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Task Planning and Collaboration of Jellyfish-inspired Multiple Spherical Underwater Robots

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Abstract

Task planning and collaboration of multiple robots have broad application prospects and value in the field of robotics. To improve the performance and working efficiency of our Spherical Underwater Robot (SUR), we propose a multi-robot control strategy that can realize the task planning and collaboration of multiple robots. To complete real-time information sharing of multiple robots, we first build an acoustic communication system with excellent communication performance under low noise ratio conditions. Then, the task planning and collaboration control strategy adjust the SURs so that they maintain their positions in the desired formation when the formation moves. Multiple SURs can move along desired trajectories in the expected formation. The control strategy of each SUR uses only its information and limited information of its neighboring SURs. Finally, based on theoretical analysis and experiments, we evaluate the validity and reliability of the proposed strategy. In comparison to the traditional leader–follower method, it is not necessary to designate a leader and its followers explicitly in our system; thus, important advantages, such as fault tolerance, are achieved.

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References

  1. Li, X., & Zhu, D. Q. (2018). An adaptive SOM neural network method for distributed formation control of a group of AUVs. IEEE Transactions on Industrial Electronics, 65, 8260–8270.

    Google Scholar 

  2. Wang, J. W., Chen, K. R., & Lewis, F. L. (2017). Coordination of multi-agent systems on interacting physical and communication topologies. Systems & Control Letters, 100, 56–65.

    Article  MathSciNet  Google Scholar 

  3. Yuan, Y. L., Liang, C. C., Kaneko, M., Chen, X., & Hogrefe, D. (2018). Topology control for energy-efficient localization in mobile underwater sensor networks using Stackelberg game. IEEE Transactions on Vehicular Technology, 68, 1487–1500.

    Article  Google Scholar 

  4. Yu, J. Z., Wang, C., & Xie, G. M. (2015). Coordination of multiple robotic fish with applications to underwater robot competition. IEEE Transactions on Industrial Electronics, 63, 1280–1288.

    Article  Google Scholar 

  5. Lee, G., & Chwa, D. (2018). Decentralized behavior-based formation control of multiple robots considering obstacle avoidance. Intelligent Service Robotics, 11, 127–138.

    Article  Google Scholar 

  6. Wang, Y., Tang, C., Wang, S., Cheng, L., Wang, R., Tan, M., & Hou, Z. G. (2021). Target tracking control of a biomimetic underwater vehicle through deep reinforcement learning. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2021.3054402

    Article  Google Scholar 

  7. He, W., Gao, H. J., Zhou, C., Yang, C. G., & Li, Z. J. (2020). Reinforcement learning control of a flexible two-link manipulator: An experimental investigation. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51, 7326–7336.

    Article  Google Scholar 

  8. Sui, Z. Z., Pu, Z. Q., Yi, J. Q., & Wu, S. G. (2020). Formation control with collision avoidance through deep reinforcement learning using model-guided demonstration. IEEE Transactions on Neural Networks and Learning Systems, 32, 2358–2372.

    Article  Google Scholar 

  9. Rizk, Y., Awad, M., & Tunstel, E. W. (2018). Decision making in multiagent systems: A survey. IEEE Transactions on Cognitive and Developmental Systems, 10, 514–529.

    Article  Google Scholar 

  10. Wang, J. Q., Wang, C., Wei, Y. J., & Zhang, C. J. (2019). Neuroadaptive sliding mode formation control of autonomous underwater vehicles with uncertain dynamics. IEEE Systems Journal, 14, 3325–3333.

    Article  Google Scholar 

  11. Zhai, Y. F., Zheng, X. W., & Xie, G. M. (2021). Fish lateral line inspired flow sensors and flow-aided control: A review. Journal of Bionic Engineering, 18, 264–291.

    Article  Google Scholar 

  12. Yu, J. Z., Wang, M., Dong, H. F., Zhang, Y. L., & Wu, Z. X. (2018). Motion control and motion coordination of bionic robotic fish: A review. Journal of Bionic Engineering, 15, 579–598.

    Article  Google Scholar 

  13. Xiao, H. Z., Li, Z. J., & Chen, C. P. (2016). Formation control of leader-follower mobile robots’ systems using model predictive control based on neural-dynamic optimization. IEEE Transactions on Industrial Electronics, 63, 5752–5762.

    Article  Google Scholar 

  14. Long, Y., Du, Z. J., Chen, C. F., Wang, W. D., He, L., Mao, X. W., Xu, G. Q., Zhao, G. Y., Li, X. L., & Dong, W. (2017). Development and analysis of an electrically actuated lower extremity assistive exoskeleton. Journal of Bionic Engineering, 14, 272–283.

    Article  Google Scholar 

  15. Li, G. N., Xu, H. L., & Lin, Y. (2018). Application of bat algorithm based time optimal control in multi-robots formation reconfiguration. Journal of Bionic Engineering, 15, 126–138.

    Article  Google Scholar 

  16. Rao, D. C., Kabat, M. R., Das, P. K., & Jena, P. K. (2019). Hybrid IWD-DE: A novel approach to model cooperative navigation planning for multi-robot in unknown dynamic environment. Journal of Bionic Engineering, 16, 235–252.

    Article  Google Scholar 

  17. Girerd, C., Kudryavtsev, A. V., Rougeot, P., Renaud, P., Rabenorosoa, K., & Tamadazte, B. (2020). SLAM-based follow-the-leader deployment of concentric tube robots. IEEE Robotics and Automation Letters, 5, 548–555.

    Article  Google Scholar 

  18. Gemmell, B. J., Costello, J. H., Colin, S. P., Stewart, C. J., Dabiri, J. O., Tafti, D., & Priya, S. (2013). Passive energy recapture in jellyfish contributes to propulsive advantage over other metazoans. Proceedings of the National Academy of Sciences, 110, 17904–17909.

    Article  Google Scholar 

  19. Gu, S. X., Guo, S. X., & Zheng, L. (2020). A highly stable and efficient spherical underwater robot with hybrid propulsion devices. Autonomous Robots, 44, 759–771.

    Article  Google Scholar 

  20. Gu, S. X., & Guo, S. X. (2017). Performance evaluation of a novel propulsion system for the spherical underwater robot (SURIII). Applied Sciences, 7, 1196.

    Article  Google Scholar 

  21. Hou, X. H., Guo, S. X., Shi, L. W., Xing, H. M., Yin, H., Li, Z., Zhou, M. G., & Xia, D. B. (2020). Improved model predictive-based underwater trajectory tracking control for the biomimetic spherical robot under constraints. Applied Sciences, 10, 8106.

    Article  Google Scholar 

  22. Guo, J., Li, C. X., & Guo, S. X. (2020). Study on the autonomous multirobot collaborative control system based on spherical amphibious robots. IEEE Systems Journal, 15, 4950–4957.

    Article  Google Scholar 

  23. An, R. C., Guo, S. X., Gu, S. X., & Zheng, L. (2019). Improvement and evaluation for the stability of mobile spherical underwater robots (SUR III). 2019 IEEE International Conference on Mechatronics and Automation, Tianjin, China, 2512–2517.

  24. Guo, J., Li, C. X., & Guo, S. X. (2019). A novel step optimal path planning algorithm for the spherical mobile robot based on fuzzy control. IEEE Access, 8, 1394–1405.

    Article  Google Scholar 

  25. Xing, H. M., Shi, L. W., Tang, K., Guo, S. X., Hou, X. H., Liu, Y., Liu, H. K., & Hu, Y. (2019). Robust RGB-D camera and IMU fusion-based cooperative and relative close-range localization for multiple turtle-inspired amphibious spherical robots. Journal of Bionic Engineering, 16, 442–454.

    Article  Google Scholar 

  26. Zheng, L., Guo, S. X., & Gu, S. X. (2019). The communication and stability evaluation of amphibious spherical robots. Microsystem Technologies, 25, 2625–2636.

    Article  Google Scholar 

  27. An, R. C., Guo, S. X., Zheng, L., Awa, T., & Sui, W. B. (2020). Modeling and experimental verification of a new spherical underwater robot. 2020 IEEE International Conference on Mechatronics and Automation, Beijing, China, 1222–1227.

  28. Zheng, L., Guo, S. X., Piao, Y., Gu, S. X., & An, R. C. (2020). Collaboration and task planning of turtle-inspired multiple amphibious spherical robots. Micromachines, 11, 71.

    Article  Google Scholar 

  29. He, Y. L., Zhu, L. Q., Sun, G. K., Qiao, J. F., & Guo, S. X. (2019). Underwater motion characteristics evaluation of multi amphibious spherical robots. Microsystem Technologies, 25, 499–508.

    Article  Google Scholar 

  30. Fossen, T. I. (1999). Guidance and control of ocean vehicles. University of Trondheim.

    Google Scholar 

  31. Ma, Y. N., Gong, Y. J., Xiao, C. F., Gao, Y., & Zhang, J. (2018). Path planning for autonomous underwater vehicles: An ant colony algorithm incorporating alarm pheromone. IEEE Transactions on Vehicular Technology, 68, 141–154.

    Article  Google Scholar 

  32. Sahu, C., Parhi, D. R., & Kumar, P. B. (2018). An approach to optimize the path of humanoids using adaptive ant colony optimization. Journal of Bionic Engineering, 15, 623–635.

    Article  Google Scholar 

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Authors and Affiliations

  1. Graduate School of Engineering, Kagawa University, Takamatsu, Kagawa, 761-0396, Japan

    Ruochen An, Shuxiang Guo, Chunying Li & Tendeng Awa

  2. Key Laboratory of Convergence Medical Engineering System and Healthcare Technology, The Ministry of Industry and Information Technology, School of Life Science and Technology, Beijing Institute of Technology, Beijing, 100081, China

    Shuxiang Guo

  3. School of Life Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China

    Yuanhua Yu

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  1. Ruochen An
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  2. Shuxiang Guo
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  3. Yuanhua Yu
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  4. Chunying Li
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  5. Tendeng Awa
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Correspondence to Shuxiang Guo.

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An, R., Guo, S., Yu, Y. et al. Task Planning and Collaboration of Jellyfish-inspired Multiple Spherical Underwater Robots. J Bionic Eng 19, 643–656 (2022). https://doi.org/10.1007/s42235-022-00164-6

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  • DOI: https://doi.org/10.1007/s42235-022-00164-6

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