Path planning for mobile robots in complex environments based on improved ant colony algorithm

Yuzhuo Shi; Huijie Zhang; Zhisheng Li; Kun Hao; Yonglei Liu; Lu Zhao; Yuzhuo Shi; Huijie Zhang; Zhisheng Li; Kun Hao; Yonglei Liu; Lu Zhao

doi:10.3934/mbe.2023695

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 9: 15568-15602. doi: 10.3934/mbe.2023695

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Research article Special Issues

Path planning for mobile robots in complex environments based on improved ant colony algorithm

1.
College of Information Technology, Tianjin College of Commerce, Tianjin 300350, China
2.
School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, China

Academic Editor: Yang Kuang

Received: 29 April 2023 Revised: 28 June 2023 Accepted: 20 July 2023 Published: 27 July 2023

Aiming at the problems of the basic ant colony algorithm in path planning, such as long convergence time, poor global path quality and not being suitable for dynamic environments and unknown environments, this paper proposes a path planning method for mobile robots in complex environments based on an improved ant colony (CBIACO) algorithm. First, a new probability transfer function is designed for an ant colony algorithm, the weights of each component in the function are adaptively adjusted to optimize the convergence speed of the algorithm, and the global path is re-optimized by using the detection and optimization mechanism of diagonal obstacles. Second, a new unknown environment path exploration strategy (UPES) is designed to solve the problem of poor path exploration ability of the ant colony algorithm in unknown environment. Finally, a collision classification model is proposed for a dynamic environment, and the corresponding dynamic obstacle avoidance strategy is given. The experimental results show that CBIACO algorithm can not only rapidly generate high-quality global paths in known environments but also enable mobile robots to reach the specified target points safely and quickly in a variety of unknown environments. The new dynamic obstacle avoidance strategy enables the mobile robot to avoid dynamic obstacles in different directions at a lower cost.
- path planning,
- ant colony algorithm,
- unknown environment,
- path exploration,
- dynamic obstacle avoidance
Citation: Yuzhuo Shi, Huijie Zhang, Zhisheng Li, Kun Hao, Yonglei Liu, Lu Zhao. Path planning for mobile robots in complex environments based on improved ant colony algorithm[J]. Mathematical Biosciences and Engineering, 2023, 20(9): 15568-15602. doi: 10.3934/mbe.2023695

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Abstract

Aiming at the problems of the basic ant colony algorithm in path planning, such as long convergence time, poor global path quality and not being suitable for dynamic environments and unknown environments, this paper proposes a path planning method for mobile robots in complex environments based on an improved ant colony (CBIACO) algorithm. First, a new probability transfer function is designed for an ant colony algorithm, the weights of each component in the function are adaptively adjusted to optimize the convergence speed of the algorithm, and the global path is re-optimized by using the detection and optimization mechanism of diagonal obstacles. Second, a new unknown environment path exploration strategy (UPES) is designed to solve the problem of poor path exploration ability of the ant colony algorithm in unknown environment. Finally, a collision classification model is proposed for a dynamic environment, and the corresponding dynamic obstacle avoidance strategy is given. The experimental results show that CBIACO algorithm can not only rapidly generate high-quality global paths in known environments but also enable mobile robots to reach the specified target points safely and quickly in a variety of unknown environments. The new dynamic obstacle avoidance strategy enables the mobile robot to avoid dynamic obstacles in different directions at a lower cost.

References

[1]	T. Ort, I. Gilitschenski, D. Rus, Autonomous navigation in inclement weather based on a localizing ground penetrating radar, IEEE Rob. Autom. Lett., 5 (2020), 3267–3274. https://doi.org/10.1109/LRA.2020.2976310 doi: 10.1109/LRA.2020.2976310
[2]	H. Nam, Data-gathering protocol-based AUV path-planning for long-duration cooperation in underwater acoustic sensor networks, IEEE Sens. J., 18 (2018), 8902–8912. https://doi.org/10.1109/JSEN.2018.2866837 doi: 10.1109/JSEN.2018.2866837
[3]	Z. Huang, C. Chen, M. Pan, Multiobjective UAV path planning for emergency information collection and transmission, IEEE Internet Things J., 7 (2020), 6993–7009. https://doi.org/10.1109/JIOT.2020.2979521 doi: 10.1109/JIOT.2020.2979521
[4]	J. Han, Y. Seo, Mobile robot path planning with surrounding point set and path improvement, Appl. Soft Comput., 57 (2017), 35–47. https://doi.org/10.1016/j.asoc.2017.03.035 doi: 10.1016/j.asoc.2017.03.035
[5]	P. Sudhakara, V. Ganapathy, B. Priyadharshini, K. Sundaran, obstacle avoidance and navigation planning of a wheeled mobile robot using amended artificial potential field method, Procedia Comput. Sci., 133 (2018), 998–1004. https://doi.org/10.1016/j.procs.2018.07.076 doi: 10.1016/j.procs.2018.07.076
[6]	R. Fareh, M. Baziyad, T. Rabie, M. Bettayeb, Enhancing path quality of real-time path planning algorithms for mobile robots: A sequential linear paths approach, IEEE Access, 8 (2020), 167090–167104. https://doi.org/10.1109/ACCESS.2020.3016525 doi: 10.1109/ACCESS.2020.3016525
[7]	J. Song, C. Hao, J. Su, Path planning for unmanned surface vehicle based on predictive artificial potential field, Int. J. Adv. Rob. Syst., 17 (2020), 1–13. https://doi.org/10.1177/1729881420918461 doi: 10.1177/1729881420918461
[8]	S. Katoch, S. S. Chauhan, V. Kumar, A review on genetic algorithm: past, present, and future, Multimedia Tools Appl., 80 (2021), 8091–8126. https://doi.org/10.1007/s11042-020-10139-6 doi: 10.1007/s11042-020-10139-6
[9]	F. Wang, H. Zhang, A. Zhou, A particle swarm optimization algorithm for mixed-variableoptimization problems, Swarm Evol. Comput., 60 (2021), 1–36. https://doi.org/10.1016/j.swevo.2020.100808 doi: 10.1016/j.swevo.2020.100808
[10]	S. Gao, Y. Ding, B. M. Chen, A frontier-based coverage path planning algorithm for robot exploration in unknown environment, in 2020 39th Chinese Control Conference (CCC), IEEE, (2020), 3920–3925. https://doi.org/10.23919/CCC50068.2020.9188784
[11]	N. Yu, S. Wang, C. Xu, RGB-D based autonomous exploration and mapping of a mobile robot in unknown indoor environment, Robot, 39 (2017), 860–871. https://doi.org/10.13973/j.cnki.robot.2017.0860 doi: 10.13973/j.cnki.robot.2017.0860
[12]	X. Lan, X. Lv, W. Liu, Y. He, X. Zhang, Research on robot global path planning based on improved A-star ant colony algorithm, in 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), IEEE, (2021), 613–617. https://doi.org/10.1109/IAEAC50856.2021.9391099
[13]	L. Meng, X. You, S. Liu, Multi-colony collaborative ant optimization algorithm based on cooperative game mechanism, IEEE Access, 8 (2020), 154153–154165. https://doi.org/10.1109/ACCESS.2020.3011936 doi: 10.1109/ACCESS.2020.3011936
[14]	S. Biswas, S. G. Anavatti, M. A. Garratt, A particle swarm optimization based path planning method for autonomous systems in unknown terrain, in 2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), IEEE, (2019), 57–63. https://doi.org/10.1109/ICIAICT.2019.8784851
[15]	K. Wu, H. Wang, M. A. Esfahani, S. Yuan, Achieving real-time path planning in unknown environments through deep neural networks, IEEE Trans. Intell. Transp. Syst., 23 (2022), 2093–2102. https://doi.org/10.1109/TITS.2020.3031962 doi: 10.1109/TITS.2020.3031962
[16]	J. S. Willners, D. Gonzalez-Adell, J. D. Hernández, È. Pairet, Y. Petillot, Online 3-dimensional path planning with kinematic constraints in unknown environments using hybrid A* with tree pruning, Sensors, 21 (2021), 1–20. https://doi.org/10.3390/s21041152 doi: 10.3390/s21041152
[17]	Z. Jia, P. Lin, J. Liu, L. Liang, Online cooperative path planning for multi-quadrotors in an unknown dynamic environment, Proc. Inst. Mech. Eng., Part G: J. Aerosp. Eng., 236 (2022), 567–582. https://doi.org/10.1177/09544100211016615 doi: 10.1177/09544100211016615
[18]	A. Q. Faridi, S. Sharma, A. Shukla, R. Tiwari, J. Dhar, Multi-robot multi-target dynamic path planning using artificial bee colony and evolutionary programming in unknown environment, Intell. Serv. Rob., 11 (2018), 171–186. https://doi.org/10.1007/s11370-017-0244-7 doi: 10.1007/s11370-017-0244-7
[19]	H. Huang, G. Tan, L. Jiang, Robot path planning using improved ant colony algorithm in the environment of internet of things, J. Rob., 2022 (2022), 1–8. https://doi.org/10.1155/2022/1739884 doi: 10.1155/2022/1739884
[20]	Q. Jin, C. Tang, W. Cai, Research on dynamic path planning based on the fusion algorithm of improved ant colony optimization and rolling window method, IEEE Access, 10 (2020), 28322–28332. https://doi.org/10.1109/ACCESS.2021.3064831 doi: 10.1109/ACCESS.2021.3064831
[21]	K. Hao, H. Zhang, Z. Li, Y. Liu, Path planning of mobile robot based on improved obstacle avoidance strategy and double optimization ant colony algorithm, Trans. Chin. Soc. Agric. Mach., 53 (2022), 303–312,422.
[22]	K. Hao, J. Zhao, K. Yu, C. Li, C. Wang, Path planning of mobile robots based on a multi-population migration genetic algorithm, Sensors, 20 (2020), 1–23. https://doi.org/10.3390/s20205873 doi: 10.3390/s20205873
[23]	K. Hao, J. Zhao, B. Wang, Y. Liu, C. Wang, The application of an adaptive genetic algorithm based on collision detection in path planning of mobile robots, Comput. Intell. Neurosci., 2021 (2021), 1–20. https://doi.org/10.1155/2021/5536574 doi: 10.1155/2021/5536574
[24]	M. Kulich, J. J. Miranda-Bront, L. Preucil, A meta-heuristic based goal-selection strategy for mobile robot search in an unknown environment, Comput. Oper. Res., 84 (2017), 178–187. https://doi.org/10.1016/j.cor.2016.04.029 doi: 10.1016/j.cor.2016.04.029
[25]	W. Yue, Design of Independent System for Map Construction of Robots, Master thesis, Shanghai Jiao Tong University, 2020. https://doi.org/10.27307/d.cnki.gsjtu.2020.001039
[26]	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), 1–10. https://doi.org/10.1016/j.cie.2021.107230 doi: 10.1016/j.cie.2021.107230
[27]	H. Zhang, X. Gan, S. Li, Z. Chen, UAV safe route planning based on PSO-BAS algorithm, J. Syst. Eng. Electron., 33 (2022), 1151–1160. https://doi.org/10.23919/JSEE.2022.000111 doi: 10.23919/JSEE.2022.000111
[28]	S. Ma, K. Feng, J. Li, H. Wang, G. Cong, J. Huai, Proxies for shortest path and distance queries, IEEE Trans. Knowl. Data Eng., 28 (2016), 1835–1850. https://doi.org/10.1109/TKDE.2016.2531667 doi: 10.1109/TKDE.2016.2531667
[29]	Y. Zhang, S. Li, Distributed biased min-consensus with applications to shortest path planning, IEEE Trans. Autom. Control, 62 (2017), 5429–5436. https://doi.org/10.1109/TAC.2017.2694547 doi: 10.1109/TAC.2017.2694547

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