METHOD FOR GENERATING INSPECTION INSTRUCTIONS FOR POWER INTELLIGENT DISPATCHING ROBOTS BASED ON DIRECTED GRAPH MODEL

Xuan Ruan,∗ Zaihe Yang,∗∗ Min Zhang,∗∗ Fan Du,∗∗ and Han Yan∗∗

References

  1. [1] H. Torajizadeh, A. Asadirad, E. Mashayekhi, and G. Dabiri,Design and manufacturing a novel screw-in-pipe inspectionrobot with steering capability, Journal of Field Robotics, 40(3),2023, 429–446.
  2. [2] A. Kakogawa, K. Murata, and S. Ma, Automatic t-branch travel of an articulated wheeled in-pipe inspectionrobot using joint angle response to environmental changes,IEEE Transactions on Industrial Electronics, 70(7), 2023,7041–7050.
  3. [3] C. Gotts, B. Hall, O. Beaumont, Z. Chen, W. Cleaver,J. England, D. White, and B. Thornton, Developmentof a prototype autonomous inspection robot for offshoreriser cables, Ocean Engineering, 257, 2022,111485.1–111485.19.
  4. [4] B, Sahu, P.K. Das, and M.R. Kabat, Multi-robot cooperationand path planning for stick transporting using improved Q-learning and democratic robotics PSO, Journal ComputerScience, 60, 2022, 101637.1–101637.16.
  5. [5] S. Sulaiman, and A.P. Sudheer, Modeling of a wheeledhumanoid robot and hybrid algorithm-based path planningof wheel base for the dynamic obstacles avoidance, IndustrialRobot, 49(6), 2022, 1058–1076.
  6. [6] R. Almadhoun, T. Taha, L. Seneviratne, and Y. Zweiri,Multi-robot hybrid coverage path planning for 3Dreconstruction of large structures, IEEE Access, 10(10), 2022,2037–2050.
  7. [7] E. Garcia, J.R. Villar, Q. Tan, J. Sedano, and C. Chira,An efficient multi-robot path planning solution using Aand coevolutionary algorithms, Integrated Computer-AidedEngineering, 30(1), 2023, 41–52.
  8. [8] L. Schulze, D.W. Bertol, and G.V. Raffo, Fast computationof binary search tree for PWA functions representationusing intersection classification, Automatica, 141(141), 2022,110217–110221.
  9. [9] S.M. Shafaei and H. Mousazadeh, Characterization of motionpower loss of off-road wheeled robot in a slippery terrain,Journal of Field Robotics, 40(1), 2023, 57–72.
  10. [10] B. Kim, L. Shimanuki, L.P. Kaelbling, and T. Lozano-P´erez,Representation, learning, and planning algorithms for geometrictask and motion planning, The International Journal ofRobotics Research, 41(2), 2022, 210–231.
  11. [11] S. H¨ugle, E. Genc, J. Dittmann, and P. Middendorf,Offline robot-path-planning and process simulation for thestructural analysis of coreless wound fibre-polymer com-posite structures, Key Engineering Materials, 926, 2022,1445–1453.
  12. [12] E. Garcia, J.R. Villar, Q. Tan, J. Sedano, and C. Chira,An efficient multi-robot path planning solution using Aand coevolutionary algorithms, Integrated Computer-AidedEngineering, 30(1), 2023, 41–52.
  13. [13] J. Muoz, B. L´opez, F. Quevedo, R. Barber, S. Garrido, and L.Moreno, Geometrically constrained path planning for roboticgrasping with differential evolution and fast marching square,Robotica, 41(2), 2022, 414–432.
  14. [14] T.V. Nguyen, M.H. Do, and J. Jo, Modet: A low-cost obstacletracker for self-driving mobile robot navigation using 2D-laserscan, Industrial Robot, 49(6), 2022, 1032–1041.
  15. [15] G. Neville, S. Chernova, and H. Ravichandar, (). D-ITAGS: A dynamic interleaved approach to resilient taskallocation, scheduling, and motion planning, IEEE Roboticsand Automation Letters, 8(2) 2022, 1037–1044.
  16. [16] J. Lim, H. Lee, and J. Choi, Nonlinear model predictive controlwith cost function scheduling for a wheeled mobile robot,in Proceeding of the IEEE/RSJ International Conference onIntelligent Robots and Systems, Kyoto, 2022, 5664–5670.
  17. [17] Z.F. Zhu, J.L. Hu, and J.J. Wen, State quantitative accuracyevaluation for obstacles in substation UAVs patrol inspection,Computer Simulation, 39(4), 2022 387–391
  18. [18] O. Thomasson, M. Battarra, G. Erdogan, and G. Laporte,Pallet location and job scheduling in a twin-robot system,Computers & Operations Research, 147, 2022, 1–12.
  19. [19] A. Kanchanaharuthai and E. Mujjalinvimut, Fixed-timecommand-filtered backstepping control design for hydraulicturbine regulating systems, Renewable Energy, 184, 2022,1091–1103.
  20. [20] P.M. Kolahi and M. Nazemizadeh, Nonlinear dynamic modelingof tractor-trailer mobile robots with consideration of wheelsinertia and their optimal point-to point path planning,Meccanica, 58(1), 2023, 245–253.
  21. [21] W. Zhang, X. Feng, and B. Sun, GLASIUS bio-inspiredneural network algorithm-based substation inspection robotdynamic path planning, International Journal of Robotics andAutomation, 39(3), 2024, 211–219.
  22. [22] S. Ziadi and M. Njah, SO-DVSF2-MT: An optimized mobilerobot motion planning approach for tracking moving targets.International Journal of Robotics and Automation, 37(5), 2022,421–430.

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