The modular intelligent robot platform has important application prospects in the field of tourism management services. Based on the intelligent robot in the scenic area, this paper constructs a partial differential analysis system for tourism management services, and adopts the modular design method to complete the hardware design of the intelligent robot system. Through system analysis, the whole system is divided into 5 major modules, including core control module, power supply module, motor control module, sensor measurement module, wireless sensor network module, to solve the problem of quantification of tourism management services. In the simulation process, the hardware development of wireless sensor network node is carried out based on MSP430F169 microcontroller and CC2420 radio frequency wireless communication chip, and the corresponding physical layer and MAC (Media Access Control) layer data definition and data definition of IEEE802.15.4 protocol are completed for software implementation, and data transmission and networking verification. The experimental results show that the encoder resolution is 1024P/R, the power supply voltage is DC5V5%, and the maximum response frequency is 100 kHz. The algorithm designed by MATLAB software can avoid the existing shortcomings and meet the real-time requirements of the system, which significantly improves the sensitivity and robustness of the intelligent robot.
Citation: Tingting Yang, Yi He. Design of intelligent robots for tourism management service based on green computing[J]. Mathematical Biosciences and Engineering, 2023, 20(3): 4798-4815. doi: 10.3934/mbe.2023222
The modular intelligent robot platform has important application prospects in the field of tourism management services. Based on the intelligent robot in the scenic area, this paper constructs a partial differential analysis system for tourism management services, and adopts the modular design method to complete the hardware design of the intelligent robot system. Through system analysis, the whole system is divided into 5 major modules, including core control module, power supply module, motor control module, sensor measurement module, wireless sensor network module, to solve the problem of quantification of tourism management services. In the simulation process, the hardware development of wireless sensor network node is carried out based on MSP430F169 microcontroller and CC2420 radio frequency wireless communication chip, and the corresponding physical layer and MAC (Media Access Control) layer data definition and data definition of IEEE802.15.4 protocol are completed for software implementation, and data transmission and networking verification. The experimental results show that the encoder resolution is 1024P/R, the power supply voltage is DC5V5%, and the maximum response frequency is 100 kHz. The algorithm designed by MATLAB software can avoid the existing shortcomings and meet the real-time requirements of the system, which significantly improves the sensitivity and robustness of the intelligent robot.
[1] | Z. Guo, K. Yu, A. K. Bashir, D. Zhang, Y. D. Al-Otaibi, M. Guizani, Deep information fusion-driven POI scheduling for mobile social networks, IEEE Network, 36 (2022), 210–216. https://doi.org/10.1109/MNET.102.2100394 doi: 10.1109/MNET.102.2100394 |
[2] | Y. Li, H. Ma, L. Wang, S. Mao, G. Wang, Optimized content caching and user association for edge computing in densely deployed heterogeneous networks, IEEE Trans. Mob. Comput., 21 (2022), 2130–2142. https://doi.org/10.1109/TMC.2020.3033563 doi: 10.1109/TMC.2020.3033563 |
[3] | Q. Zhang, K. Yu, Z. Guo, S. Garg, J. J. P. C. Rodrigues, M. M. Hassan, et al., Graph neural networks-driven traffic forecasting for connected internet of vehicles, IEEE Trans. Network Sci. Eng., 9 (2022), 3015–3027. https://doi.org/10.1109/TNSE.2021.3126830 doi: 10.1109/TNSE.2021.3126830 |
[4] | S. Xia, Z. Yao, Y. Li, S. Mao, Online distributed offloading and computing resource management with energy harvesting for heterogeneous MEC-enabled IoT, IEEE Trans. Wireless Commun., 20 (2022), 6743–6757.https://doi.org/10.1109/TWC.2021.3076201 doi: 10.1109/TWC.2021.3076201 |
[5] | Z. Guo, K. Yu, Z. Lv, K. K. R. Choo, P. Shi, J. J. P. C. Rodrigues, Deep federated learning enhanced secure POI microservices for cyber-physical systems, IEEE Wireless Commun., 29 (2022), 22–29. https://doi.org/10.1109/MWC.002.210027 doi: 10.1109/MWC.002.210027 |
[6] | L. Zhao, Z. Yin, K. Yu, X. Tang, L. Xu, Z. Guo, et al., A fuzzy logic based intelligent multi-attribute routing scheme for two-layered SDVNs, IEEE Trans. Network Serv. Manage., 2022, early access, https://doi.org/10.1109/TNSM.2022.3202741 |
[7] | D. Peng, D. He, Y. Li, Z. Wang, Integrating terrestrial and satellite multibeam systems toward 6G: Techniques and challenges for interference mitigation, IEEE Wireless Commun., 29 (2022), 24–31. https://doi.org/10.1109/MWC.002.00293 doi: 10.1109/MWC.002.00293 |
[8] | L. Huang, R. Nan, K. Chi, Q. Hua, K. Yu, N. Kumar, et al., Throughput guarantees for multi-cell wireless powered communication networks with non-orthogonal multiple access, IEEE Trans. Veh. Technol., 71 (2022), 12104–12116. https://doi.org/10.1109/TVT.2022.3189699 doi: 10.1109/TVT.2022.3189699 |
[9] | Z. Cai, X. Zheng, J. Yu, A Differential-private framework for urban traffic flows estimation via taxi companies, IEEE Trans. Ind. Inf., 15 (2019), 6492–6499. https://doi.org/10.1109/TII.2019.2911697 doi: 10.1109/TII.2019.2911697 |
[10] | Z. Zhou, X. Dong, Z. Li, K. Yu, C. Ding, Y. Yang, Spatio-temporal feature encoding for traffic accident detection in VANET environment, IEEE Trans. Intell. Transp. Syst., 23 (2022), 19772–19781. https://doi.org/10.1109/TITS.2022.3147826 doi: 10.1109/TITS.2022.3147826 |
[11] | X. Zheng, Z. Cai, Privacy-preserved data sharing towards multiple parties in industrial IoTs, IEEE J. Sel. Areas Commun., 38 (2020), 968–979. https://doi.org/10.1109/JSAC.2020.2980802 doi: 10.1109/JSAC.2020.2980802 |
[12] | S. H. Ivanov, C. Webster, E. Stoilova, D. Slobodskoy, Biosecurity, crisis management, automation technologies and economic performance of travel, tourism and hospitality companies—A conceptual framework, Tourism Econ., 28 (2022), 3–26. https://doi.org/10.1177/13548166209465 doi: 10.1177/13548166209465 |
[13] | B. Hysa, A. Karasek, I. Zdonek, Social media usage by different generations as a tool for sustainable tourism marketing in society 5.0 idea, Sustainability, 13 (2021), 1018. https://doi.org/10.3390/su13031018 doi: 10.3390/su13031018 |
[14] | H. E. Arici, M. A. Köseoglu, A. Sökmen, The intellectual structure of customer experience research in service scholarship: a bibliometric analysis, Serv. Ind. J., 42 (2022), 514–550. https://doi.org/10.1080/02642069.2022.2043286 doi: 10.1080/02642069.2022.2043286 |
[15] | Y. Wu, Z. Huo, W. Xing, Z, Ma, H. M. A. Aldeeb, Application of experience economy and recommendation algorithm in tourism reuse of industrial wasteland, Appl. Math. Nonlinear Sci., 6 (2021), 227–238. https://doi.org/10.2478/amns.2021.2.00039 doi: 10.2478/amns.2021.2.00039 |
[16] | J. Reis, N. Melão, J. Salvadorinho, B. Soares, A. Rosetea, Service robots in the hospitality industry: The case of Henn-na hotel, Technol. Soc., 63 (2020), 101423. https://doi.org/10.1016/j.techsoc.2020.101423 doi: 10.1016/j.techsoc.2020.101423 |
[17] | R. Kervenoael, R. Hasan, A. Schwob, E. Goh, Leveraging human-robot interaction in hospitality services: Incorporating the role of perceived value, empathy, and information sharing into visitors' intentions to use social robots, Tourism Manage., 78 (2020), 104042. https://doi.org/10.1016/j.tourman.2019.104042 doi: 10.1016/j.tourman.2019.104042 |
[18] | H. Fan, W. Gao, B. Han, How does (im) balanced acceptance of robots between customers and frontline employees affect hotels' service quality, Comput. Human Behav., 133 (2022), 107287. https://doi.org/10.1016/j.chb.2022.107287 doi: 10.1016/j.chb.2022.107287 |
[19] | S. H. W. Chuah, E. C. X. Aw, C. F. Cheng, A silver lining in the COVID-19 cloud: Examining customers' value perceptions, willingness to use and pay more for robotic restaurants, J. Hosp. Market. Manage., 31 (2022), 49–76. https://doi.org/10.1080/19368623.2021.1926038 doi: 10.1080/19368623.2021.1926038 |
[20] | S. Ivanov, C. Webster, Willingness-to-pay for robot-delivered tourism and hospitality services–an exploratory study, Int. J. Contemp. Hosp. Manage., 33 (2021), 3926–3955. https://doi.org/10.1108/IJCHM-09-2020-1078 doi: 10.1108/IJCHM-09-2020-1078 |
[21] | A. Tuomi, I. P. Tussyadiah, J. Stienmetz, Applications and implications of service robots in hospitality, Cornell Hosp. Q., 62 (2021), 232–247. https://doi.org/10.1177/193896552092396 doi: 10.1177/193896552092396 |
[22] | S. M. C. Loureiro, R. G. Bilro, The role of commitment amongst tourists and intelligent virtual assistants, J. Promot. Manage., 28 (2022), 175–188. https://doi.org/10.1080/10496491.2021.1987979 doi: 10.1080/10496491.2021.1987979 |
[23] | S. Kabadayi, F. Ali, H. Choi, H. Joosten, C. Lu, Smart service experience in hospitality and tourism services: A conceptualization and future research agenda, J. Serv. Manage., 30 (2019), 326–348. https://doi.org/10.1108/JOSM-11-2018-0377 doi: 10.1108/JOSM-11-2018-0377 |
[24] | C. T. Zhang, L. P. Liu, Research on coordination mechanism in three-level green supply chain under non-cooperative game, Appl. Math. Modell., 37 (2013), 3369–3379. https://doi.org/10.1016/j.apm.2012.08.006 doi: 10.1016/j.apm.2012.08.006 |
[25] | F. Ruggiero, V. Lippiello, B.Siciliano, Nonprehensile dynamic manipulation: A survey, IEEE Robot. Autom. Let., 3 (2018), 1711–1718. https://doi.org/10.1109/LRA.2018.2801939 doi: 10.1109/LRA.2018.2801939 |
[26] | X. Zhang, T. Feng, Q. Niu, X. Deng, A novel swarm optimisation algorithm based on a mixed-distribution model, Appl. Sci., 8 (2018), 632. https://doi.org/10.3390/app8040632 doi: 10.3390/app8040632 |
[27] | X. Yuan, J. Shi, L. Gu, A review of deep learning methods for semantic segmentation of remote sensing imagery, Exp. Syst. Appl., 169 (2021), 114417. https://doi.org/10.1016/j.eswa.2020.114417 doi: 10.1016/j.eswa.2020.114417 |
[28] | V. Q. Trinh, N. Seetaram, Top-management compensation and survival likelihood: the case of tourism and leisure firms in the US, Ann. Tourism Res., 92 (2022), 103323. https://doi.org/10.1016/j.annals.2021.103323 doi: 10.1016/j.annals.2021.103323 |