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Dao TK, Ngo TG, Pan JS, Nguyen TTT, Nguyen TT. Enhancing Path Planning Capabilities of Automated Guided Vehicles in Dynamic Environments: Multi-Objective PSO and Dynamic-Window Approach. Biomimetics (Basel) 2024; 9:35. [PMID: 38248609 PMCID: PMC10813721 DOI: 10.3390/biomimetics9010035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/20/2023] [Accepted: 11/21/2023] [Indexed: 01/23/2024] Open
Abstract
Automated guided vehicles (AGVs) are vital for optimizing the transport of material in modern industry. AGVs have been widely used in production, logistics, transportation, and commerce, enhancing productivity, lowering labor costs, improving energy efficiency, and ensuring safety. However, path planning for AGVs in complex and dynamic environments remains challenging due to the computation of obstacle avoidance and efficient transport. This study proposes a novel approach that combines multi-objective particle swarm optimization (MOPSO) and the dynamic-window approach (DWA) to enhance AGV path planning. Optimal AGV trajectories considering energy consumption, travel time, and collision avoidance were used to model the multi-objective functions for dealing with the outcome-feasible optimal solution. Empirical findings and results demonstrate the approach's effectiveness and efficiency, highlighting its potential for improving AGV navigation in real-world scenarios.
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Affiliation(s)
- Thi-Kien Dao
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350118, China;
- School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China
- Multimedia Communications Laboratory, University of Information Technology, Ho Chi Minh City 700000, Vietnam
- Vietnam National University, Ho Chi Minh City 700000, Vietnam
| | - Truong-Giang Ngo
- Faculty of Computer Science and Engineering, Thuyloi University, Hanoi 116705, Vietnam
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266510, China;
| | - Thi-Thanh-Tan Nguyen
- Faculty of Information Technology, Electric Power University, Hanoi 100000, Vietnam;
| | - Trong-The Nguyen
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350118, China;
- School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China
- Multimedia Communications Laboratory, University of Information Technology, Ho Chi Minh City 700000, Vietnam
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Dao TK, Chu SC, Nguyen TT, Nguyen TD, Nguyen VT. An Optimal WSN Node Coverage Based on Enhanced Archimedes Optimization Algorithm. Entropy 2022; 24:e24081018. [PMID: 35892997 PMCID: PMC9329719 DOI: 10.3390/e24081018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/15/2022] [Accepted: 07/19/2022] [Indexed: 01/27/2023]
Abstract
Node coverage is one of the crucial metrics for wireless sensor networks’ (WSNs’) quality of service, directly affecting the target monitoring area’s monitoring capacity. Pursuit of the optimal node coverage encounters increasing difficulties because of the limited computational power of individual nodes, the scale of the network, and the operating environment’s complexity and constant change. This paper proposes a solution to the optimal node coverage of unbalanced WSN distribution during random deployment based on an enhanced Archimedes optimization algorithm (EAOA). The best findings for network coverage from several sub-areas are combined using the EAOA. In order to address the shortcomings of the original Archimedes optimization algorithm (AOA) in handling complicated scenarios, we suggest an EAOA based on the AOA by adapting its equations with reverse learning and multidirection techniques. The obtained results from testing the benchmark function and the optimal WSN node coverage of the EAOA are compared with the other algorithms in the literature. The results show that the EAOA algorithm performs effectively, increasing the feasible range and convergence speed.
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Affiliation(s)
- Thi-Kien Dao
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350118, China;
| | - Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China;
| | - Trong-The Nguyen
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350118, China;
- University of Information Technology, Ho Chi Minh City 700000, Vietnam; (T.-D.N.); (V.-T.N.)
- Vietnam National University, Ho Chi Minh City 700000, Vietnam
- Correspondence:
| | - Trinh-Dong Nguyen
- University of Information Technology, Ho Chi Minh City 700000, Vietnam; (T.-D.N.); (V.-T.N.)
- Vietnam National University, Ho Chi Minh City 700000, Vietnam
| | - Vinh-Tiep Nguyen
- University of Information Technology, Ho Chi Minh City 700000, Vietnam; (T.-D.N.); (V.-T.N.)
- Vietnam National University, Ho Chi Minh City 700000, Vietnam
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Zhang SM, Gao SB, Dao TK, Huang DG, Wang J, Yao HW, Alfarraj O, Tolba A. An Analysis Scheme of Balancing Energy Consumption with Mobile Velocity Control Strategy for Wireless Rechargeable Sensor Networks. Sensors (Basel) 2020; 20:s20164494. [PMID: 32796687 PMCID: PMC7472165 DOI: 10.3390/s20164494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 08/06/2020] [Accepted: 08/08/2020] [Indexed: 06/11/2023]
Abstract
Wireless Rechargeable Sensor Networks (WRSN) are not yet fully functional and robust due to the fact that their setting parameters assume fixed control velocity and location. This study proposes a novel scheme of the WRSN with mobile sink (MS) velocity control strategies for charging nodes and collecting its data in WRSN. Strip space of the deployed network area is divided into sub-locations for variant corresponding velocities based on nodes energy expenditure demands. The points of consumed energy bottleneck nodes in sub-locations are determined based on gathering data of residual energy and expenditure of nodes. A minimum reliable energy balanced spanning tree is constructed based on data collection to optimize the data transmission paths, balance energy consumption, and reduce data loss during transmission. Experimental results are compared with the other methods in the literature that show that the proposed scheme offers a more effective alternative in reducing the network packet loss rate, balancing the nodes' energy consumption, and charging capacity of the nodes than the competitors.
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Affiliation(s)
- Shun-Miao Zhang
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 3500118, China
| | - Sheng-Bo Gao
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 3500118, China
| | - Thi-Kien Dao
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 3500118, China
| | - De-Gen Huang
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Jin Wang
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 3500118, China
- School of Computer & Communication Engineering, Changsha University of Science & Technology, Changsha 410000, China
| | - Hong-Wei Yao
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 3500118, China
| | - Osama Alfarraj
- Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia
| | - Amr Tolba
- Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia
- Mathematics and Computer Science Department, Faculty of Science, Menoufia University, Shebin-El-Kom 32511, Egypt
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Affiliation(s)
- Trong-The Nguyen
- Intelligent Information Processing Research Center, Fujian University of Technology, Fuzhou, Fujian, China
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fujian, China
- Department of Information Technology, University of Manage and Technology, Haiphong, Vietnam
| | - Yu Qiao
- Intelligent Information Processing Research Center, Fujian University of Technology, Fuzhou, Fujian, China
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fujian, China
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Kuo-Chi Chang
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fujian, China
| | - Xingsi Xue
- Intelligent Information Processing Research Center, Fujian University of Technology, Fuzhou, Fujian, China
| | - Thi-Kien Dao
- Intelligent Information Processing Research Center, Fujian University of Technology, Fuzhou, Fujian, China
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fujian, China
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Pan TS, Dao TK, Nguyen TT, Chu SC. Hybrid Particle Swarm Optimization with Bat Algorithm. Advances in Intelligent Systems and Computing 2015. [DOI: 10.1007/978-3-319-12286-1_5] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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Dao TK, Bell RC, Feng J, Jameson DM, Lipton JM. C-reactive protein, leukocytes, and fever after central IL 1 and alpha-MSH in aged rabbits. Am J Physiol 1988; 254:R401-9. [PMID: 2831740 DOI: 10.1152/ajpregu.1988.254.3.r401] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Aged and young unanesthetized rabbits with intracerebroventricular cannulas were tested in experiments designed to determine whether increases in plasma C-reactive protein (CRP) level and leukocytosis can be rapidly induced by central administration of crude buffy-coat supernatant commonly called endogenous pyrogen or interleukin 1 (IL 1). The results indicate that both acute-phase responses occur during fever caused by central administration of this supernatant and that they are generally detectable within 2 h. Although the febrile response was smaller in aged female rabbits, there was no decline in CRP or leukocyte responses, an observation that was not predicted. The antipyretic neuropeptide alpha-melanocyte-stimulating hormone (alpha-MSH) reduced fever caused by central IL 1 more effectively in the aged rabbits. alpha-MSH likewise inhibited the CRP and leukocyte responses to central IL 1. The results confirm that CRP and leukocyte responses can be driven by a central IL 1 signal and further indicate that the response can occur rapidly, consistent with direct central nervous system control of the acute-phase responses. The findings indicate that the acute-phase responses depend in part on the age of the host and that the responses can be modulated by an endogenous central nervous system peptide with known antipyretic and immune modulatory properties.
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Affiliation(s)
- T K Dao
- Department of Physiology, University of Texas Health Science Center, Dallas 75235
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