Neural Comput. Appl., 32 (2020), 1555-1566. https://doi.org/10.1007/s00521-019-04172-2 doi: 10.1007/s00521-019-04172-2
[29]	W. Li, L. Xia, Y. Huang, S. Mahmoodi, An ant colony optimization algorithm with adaptive greedy strategy to optimize path problems, J. Ambient Intell. Hum. Comput., 13 (2022), 1557-1571. https://doi.org/10.1007/s12652-021-03120-0 doi: 10.1007/s12652-021-03120-0
[30]	S. Li, X. You, S. Liu, Co-evolutionary multi-colony ant colony optimization based on adaptive guidance mechanism and its application, Arabian J. Sci. Eng., 46 (2021), 9045-9063. https://doi.org/10.1007/s13369-021-05694-5 doi: 10.1007/s13369-021-05694-5
[31]	D. Lee, S. Lee, C. Ahn, C. Lim, Finite distribution estimation-based dynamic window approach to reliable obstacle avoidance of mobile robot, IEEE Trans. Ind. Electron., 68 (2020), 9998-10006. https://doi.org/10.1007/10.1109/TIE.2020.3020024 doi: 10.1007/10.1109/TIE.2020.3020024
[32]	L. Chang, L. Shan, C. Jiang, Y. Dai, Reinforcement based mobile robot path planning with improved dynamic window approach in unknown environment, Auton. Robot., 45 (2021), 51-76. https://doi.org/10.1007/s10514-020-09947-4 doi: 10.1007/s10514-020-09947-4
[33]	X. You, S. Liu, C. Zhang, An improved ant colony system algorithm for robot path planning and performance analysis, Int. J. Robot. Autom., 33 (2018), 527-533. https://doi.org/10.2316/Journal.206.2018.5.206-0071 doi: 10.2316/Journal.206.2018.5.206-0071
[34]	Z. Zhang, R. He, K. Yang, A bioinspired path planning approach for mobile robots based on improved sparrow search algorithm, Adv. Manuf., 10 (2022), 114-130. https://doi.org/10.1007/s40436-021-00366-x doi: 10.1007/s40436-021-00366-x
[35]	X. Chi, H. Li, J. Fei, Research on random obstacle avoidance method for robots based on the fusion of improved A~* algorithm and dynamic window method, Chin. J. Sci. Instrum., 42 (2021), 132-140. https://doi.org/10.19650/j.cnki.cjsi.J2007064 doi: 10.19650/j.cnki.cjsi.J2007064

Reader Comments

your name:*

Email:*
© 2022 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)