Wei Zhang, Xiaoliang Feng, and Bing Sun


  1. [1] H. Liu, X. Huang, L. Tan, J. Guo, W. Wang, C. Yan, andC.Xu, Dynamic wireless charging for inspection robots based ondecentralized energy pickup structure, IEEE Transactions onIndustrial Informatics, 14(4), 2018, 1786–1797.
  2. [2] M. Cheng and D. Xiang, The design and application of a track-type autonomous inspection robotfor electrical distributionroom, Robotica, 38(2), 2020, 185–206.
  3. [3] Q. Jiang, Y. Liu, Y. Yan, P. Xu, L. Pei, and X. Jiang, Activepose re localization for intelligent substation inspectionrobot,IEEE Transactions on Industrial Electronics, 70(5), 2023,4972–4982.
  4. [4] U. Orozco-Rosas, O. Montiel, R. Sep´ulveda, Mobile robot pathplanning using membrane evolutionary artificial potential field,Applied Soft Computing, 77,2019, 236–251.
  5. [5] Z. Pan, C. Zhang, Y. Xia, H. Xiong, and X. Shao, An improvedartificial potential field method for path planning and formationcontrol of the multi-UAV systems, IEEE Transactions onCircuits and Systems II: Express Briefs, 69(3), 2021,1129–1133.
  6. [6] W. Pang, D. Zhu, and S. XYang, A novel time-varyingformation obstacle avoidance algorithm for multi AUVs,International Journal of Robotics and Automation, 2023, DOI:
  7. [7] B.K. Patle, L.G. Babu, A. Pandey, D.R.K. Parhi, andA. Jagadeesh, A review: On path planning strategies fornavigation of mobile robot, Defence Technology, 15(4), 2019,582–606.
  8. [8] X. Zhong, J. Tian, H. Hu, andX. Peng, Hybrid path planningbased on safe Aalgorithm and adaptive window approach formobile robot in large-scale dynamic environment, Journal ofIntelligent and Robotic Systems, 99, 2020, 65–77.
  9. [9] H. Sang, Y. You, X. Sun, Y. Zhou, and F. Liu, The hybridpath planning algorithm based on improved A and artificialpotential field for unmanned surface vehicle formations, OceanEngineering, 223, 2021, 108709.
  10. [10] F.H. Ajeil, I.K. Ibraheem, M.A. Sahib, and A.J. Humaidi,Multi-objective path planning of an autonomous mobile robotusing hybrid PSO-MFB optimization algorithm, Applied SoftComputing, 89, 2020, 106076.
  11. [11] C. Luo, J. Gao, Y. L. Murphey, and G. E. Jan,A computationally efficient neural dynamics approach totrajectory planning of an intelligent vehicle, IEEE InternationalJoint Conference on Neural Network, Beijing, China, 2014,934–939.
  12. [12] V.G. Nair and K.R. Guruprasad, Geodesic-VPC: Spatialpartitioning for multi-robot coverage problem, Interna-tional Journal of Robotics and Automation, 2020, 35(3),189–198.
  13. [13] V.G. Nair and K.R. Guruprasad, GM-VPC: An algorithm formulti-robot coverage of known spaces using generalized Voronoipartition, Robotica, 38(5), 2020, 845–860.
  14. [14] V.G. Nair and K.R. Guruprasad, 2D-VPC: An efficient coveragealgorithm for multiple autonomous vehicles, InternationalJournal of Control, Automation and Systems, 19(8), 2021,2891–2901.
  15. [15] C. Luo, S. X. Yang, X. Li, and M. Q.H. Meng, Neural-dynamics-driven complete area coverage navigation through cooperationof multiple mobile robots, IEEE Transactions on IndustrialElectronics, 64(1), 2016, 750–760.
  16. [16] B. Sun, D. Zhu, C. Tian, and C. Luo, Complete coverageautonomous underwater vehicles path planning based onglasius bio-inspired neural network algorithm for discrete andcentralized programming, IEEE transactions on cognitive anddevelopmental systems, 11(1), 2018, 73–84.
  17. [17] L. Han, X. Tan, Q. Wu, and X. Deng, An improvedalgorithm for complete coverage path planningbased onbiologically inspired neural network, IEEE Transactionson Cognitive and Developmental Systems, 2023, DOI:
  18. [18] D.V. Rodrigo, J.E. Sierra-Garc’ıa, andM. Santos, Glasius bio-inspired neural networks based UV-C disinfection path planningimproved by preventive deadlock processing algorithm,Advances in Engineering Software, 175, 2023, 103330.
  19. [19] P. Yao and Z. Zhao, Improved Glasius bio-inspired neuralnetwork for target search by multiagents, Information Sciences,568, 2021, 40–53.
  20. [20] P. Yao, K. Wu, and Y. Lou, Path Planning for MultipleUnmanned surface vehicles using glasius bio-inspired neuralnetwork with hungarian algorithm, IEEE Systems Journal,DOI:
  21. [21] M.A.V.J. Muthugala, S.M.B.P. Samarakoon, and M.R. Elara,Toward energy-efficient online complete coverage path planningof a ship hull maintenance robot based on glasius bio-inspiredneural network, Expert Systems with Applications, 187, 2022,115940.
  22. [22] Z.L. Hu, J.H. Li, A. Chen, F. Xu, R. Jia, F.-L. Lin, and C.-B.Tang, Optimize grouping and path of pylon inspection in powersystem, IEEE Access, 8, 2020, 108885–108895.
  23. [23] N Chen andY. Wang, Design and collaborative operation ofmultimobile inspection robots in smart microgrids, Complexity,2021, 2021, 1–11.
  24. [24] B. Wang, R. Guo, B. Li, L. Han, Y. Sun, and M. Wang,Smart Guard: An autonomous robotic system for inspectingsubstation equipment, Journal of Field Robotics, 29(1), 2012,123–137.
  25. [25] B. Chen, H. Zhang, F. Zhang, Y. Liu, C. Tan, H. Yu, and Y.Wang, A multi-robot distributed collaborative region coveragesearch algorithm based on glasius bio-inspired neural network,IEEETransactions on Cognitive and Developmental Systems,2022, DOI:
  26. [26] D. Zhu, C. Tian, B. Sun, and C. Luo, Complete coverage pathplanning of autonomous underwater vehicle based on GBNNalgorithm, Journal of Intelligent and Robotic Systems, 94,2019, 237–249.8

Important Links:

Go Back