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Spatial–Temporal Fusion Based Path Planning for Source Seeking in Wireless Sensor Network

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Abstract

Source seeking problem has been faced in many fields, especially in search and rescue applications. We proposed a virtual structure-based spatial–temporal method to realize cooperative source seeking using multi-agents. Spatially, a circular formation is considered to gather collaborative information and estimate the gradient direction of the formation center. In terms of temporal information, we use the formation positions in time sequence to construct a virtual structure sequence. Then, we fuse the sequential gradient as a whole. Experimental results show that, compared with state-of-the-art, the proposed method can quickly and efficiently find the source so that the formation can minimize the movement distance during the moving process and increase the efficiency of source seeking. Numerical simulations confirm the efficiency of the scheme put forth. Compared with state-of-the-art source-seeking methods, the iterative steps of our proposed method are reduced by 20%, indicating that the method can find the signal source with higher efficiency and lower energy consumption, and better robustness.

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Data Availability

All related simulation data will be made available upon reasonable request from the corresponding authors (C. Xu and S. Duan) for academic use and within the limitations of the provided informed consent by the corresponding author upon acceptance.

References

  1. D. Waleed et al., “An In-Pipe Leak Detection Robot With a Neural-Network-Based Leak Verification System,” in IEEE Sensors Journal, vol. 19, no. 3, pp. 1153-1165, 1 Feb.1, 2019.

  2. H. Che, C. Shi, X. Xu, J. Li and B. Wu, “Research on Improved ACO Algorithm-Based Multi-Robot Odor Source Localization,” 2018 2nd International Conference on Robotics and Automation Sciences (ICRAS), Wuhan, 2018, pp. 1-5.

  3. X. Cao, Z. Jin, C. Wang and M. Dong, “Kinematics simulation of environmental parameter monitor robot used in coalmine underground,” 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), Xi’an, 2016, pp. 576-581.

  4. Li, Zhuo, Keyou You, and Shiji Song. “Cooperative source seeking via networked multi-vehicle systems.” Automatica 115 (2020): 108853.

  5. M. Kanwar and L. Agilandeeswari, “IOT Based Fire Fighting Robot,” 2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, 2018, pp. 718-723.

  6. Consi, T. R., et al. “AUV guidance with chemical signals.” Proceedings of IEEE Symposium on Autonomous Underwater Vehicle Technology (AUV’94). IEEE, 1994.

  7. Chen, Xin-xing, and Jian Huang. “Odor source localization algorithms on mobile robots: A review and future outlook.” Robotics and Autonomous Systems 112 (2019): 123–136.

  8. Russell, R. Andrew, et al. “A comparison of reactive robot chemotaxis algorithms.” Robotics and Autonomous Systems 45.2 (2003): 83-97.

  9. M. Dorigo, V. Maniezzo and A. Colorni, “Ant system: optimization by a colony of cooperating agents,” in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 26, no. 1, pp. 29–41, Feb. 1996.

  10. D. Karaboga, An idea based on honeybee swarm for numerical optimization, Technical Report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.

  11. Cai, Y. and Y. Shen, An Integrated Localization and Control Framework for Multi-Agent Formation. IEEE Transactions on Signal Processing, 2019. 67(7): p. 1941–1956.

    Article  MathSciNet  Google Scholar 

  12. Neto, Mário Tasso Ribeiro Serra, Marco Antônio Florenzano Mollinetti, and Rodrigo Lisbôa Pereira. “Evolutionary artificial bee colony for neural networks training.” 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2017.

  13. W. Wu and F. Zhang, “A Speeding-Up and Slowing-Down Strategy for Distributed Source Seeking With Robustness Analysis,” in IEEE Transactions on Control of Network Systems, vol. 3, no. 3, pp. 231–240, Sept. 2016.

  14. S. Al-Abri, W. Wu and F. Zhang, “A Gradient-Free Three-Dimensional Source Seeking Strategy With Robustness Analysis,” in IEEE Transactions on Automatic Control, vol. 64, no. 8, pp. 3439–3446, Aug. 2019.

  15. J. Kennedy and R. Eberhart, “Particle swarm optimization,” Proceedings of ICNN’95 - International Conference on Neural Networks, Perth, WA, Australia, 1995, pp. 1942-1948 vol.4.

  16. Jatmiko, Wisnu, Kosuke Sekiyama, and Toshio Fukuda. “A pso-based mobile robot for odor source localization in dynamic advection-diffusion with obstacles environment: theory, simulation and measurement.” IEEE Computational Intelligence Magazine 2.2 (2007): 37–51.

  17. Jatmiko, Wisnu, et al. “Robots implementation for odor source localization using PSO algorithm.” WSEAS Transactions on Circuits and Systems 10.4 (2011): 115-125.

  18. Song, C., L. Liu and S. Xu, Circle Formation Control of Mobile Agents With Limited Interaction Range. IEEE Transactions on Automatic Control, 2019. 64(5): p. 2115–2121.

    Article  MathSciNet  Google Scholar 

  19. Yang, Linyu, and Shujun Liu. ”Distributed Stochastic Source Seeking for Multiple Vehicles over Fixed Topology.” Journal of Systems Science and Complexity 33.3 (2020): 652–671.

  20. Song, Cheng, et al. “Collaborative infotaxis: Searching for a signal-emitting source based on particle filter and Gaussian fitting.” Robotics and Autonomous Systems 125 (2020): 103414.

  21. N. Atanasov, J. Le Ny, N. Michael and G. J. Pappas, “Stochastic source seeking in complex environments,” 2012 IEEE International Conference on Robotics and Automation, Saint Paul, MN, 2012, pp. 3013-3018.

  22. P. Ögren, E. Fiorelli and N. E. Leonard, “Cooperative control of mobile sensor networks: Adaptive gradient climbing in a distributed environment,” in IEEE Transactions on Automatic Control, vol. 49, no. 8, pp. 1292–1302, Aug. 2004.

  23. Zhu S, Wang D, Low C B. Cooperative control of multiple UAVs for moving source seeking[J]. Journal of Intelligent & Robotic Systems, 2014, 74(1–2): 333–346.

    Article  Google Scholar 

  24. S. Li, R. Kong and Y. Guo, “Cooperative Distributed Source Seeking by Multiple Robots: Algorithms and Experiments,” in IEEE/ASME Transactions on Mechatronics, vol. 19, no. 6, pp. 1810–1820, Dec. 2014.

  25. R. Fabbiano, F. Garin and C. Canudas-de-Wit, “Distributed Source Seeking Without Global Position Information,” in IEEE Transactions on Control of Network Systems, vol. 5, no. 1, pp. 228–238, March 2018.

  26. Briñón-Arranz L, Seuret A, Pascoal A. Circular formation control for cooperative target tracking with limited information[J]. Journal of the Franklin Institute, 2019, 356(4): 1771–1788.

    Article  MathSciNet  Google Scholar 

  27. Silic, Matthew, and Kamran Mohseni. “Field deployment of a plume monitoring UAV flock.” IEEE Robotics and Automation Letters 4.2 (2019): 769–775.

  28. Briñón-Arranz, Lara, Alessandro Renzaglia, and Luca Schenato. “Multirobot Symmetric Formations for Gradient and Hessian Estimation With Application to Source Seeking.” IEEE Transactions on Robotics 35.3 (2019): 782–789.

  29. J. Spall, Intro to Stochastic Search and Optimization. Wiley, 2003.

  30. Xu C, He J, Zhang X, et al. Toward near-ground localization: Modeling and applications for TOA ranging error[J]. IEEE Transactions on Antennas and Propagation, 2017, 65(10): 5658–5662.

    Article  Google Scholar 

  31. Sastry S. Nonlinear systems: analysis, stability, and control[M]. Springer Science & Business Media, 2013

Download references

Acknowledgements

This work is supported in part by China National Postdoctoral Program for Innovative Talents under Grant BX20190033, in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2019A1515110325, in part by Project funded by China Postdoctoral Science Foundation under Grant 2020M670135, in part by Postdoctor Research Foundation of Shunde Graduate School of University of Science and Technology Beijing under Grant 2020BH001, and in part by the Fundamental Research Funds for the Central Universities under Grant 06500127.

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Authors and Affiliations

  1. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China

    Cheng Xu, Jiawei Rong, Yulin Chen, Hang Wu & Shihong Duan

  2. Shunde Graduate School, University of Science and Technology Beijing, Beijing, China

    Cheng Xu

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  1. Cheng Xu
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  2. Jiawei Rong
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  3. Yulin Chen
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  4. Hang Wu
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  5. Shihong Duan
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Correspondence to Cheng Xu or Shihong Duan.

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Xu, C., Rong, J., Chen, Y. et al. Spatial–Temporal Fusion Based Path Planning for Source Seeking in Wireless Sensor Network. Int J Wireless Inf Networks 29, 1–13 (2022). https://doi.org/10.1007/s10776-021-00540-9

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