Xia Li This email address is being protected from spambots. You need JavaScript enabled to view it.1

1College of Intelligent Manufacturing Engineers, Hefei College of Finance & Economics, Anhui, China,230601


 

Received: May 19, 2022
Accepted: June 30, 2022
Publication Date: August 16, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202305_26(5).0008  


ABSTRACT


Aiming at the existing problems of multiple sensors, a multi-sensor cooperative positioning technology based on dual neural networks is proposed. The established multi-sensor joint indoor positioning model introduces inertial sensor data information on the basis of theWi-Fi positioning model. This article uses Gated Recurrent Unit (GRU) to establish a motion trajectory model, which improves the accuracy of indoor positioning and anti-interference ability, so that it can still maintain good positioning accuracy in a complex indoor environment. The basic modeling of UAV trajectory planning is studied. We analyze the physical performance and planning environment of the UAV, and transform the problem into a mathematical function. In the realization of the planning algorithm, the road sign guidance method is used to solve the local trap problem of the velocity vector field. We research on the UAV constraint fusion method, and use the "detection step method" to solve the UAV minimum turning radius constraint fusion problem. By setting the distance between the wall-climbing robot and the UAV formation relationship and introducing the speed of avoiding other UAVs, the trajectory collision-free constraint fusion is realized. On this basis, two coordination mechanisms of temporary target selection and detection step adjustment are designed to realize the coordination of the trajectory of the wall-climbing robot and the UAV in different scenarios. We study environment update methods and adopt partial update strategies to realize the processing of planning space update, task update, and threat update, so as to ensure that the algorithm can be applied to a dynamically changing environment. The design of the planning algorithm was completed, the track evaluation index was proposed, and the simulation verification software was developed to complete the verification of the algorithm’s feasibility, rationality, completeness, and rapidity. The effects of adding threats and deleting threats during the planning process on the coordinated trajectory planning of wall-climbing robots and UAVs are studied. In both cases, the wall-climbing robot and the UAV can avoid the threat area and reach their respective target points in coordination. It can be seen that the local update mechanism of the environment can quickly and effectively respond to the dynamic change of the threat, which not only ensures the speed of the algorithm. It also ensures that wall-climbing robots and drones can safely avoid dynamic threats, effectively realizing the collaboration allowed by the environment.


Keywords: wall-climbing robot; unmanned aerial vehicle; collaborative constraint; multi-sensor


REFERENCES


  1. [1] I. Tsitsimpelis, C. Taylor, B. Lennox, and M. Joyce, (2019) “A review of ground-based robotic systems for the characterization of nuclear environments" Progress in Nuclear Energy 111: 109–124. DOI: 10.1016/j.pnucene.2018.10.023.
  2. [2] M. Fujita, S. Ikeda, T. Fujimoto, T. Shimizu, S. Ikemoto, and T. Miyamoto, (2018) “Development of universal vacuum gripper for wall-climbing robot" Advanced Robotics 32(6): 283–296. DOI: 10.1080/01691864.2018.1447238.
  3. [3] S. Bian, Y. Wei, F. Xu, and D. Kong, (2021) “A Fourlegged Wall-climbing Robot with Spines and Miniature Setae Array Inspired by Longicorn and Gecko" Journal of Bionic Engineering 18(2): 292–305. DOI: 10.1007/s42235-021-0032-0.
  4. [4] K. Zhang, Y. Chen, H. Gui, D. Li, and Z. Li, (2018) “Identification of the deviation of seam tracking and weld cross type for the derusting of ship hulls using a wallclimbing robot based on three-line laser structural light" Journal of Manufacturing Processes 35: 295–306. DOI: 10.1016/j.jmapro.2018.08.014.
  5. [5] K. Zhang, Y. Chen, H. Gui, D. Li, and Z. Li, (2018) “Identification of the deviation of seam tracking and weld cross type for the derusting of ship hulls using a wallclimbing robot based on three-line laser structural light" Journal of Manufacturing Processes 35: 295–306. DOI: 10.1016/j.jmapro.2018.08.014.
  6. [6] A. Sahbel, A. Abbas, and T. Sattar, (2019) “Experimental and numerical optimization of magnetic adhesion force for wall climbing robot applications" International Journal of Mechanical Engineering and Robotics Research 8(1): 18–24. DOI: 10.18178/ijmerr.8.1.18-24.
  7. [7] J. Zhao, X. Li, and J. Bai, (2018) “Experimental study of vortex suction unit-based wall-climbing robot on walls with various surface conditions" Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 232(21): 3977–3991. DOI: 10.1177/0954406218791203.
  8. [8] J. Ni, G. Tang, Z. Mo,W. Cao, and S. Yang, (2020) “An Improved Potential Game Theory Based Method for Multi-UAV Cooperative Search" IEEE Access 8: 47787–47796. DOI: 10.1109/ACCESS.2020.2978853.
  9. [9] X. Zhang and M. Ali, (2020) “A Bean Optimization-Based CooperationMethod for Target Searching by Swarm UAVs in Unknown Environments" IEEE Access 8: 43850–43862. DOI: 10.1109/ACCESS.2020.2977499.
  10. [10] S. Sano, D. Takaki, A. Ishida, and T. Ishida, (2021) “Image Mosaicking and Localization Using a Camera Mounted on a Hanging-TypeWall Climbing Robot" Journal of Robotics and Mechatronics 33(6): 1373–1383. DOI: 10.20965/jrm.2021.p1373.
  11. [11] J. Zhao, F. Gao, G. Ding, T. Zhang, W. Jia, and A. Nallanathan, (2018) “Integrating communications and control for UAV Systems: Opportunities and challenges" IEEE Access 6: 67519–67527. DOI: 10.1109/ACCESS.2018.2879637.
  12. [12] D. Kim, Y.-S. Kim, K. Noh, M. Jang, and S. Kim, (2020) “Wall Climbing Robot with Active Sealing for Radiation Safety of Nuclear Power Plants" Nuclear Science and Engineering: 1162–1174. DOI: 10.1080/00295639.2020.1777023.
  13. [13] L. Ruan, J. Wang, J. Chen, Y. Xu, Y. Yang, H. Jiang, Y. Zhang, and Y. Xu, (2018) “Energy-efficient multi-UAV coverage deployment in UAV networks: A game-theoretic framework" China Communications 15(10): 194–209. DOI: 10.1109/CC.2018.8485481.
  14. [14] C.WANG, L. WU, C. YAN, Z.WANG, H. LONG, and C. YU, (2020) “Coactive design of explainable agent-based task planning and deep reinforcement learning for human-UAVs teamwork" Chinese Journal of Aeronautics 33(11): 2930–2945. DOI: 10.1016/j.cja.2020.05.001.
  15. [15] Y. Liu and T. Seo, (2018) “AnyClimb-II: Dry-adhesive linkage-type climbing robot for uneven vertical surfaces" Mechanism and Machine Theory 124: 197–210. DOI: 10.1016/j.mechmachtheory.2018.02.010.
  16. [16] Z. ZHAO, Y. NIU, and L. SHEN, (2020) “Adaptive level of autonomy for human-UAVs collaborative surveillance using situated fuzzy cognitive maps" Chinese Journal of Aeronautics 33(11): 2835–2850. DOI: 10.1016/j.cja.2020.03.031.
  17. [17] I. Nemer, T. Sheltami, and A. Mahmoud, (2020) “A game theoretic approach of deployment a multiple UAVs for optimal coverage" Transportation Research Part A: Policy and Practice 140: 215–230. DOI: 10.1016/j.tra.2020.08.004.
  18. [18] J. Liu, L. Xu, S. Chen, H. Xu, G. Cheng, and J. Xu, (2021) “Development of a Bio-inspired Wall-Climbing Robot Composed of Spine Wheels, Adhesive Belts and Eddy Suction Cup" Robotica 39(1): 3–22. DOI: 10.1017/S026357471900184X.
  19. [19] W. Shule, C. Almansa, J. Queralta, Z. Zou, and T. Westerlund. “UWB-based localization for multi-UAV systems and collaborative heterogeneous multi-robot systems”. In: 175. cited By 23. 2020, 357–364. DOI: 10.1016/j.procs.2020.07.051.
  20. [20] A. Nunuparov and V. Chashchukhin, (2020) “Control System of an Aerodynamically Adhesive Wall-Climbing Robot" Journal of Computer and Systems Sciences International 59(2): 301–309. DOI: 10.1134/S1064230720010098.
  21. [21] D. Golightly, C. Gamble, R. Palacin, and K. Pierce, (2020) “Applying ergonomics within the multi-modelling paradigm with an example from multiple UAV control" Ergonomics 63(8): 1027–1043. DOI: 10.1080/00140139.2020.1723683.
  22. [22] X.Wu, Y. Liu, S. Xie, and Y. Guo, (2020) “Collaborative Defense with Multiple USVs and UAVs Based on Swarm Intelligence" Journal of Shanghai Jiaotong University (Science) 25(1): 51–56. DOI: 10.1007/s12204-019-2142-y.
  23. [23] A. Mukherjee, S. Misra, V. Pradeep Chandra, and N. Raghuwanshi, (2020) “ECoR: Energy-Aware Collaborative Routing for Task Offload in Sustainable UAV Swarms" IEEE Transactions on Sustainable Computing: DOI: 10.1109/TSUSC.2020.2976453.
  24. [24] B. Arbanas, A. Ivanovic, M. Car, M. Orsag, T. Petrovic, and S. Bogdan, (2018) “Decentralized planning and control for UAV–UGV cooperative teams" Autonomous Robots 42(8): 1601–1618. DOI: 10.1007/s10514-018-9712-y.
  25. [25] S. Huang, Z. Zeng, K. Ota, M. Dong, T. Wang, and N. Xiong, (2021) “An Intelligent Collaboration Trust Interconnections System for Mobile Information Control in Ubiquitous 5G Networks" IEEE Transactions on Network Science and Engineering 8(1): 347–365. DOI:10.1109/TNSE.2020.3038454.


    
 

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