The significance of discrete neural models lies in their mathematical simplicity and computational ease. This research focuses on enhancing a neural map model by incorporating a hyperbolic tangent-based memristor. The study extensively explores the impact of magnetic induction strength on the model's dynamics, analyzing bifurcation diagrams and the presence of multistability. Moreover, the investigation extends to the collective behavior of coupled memristive neural maps with electrical, chemical, and magnetic connections. The synchronization of these coupled memristive maps is examined, revealing that chemical coupling exhibits a broader synchronization area. Additionally, diverse chimera states and cluster synchronized states are identified and discussed.
Citation: Prasina Alexander, Fatemeh Parastesh, Ibrahim Ismael Hamarash, Anitha Karthikeyan, Sajad Jafari, Shaobo He. Effect of the electromagnetic induction on a modified memristive neural map model[J]. Mathematical Biosciences and Engineering, 2023, 20(10): 17849-17865. doi: 10.3934/mbe.2023793
[1] | Baoye Song, Shumin Tang, Yao Li . A new path planning strategy integrating improved ACO and DWA algorithms for mobile robots in dynamic environments. Mathematical Biosciences and Engineering, 2024, 21(2): 2189-2211. doi: 10.3934/mbe.2024096 |
[2] | Jian Si, Xiaoguang Bao . A novel parallel ant colony optimization algorithm for mobile robot path planning. Mathematical Biosciences and Engineering, 2024, 21(2): 2568-2586. doi: 10.3934/mbe.2024113 |
[3] | 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. Mathematical Biosciences and Engineering, 2023, 20(9): 15568-15602. doi: 10.3934/mbe.2023695 |
[4] | Zhen Yang, Junli Li, Liwei Yang, Qian Wang, Ping Li, Guofeng Xia . Path planning and collision avoidance methods for distributed multi-robot systems in complex dynamic environments. Mathematical Biosciences and Engineering, 2023, 20(1): 145-178. doi: 10.3934/mbe.2023008 |
[5] | Tian Xue, Liu Li, Liu Shuang, Du Zhiping, Pang Ming . Path planning of mobile robot based on improved ant colony algorithm for logistics. Mathematical Biosciences and Engineering, 2021, 18(4): 3034-3045. doi: 10.3934/mbe.2021152 |
[6] | Xuewu Wang, Bin Tang, Xin Zhou, Xingsheng Gu . Double-robot obstacle avoidance path optimization for welding process. Mathematical Biosciences and Engineering, 2019, 16(5): 5697-5708. doi: 10.3934/mbe.2019284 |
[7] | Zhenao Yu, Peng Duan, Leilei Meng, Yuyan Han, Fan Ye . Multi-objective path planning for mobile robot with an improved artificial bee colony algorithm. Mathematical Biosciences and Engineering, 2023, 20(2): 2501-2529. doi: 10.3934/mbe.2023117 |
[8] | Ping Li, Liwei Yang . Conflict-free and energy-efficient path planning for multi-robots based on priority free ant colony optimization. Mathematical Biosciences and Engineering, 2023, 20(2): 3528-3565. doi: 10.3934/mbe.2023165 |
[9] | Jinzhuang Xiao, Xuele Yu, Keke Sun, Zhen Zhou, Gang Zhou . Multiobjective path optimization of an indoor AGV based on an improved ACO-DWA. Mathematical Biosciences and Engineering, 2022, 19(12): 12532-12557. doi: 10.3934/mbe.2022585 |
[10] | Chikun Gong, Yuhang Yang, Lipeng Yuan, Jiaxin Wang . An improved ant colony algorithm for integrating global path planning and local obstacle avoidance for mobile robot in dynamic environment. Mathematical Biosciences and Engineering, 2022, 19(12): 12405-12426. doi: 10.3934/mbe.2022579 |
The significance of discrete neural models lies in their mathematical simplicity and computational ease. This research focuses on enhancing a neural map model by incorporating a hyperbolic tangent-based memristor. The study extensively explores the impact of magnetic induction strength on the model's dynamics, analyzing bifurcation diagrams and the presence of multistability. Moreover, the investigation extends to the collective behavior of coupled memristive neural maps with electrical, chemical, and magnetic connections. The synchronization of these coupled memristive maps is examined, revealing that chemical coupling exhibits a broader synchronization area. Additionally, diverse chimera states and cluster synchronized states are identified and discussed.
[1] |
M. Courbage, V. I. Nekorkin, Map based models in neurodynamics, Int. J. Bifurcation Chaos, 20 (2010), 1631–1651. https://doi.org/10.1142/S0218127410026733 doi: 10.1142/S0218127410026733
![]() |
[2] |
B. Ibarz, J. M. Casado, M. A. Sanjuán, Map-based models in neuronal dynamics, Phys. Rep., 501 (2011), 1–74. https://doi.org/10.1016/j.physrep.2010.12.003 doi: 10.1016/j.physrep.2010.12.003
![]() |
[3] |
A. Holko, M. Mȩdrek, Z. Pastuszak, K. Phusavat, Epidemiological modeling with a population density map-based cellular automata simulation system, Expert Syst. Appl., 48 (2016), 1–8. https://doi.org/10.1016/j.eswa.2015.08.018 doi: 10.1016/j.eswa.2015.08.018
![]() |
[4] |
V. V. Tarasova, V. E. Tarasov, Logistic map with memory from economic model, Chaos Solitons Fractals, 95 (2017), 84–91. https://doi.org/10.1016/j.chaos.2016.12.012 doi: 10.1016/j.chaos.2016.12.012
![]() |
[5] |
K. Tanaka, T. Kinkyo, S. Hamori, Financial hazard map: Financial vulnerability predicted by a random forests classification model, Sustainability, 10 (2018), 1530. https://doi.org/10.3390/su10051530 doi: 10.3390/su10051530
![]() |
[6] |
Z. Hua, Y. Zhou, Exponential chaotic model for generating robust chaos, IEEE Trans. Syst. Man Cybern., 51 (2019), 3713–3724. https://doi.org/10.1109/TSMC.2019.2932616 doi: 10.1109/TSMC.2019.2932616
![]() |
[7] | M. Ausloos, The Logistic Map and the Route to Chaos: From the Beginnings to Modern Applications, Springer Science & Business Media, 2006. |
[8] |
B. Cessac, B. Doyon, M. Quoy, M. Samuelides, Mean-field equations, bifurcation map and route to chaos in discrete time neural networks, Physica D, 74 (1994), 24–44. https://doi.org/10.1016/0167-2789(94)90024-8 doi: 10.1016/0167-2789(94)90024-8
![]() |
[9] |
Q. Xu, T. Liu, S. Ding, H. Bao, Z. Li, B. Chen, Extreme multistability and phase synchronization in a heterogeneous bi-neuron Rulkov network with memristive electromagnetic induction, Cognit. Neurodyn., 17 (2023), 755–766. https://doi.org/10.1007/s11571-022-09866-3 doi: 10.1007/s11571-022-09866-3
![]() |
[10] |
I. Bashkirtseva, L. Ryashko, J. M. Seoane, M. A. Sanjuán, Noise-induced complex dynamics and synchronization in the map-based Chialvo neuron model, Commun. Nonlinear Sci. Numer. Simul., 116 (2023), 106867. https://doi.org/10.1016/j.cnsns.2022.106867 doi: 10.1016/j.cnsns.2022.106867
![]() |
[11] |
J. Sausedo-Solorio, A. Pisarchik, Synchronization of map-based neurons with memory and synaptic delay, Phys. Lett. A, 378 (2014), 2108–2112. https://doi.org/10.1016/j.physleta.2014.05.003 doi: 10.1016/j.physleta.2014.05.003
![]() |
[12] |
I. Franović, V. Miljković, Power law behavior related to mutual synchronization of chemically coupled map neurons, Eur. Phys. J. B, 76 (2010), 613–624. https://doi.org/10.1140/epjb/e2010-00205-4 doi: 10.1140/epjb/e2010-00205-4
![]() |
[13] |
Q. Xu, T. Liu, C. T. Feng, H. Bao, H. G. Wu, B. C. Bao, Continuous non-autonomous memristive Rulkov model with extreme multistability, Chin. Phys. B, 30 (2021), 128702. https://doi.org/10.1088/1674-1056/ac2f30 doi: 10.1088/1674-1056/ac2f30
![]() |
[14] |
S. He, K. Rajagopal, A. Karthikeyan, A. Srinivasan, A discrete Huber-Braun neuron model: From nodal properties to network performance, Cognit. Neurodyn., 17 (2023), 301–310. https://doi.org/10.1007/s11571-022-09806-1 doi: 10.1007/s11571-022-09806-1
![]() |
[15] |
F. Wang, H. Cao, Mode locking and quasiperiodicity in a discrete-time Chialvo neuron model, Commun. Nonlinear Sci. Numer. Simul., 56 (2018), 481–489. https://doi.org/10.1016/j.cnsns.2017.08.027 doi: 10.1016/j.cnsns.2017.08.027
![]() |
[16] | Y. Li, C. Li, Y. Zhao, S. Liu, Memristor-type chaotic mapping, Chaos, 32 (2022). https://doi.org/10.1063/5.0082983 |
[17] |
X. Zhang, C. Li, E. Dong, Y. Zhao, Z. Liu, A conservative memristive system with amplitude control and offset boosting, Int. J. Bifurcation Chaos, 32 (2022), 2250057. https://doi.org/10.1142/S0218127422500572 doi: 10.1142/S0218127422500572
![]() |
[18] |
Y. Jiang, C. Li, C. Zhang, Y. Zhao, H. Zang, A double-memristor hyperchaotic oscillator with complete amplitude control, IEEE Trans. Circuits Syst. I, 68 (2021), 4935–4944. https://doi.org/10.1109/TCSI.2021.3121499 doi: 10.1109/TCSI.2021.3121499
![]() |
[19] |
H. Jeong, L. Shi, Memristor devices for neural networks, J. Phys. D, 52 (2018), 023003. https://doi.org/10.1088/1361-6463/aae223 doi: 10.1088/1361-6463/aae223
![]() |
[20] |
D. B. Strukov, G. S. Snider, D. R. Stewart, R. S. Williams, The missing memristor found, Nature, 453 (2008), 80–83. https://doi.org/10.1038/nature06932 doi: 10.1038/nature06932
![]() |
[21] |
H. Wang, C. L. Li, S. Banerjee, S. B. He, Novel memristor and memristor-based applications, Eur. Phys. J. Spec. Top., 231 (2022), 2973–2977. https://doi.org/10.1140/epjs/s11734-022-00697-1 doi: 10.1140/epjs/s11734-022-00697-1
![]() |
[22] |
M. Ge, Y. Jia, Y. Xu, L. Yang, Mode transition in electrical activities of neuron driven by high and low frequency stimulus in the presence of electromagnetic induction and radiation, Nonlinear Dyn., 91 (2018), 515–523. https://doi.org/10.1007/s11071-017-3886-2 doi: 10.1007/s11071-017-3886-2
![]() |
[23] |
S. P. Adhikari, C. Yang, H. Kim, L. O. Chua, Memristor bridge synapse-based neural network and its learning, IEEE Trans. Neural Networks Learn. Syst., 23 (2012), 1426–1435. https://doi.org/10.1109/TNNLS.2012.2204770 doi: 10.1109/TNNLS.2012.2204770
![]() |
[24] |
J. C. Magee, C. Grienberger, Synaptic plasticity forms and functions, Ann. Rev. Neurosci., 43 (2020), 95–117. https://doi.org/10.1146/annurev-neuro-090919-022842 doi: 10.1146/annurev-neuro-090919-022842
![]() |
[25] |
C. Chen, F. Min, Y. Zhang, B. Bao, Memristive electromagnetic induction effects on Hopfield neural network, Nonlinear Dyn., 106 (2021), 2559–2576. https://doi.org/10.1007/s11071-021-06910-5 doi: 10.1007/s11071-021-06910-5
![]() |
[26] |
S. Hu, Y. Liu, Z. Liu, T. Chen, J. Wang, Q. Yu, et al., Associative memory realized by a reconfigurable memristive Hopfield neural network, Nat. Commun., 6 (2015), 7522. https://doi.org/10.1038/ncomms8522 doi: 10.1038/ncomms8522
![]() |
[27] |
K. Rajagopal, A. Karthikeyan, S. Jafari, F. Parastesh, C. Volos, I. Hussain, Wave propagation and spiral wave formation in a Hindmarsh-Rose neuron model with fractional-order threshold memristor synapse, Int. J. Mod. Phys. B, 34 (2020), 2050157. https://doi.org/10.1142/S021797922050157X doi: 10.1142/S021797922050157X
![]() |
[28] |
Q. Xu, Z. Ju, S. Ding, C. Feng, M. Chen, B. Bao, Electromagnetic induction effects on electrical activity within a memristive Wilson neuron model, Cognit. Neurodyn., 16 (2022), 1221–1231. https://doi.org/10.1007/s11571-021-09764-0 doi: 10.1007/s11571-021-09764-0
![]() |
[29] |
S. Qiao, C. Gao, Complex dynamics of a non-smooth temperature-sensitive memristive Wilson neuron model, Commun. Nonlinear Sci. Numer. Simul., 125 (2023), 107410. https://doi.org/10.1016/j.cnsns.2023.107410 doi: 10.1016/j.cnsns.2023.107410
![]() |
[30] |
C. Li, Y. Yang, X. Yang, Y. Lu, Application of discrete memristors in logistic map and Hindmarsh-Rose neuron, Eur. Phys. J. Spec. Top., 231 (2022), 3209–3224. https://doi.org/10.1140/epjs/s11734-022-00645-z doi: 10.1140/epjs/s11734-022-00645-z
![]() |
[31] |
Q. Lai, C. Lai, H. Zhang, C. Li, Hidden coexisting hyperchaos of new memristive neuron model and its application in image encryption, Chaos Solitons Fractals, 158 (2022), 112017. https://doi.org/10.1016/j.chaos.2022.112017 doi: 10.1016/j.chaos.2022.112017
![]() |
[32] |
C. Gao, S. Qiao, X. An, Global multistability and mechanisms of a memristive autapse-based Filippov Hindmash-Rose neuron model, Chaos Solitons Fractals, 160 (2022), 112281. https://doi.org/10.1016/j.chaos.2022.112281 doi: 10.1016/j.chaos.2022.112281
![]() |
[33] |
S. Qiao, C. Gao, X. An, Hidden dynamics and control of a Filippov memristive hybrid neuron model, Nonlinear Dyn., 111 (2023), 10529–10557. https://doi.org/10.1016/j.chaos.2022.112017 doi: 10.1016/j.chaos.2022.112017
![]() |
[34] |
A. Arenas, A. Díaz-Guilera, J. Kurths, Y. Moreno, C. Zhou, Synchronization in complex networks, Phys. Rep., 469 (2008), 93–153. https://doi.org/10.1016/j.physrep.2008.09.002 doi: 10.1016/j.physrep.2008.09.002
![]() |
[35] |
Z. Wang, R. Ramamoorthy, X. Xi, H. Namazi, Synchronization of the neurons coupled with sequential developing electrical and chemical synapses, Math. Biosci. Eng., 19 (2022), 1877–1890. https://doi.org/10.3934/mbe.2022088 doi: 10.3934/mbe.2022088
![]() |
[36] |
S. N. Chowdhury, S. Majhi, M. Ozer, D. Ghosh, M. Perc, Synchronization to extreme events in moving agents, New J. Phys., 21 (2019), 073048. https://doi.org/10.1088/1367-2630/ab2a1f doi: 10.1088/1367-2630/ab2a1f
![]() |
[37] |
G. Vivekanandhan, H. Natiq, Y. Merrikhi, K. Rajagopal, S. Jafari, Dynamical analysis and synchronization of a new memristive Chialvo neuron model, Electronics, 12 (2023), 545. https://doi.org/10.3390/electronics12030545 doi: 10.3390/electronics12030545
![]() |
[38] |
S. He, Complexity and chimera states in a ring-coupled fractional-order memristor neural network, Front. Appl. Math. Stat., 6 (2020), 24. https://doi.org/10.3389/fams.2020.00024 doi: 10.3389/fams.2020.00024
![]() |
[39] |
Z. Wang, H. Tian, O. Krejcar, H. Namazi, Synchronization in a network of map-based neurons with memristive synapse, Eur. Phys. J. Spec. Top., 231 (2022), 4057–4064. https://doi.org/10.1140/epjs/s11734-022-00691-7 doi: 10.1140/epjs/s11734-022-00691-7
![]() |
[40] |
F. Parastesh, S. Jafari, H. Azarnoush, Z. Shahriari, Z. Wang, S. Boccaletti, et al., Chimeras, Phys. Rep., 898 (2021), 1–114. https://doi.org/10.1016/j.physrep.2020.10.003 doi: 10.1016/j.physrep.2020.10.003
![]() |
[41] |
S. Majhi, B. K. Bera, D. Ghosh, M. Perc, Chimera states in neuronal networks: A review, Phys. Life Rev., 28 (2019), 100–121. https://doi.org/10.1016/j.plrev.2018.09.003 doi: 10.1016/j.plrev.2018.09.003
![]() |
[42] | I. Franović, K. Todorović, N. Vasović, N. Burić, Cluster synchronization of spiking induced by noise and interaction delays in homogenous neuronal ensembles, Chaos, 22 (2012). https://doi.org/10.1063/1.4753919 |
[43] |
I. Franović, K. Todorović, N. Vasović, N. Burić, Spontaneous formation of synchronization clusters in homogenous neuronal ensembles induced by noise and interaction delays, Phys. Rev. Lett., 108 (2012), 094101. https://doi.org/10.1103/PhysRevLett.108.094101 doi: 10.1103/PhysRevLett.108.094101
![]() |
[44] |
M. Mehrabbeik, F. Parastesh, J. Ramadoss, K. Rajagopal, H. Namazi, S. Jafari, Synchronization and chimera states in the network of electrochemically coupled memristive Rulkov neuron maps, Math. Biosci. Eng., 18 (2021), 9394–9409. https://doi.org/10.3934/mbe.2021462 doi: 10.3934/mbe.2021462
![]() |
[45] |
K. Li, B. Bao, J. Ma, M. Chen, H. Bao, Synchronization transitions in a discrete memristor-coupled bi-neuron model, Chaos Solitons Fractals, 165 (2022), 112861. https://doi.org/10.1016/j.chaos.2022.112861 doi: 10.1016/j.chaos.2022.112861
![]() |
[46] | B. Ramakrishnan, M. Mehrabbeik, F. Parastesh, K. Rajagopal, S. Jafari, A new memristive neuron map model and its network's dynamics under electrochemical coupling, Electronics, 11 (2022), 153. https://doi.org/10.3390/electronics11010153 |
[47] |
S. Wang, Z. Wei, Synchronization of coupled memristive Hindmarsh–Rose maps under different coupling conditions, AEU-Int. J. Electron. Commun., 161 (2023), 154561. https://doi.org/10.1016/j.aeue.2023.154561 doi: 10.1016/j.aeue.2023.154561
![]() |
[48] |
G. Baghdadi, S. Jafari, J. C. Sprott, F. Towhidkhah, M. H. Golpayegani, A chaotic model of sustaining attention problem in attention deficit disorder, Commun. Nonlinear Sci. Numer. Simul., 20 (2015), 174–185. https://doi.org/10.1016/j.cnsns.2014.05.015 doi: 10.1016/j.cnsns.2014.05.015
![]() |
[49] |
B. Bao, H. Qian, Q. Xu, M. Chen, J. Wang, Y. Yu, Coexisting behaviors of asymmetric attractors in hyperbolic-type memristor based Hopfield neural network, Front. Comput. Neurosci., 11 (2017), 81. https://doi.org/10.3389/fncom.2017.00081 doi: 10.3389/fncom.2017.00081
![]() |
[50] |
D. Ding, J. Luo, X. Shan, Y. Hu, Z. Yang, L. Ding, Coexisting behaviors of a fraction-order novel hyperbolic-type memristor Hopfield neuron network based on three neurons, Int. J. Mod. Phys. B, 34 (2020), 2050302. https://doi.org/10.1142/S0217979220503026 doi: 10.1142/S0217979220503026
![]() |
[51] |
R. Li, Z. Wang, E. Dong, A new locally active memristive synapse-coupled neuron model, Nonlinear Dyn., 104 (2021), 4459–4475. https://doi.org/10.1007/s11071-021-06574-1 doi: 10.1007/s11071-021-06574-1
![]() |
[52] |
K. Li, H. Bao, H. Li, J. Ma, Z. Hua, B. Bao, Memristive Rulkov neuron model with magnetic induction effects, IEEE Trans. Ind. Inf., 18 (2021), 1726–1736. https://doi.org/10.1109/TⅡ.2021.3086819 doi: 10.1109/TⅡ.2021.3086819
![]() |
[53] |
S. Garai, S. Karmakar, S. Jafari, N. Pal, Coexistence of triple, quadruple attractors and Wada basin boundaries in a predator-prey model with additional food for predators, Commun. Nonlinear Sci. Numer. Simul., 121 (2023), 107208. https://doi.org/10.1016/j.cnsns.2023.107208 doi: 10.1016/j.cnsns.2023.107208
![]() |
[54] |
Y. Zhou, J. Gao, K. D. White, I. Merk, K. Yao, Perceptual dominance time distributions in multistable visual perception, Biol. Cybern., 90 (2004), 256–263. https://doi.org/10.1007/s00422-004-0472-8 doi: 10.1007/s00422-004-0472-8
![]() |
[55] |
D. Durstewitz, G. Deco, Computational significance of transient dynamics in cortical networks, Eur. J. Neurosci., 27 (2008), 217–227. https://doi.org/10.1111/j.1460-9568.2007.05976.x doi: 10.1111/j.1460-9568.2007.05976.x
![]() |
[56] |
T. Ionescu, Exploring the nature of cognitive flexibility, New Ideas Psychol., 30 (2012), 190–200. https://doi.org/10.1016/j.newideapsych.2011.11.001 doi: 10.1016/j.newideapsych.2011.11.001
![]() |
[57] |
Y. Xu, Y. Jia, J. Ma, A. Alsaedi, B. Ahmad, Synchronization between neurons coupled by memristor, Chaos Solitons Fractals, 104 (2017), 435–442. https://doi.org/10.1016/j.chaos.2017.09.002 doi: 10.1016/j.chaos.2017.09.002
![]() |
[58] |
P. Zhou, Y. Xu, J. Ma, Dynamical and coherence resonance in a photoelectric neuron under autaptic regulation, Physica A, 620 (2023), 128746. https://doi.org/10.1016/j.physa.2023.128746 doi: 10.1016/j.physa.2023.128746
![]() |
[59] |
A. N. Pisarchik, A. E. Hramov, Coherence resonance in neural networks: Theory and experiments, Phys. Rep., 1000 (2023), 1–57. https://doi.org/10.1016/j.physrep.2022.11.004 doi: 10.1016/j.physrep.2022.11.004
![]() |
1. | Liwei Yang, Lixia Fu, Ping Li, Jianlin Mao, Ning Guo, An Effective Dynamic Path Planning Approach for Mobile Robots Based on Ant Colony Fusion Dynamic Windows, 2022, 10, 2075-1702, 50, 10.3390/machines10010050 | |
2. | Qian Wang, Junli Li, Liwei Yang, Zhen Yang, Ping Li, Guofeng Xia, Distributed Multi-Mobile Robot Path Planning and Obstacle Avoidance Based on ACO–DWA in Unknown Complex Terrain, 2022, 11, 2079-9292, 2144, 10.3390/electronics11142144 | |
3. | Pranshav Gajjar, Virensinh Dodia, Siddharth Mandaliya, Pooja Shah, Vijay Ukani, Madhu Shukla, 2022, Chapter 19, 978-3-031-23094-3, 262, 10.1007/978-3-031-23095-0_19 | |
4. | Xingcheng Pu, Xinlin Song, Ling Tan, Yi Zhang, Improved ant colony algorithm in path planning of a single robot and multi-robots with multi-objective, 2023, 1864-5909, 10.1007/s12065-023-00821-7 | |
5. | Xiaoling Meng, Xijing Zhu, Autonomous Obstacle Avoidance Path Planning for Grasping Manipulator Based on Elite Smoothing Ant Colony Algorithm, 2022, 14, 2073-8994, 1843, 10.3390/sym14091843 | |
6. | Sai Zhang, Li Tang, Yan-Jun Liu, Formation deployment control of multi-agent systems modeled with PDE, 2022, 19, 1551-0018, 13541, 10.3934/mbe.2022632 | |
7. | Jie Zhang, Xiuqin Pan, 2022, Chapter 1, 978-3-031-23584-9, 3, 10.1007/978-3-031-23585-6_1 | |
8. | Zhen Yang, Junli Li, Liwei Yang, Qian Wang, Ping Li, Guofeng Xia, Path planning and collision avoidance methods for distributed multi-robot systems in complex dynamic environments, 2022, 20, 1551-0018, 145, 10.3934/mbe.2023008 | |
9. | Nour Abujabal, Raouf Fareh, Saif Sinan, Mohammed Baziyad, Maamar Bettayeb, A comprehensive review of the latest path planning developments for multi-robot formation systems, 2023, 0263-5747, 1, 10.1017/S0263574723000322 | |
10. | Yiqi Xu, Qiongqiong Li, Xuan Xu, Jiafu Yang, Yong Chen, Research Progress of Nature-Inspired Metaheuristic Algorithms in Mobile Robot Path Planning, 2023, 12, 2079-9292, 3263, 10.3390/electronics12153263 | |
11. | Wenjie Ning, Li Ma, Zhichuang Wang, Fangyuan Hou, 2024, Chapter 33, 978-981-97-3327-9, 393, 10.1007/978-981-97-3328-6_33 | |
12. | Semonti Banik, Sajal Chandra Banik, Sarker Safat Mahmud, Path Planning Approaches in Multi‐robot System: A Review, 2024, 2577-8196, 10.1002/eng2.13035 | |
13. | Georgios Karamitsos, Dimitrios Bechtsis, Naoum Tsolakis, Dimitrios Vlachos, 2024, Chapter 5, 978-3-031-58918-8, 139, 10.1007/978-3-031-58919-5_5 | |
14. | Liwei Yang, Ping Li, Song Qian, He Quan, Jinchao Miao, Mengqi Liu, Yanpei Hu, Erexidin Memetimin, Path Planning Technique for Mobile Robots: A Review, 2023, 11, 2075-1702, 980, 10.3390/machines11100980 | |
15. | Bilal Gurevin, Furkan Gulturk, Muhammed Yildiz, Ihsan Pehlivan, Trung Thanh Nguyen, Fatih Caliskan, Baris Boru, Mustafa Zahid Yildiz, A Novel GUI Design for Comparison of ROS-Based Mobile Robot Local Planners, 2023, 11, 2169-3536, 125738, 10.1109/ACCESS.2023.3327705 | |
16. | Zhen Zhou, Chenchen Geng, Buhu Qi, Aiwen Meng, Jinzhuang Xiao, Research and experiment on global path planning for indoor AGV via improved ACO and fuzzy DWA, 2023, 20, 1551-0018, 19152, 10.3934/mbe.2023846 | |
17. | Mohammed Baziyad, Nour AbuJabal, Raouf Fareh, Tamer Rabie, Ibrahim Kamel, Maamar Bettayeb, 2023, A Direction for Swarm Robotic Path Planning Technique Using Potential Field Concepts and Particle Swarm Optimization, 979-8-3503-8239-6, 7, 10.1109/IIT59782.2023.10366467 | |
18. | Shuai Wu, Ani Dong, Qingxia Li, Wenhong Wei, Yuhui Zhang, Zijing Ye, Application of ant colony optimization algorithm based on farthest point optimization and multi-objective strategy in robot path planning, 2024, 167, 15684946, 112433, 10.1016/j.asoc.2024.112433 | |
19. | Yongrong Cai, Haibin Liu, Mingfei Li, Fujie Ren, A Method of Dual-AGV-Ganged Path Planning Based on the Genetic Algorithm, 2024, 14, 2076-3417, 7482, 10.3390/app14177482 | |
20. | Shuai Wu, Qingxia Li, Wenhong Wei, Zijing Ye, 2023, Research on Mobile Robot Path Planning in Angle-Guided Ant Colony Optimization Algorithm, 979-8-3503-0375-9, 7070, 10.1109/CAC59555.2023.10450803 | |
21. | Nour AbuJabal, Tamer Rabie, Mohammed Baziyad, Ibrahim Kamel, Khawla Almazrouei, Path Planning Techniques for Real-Time Multi-Robot Systems: A Systematic Review, 2024, 13, 2079-9292, 2239, 10.3390/electronics13122239 | |
22. | Nour Ayman Abujabal, Tamer Rabie, Ibrahim Kamel, 2023, Path Planning Techniques for Multi-robot Systems: A Systematic Review, 979-8-3503-8239-6, 1, 10.1109/IIT59782.2023.10366472 | |
23. | Cuicui Cai, Chaochuan Jia, Yao Nie, Jinhong Zhang, Ling Li, A path planning method using modified harris hawks optimization algorithm for mobile robots, 2023, 9, 2376-5992, e1473, 10.7717/peerj-cs.1473 | |
24. | Shuai Wu, Qingxia Li, Wenhong Wei, Application of Ant Colony Optimization Algorithm Based on Triangle Inequality Principle and Partition Method Strategy in Robot Path Planning, 2023, 12, 2075-1680, 525, 10.3390/axioms12060525 | |
25. | Meltem Eyuboglu, Gokhan Atali, A novel collaborative path planning algorithm for 3-wheel omnidirectional Autonomous Mobile Robot, 2023, 169, 09218890, 104527, 10.1016/j.robot.2023.104527 | |
26. | Wenteng Wang, 2024, Chapter 4, 978-981-97-3209-8, 39, 10.1007/978-981-97-3210-4_4 | |
27. | Haobo Feng, Qiao Hu, Zhenyi Zhao, Xinglong Feng, Chuan Jiang, A varied-width path planning method for multiple AUV formation, 2025, 199, 03608352, 110746, 10.1016/j.cie.2024.110746 | |
28. | Luis E. Ruiz-Fernandez, Javier Ruiz-Leon, David Gomez-Gutierrez, Rafael Murrieta-Cid, Decentralized multi-robot formation control in environments with non-convex and dynamic obstacles based on path planning algorithms, 2025, 1861-2776, 10.1007/s11370-024-00582-x | |
29. | Yong Li, Neng Long, 2024, Path Planning for Mobile Robots Based on the Improved Adaptive Ant Colony Algorithm, 979-8-3503-6860-4, 1761, 10.1109/CAC63892.2024.10865367 | |
30. | Wenyan Zhu, Wenzheng Cai, Hoiio Kong, Optimal Path Planning Based on ACO in Intelligent Transportation, 2025, 26663074, 10.1016/j.ijcce.2025.02.006 | |
31. | Huiliao Yang, Bo Zhang, Chang Xiao, 2025, Chapter 44, 978-981-96-2227-6, 470, 10.1007/978-981-96-2228-3_44 | |
32. | Guangping Qiu, Jizhong Deng, Jincan Li, Weixing Wang, Hybrid Clustering-Enhanced Brain Storm Optimization Algorithm for Efficient Multi-Robot Path Planning, 2025, 10, 2313-7673, 347, 10.3390/biomimetics10060347 |