A two-stage optimization method for unmanned aerial vehicle inspection of an oil and gas pipeline network
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摘要
Oil and gas pipeline networks are a key link in the coordinated development of oil and gas both upstream and downstream.To improve the reliability and safety of the oil and gas pipeline network, inspections are implemented to minimize the risk of leakage, spill and theft, as well as documenting actual incidents. In recent years, unmanned aerial vehicles have been recognized as a promising option for inspection due to their high efficiency. However, the integrated optimization of unmanned aerial vehicle inspection for oil and gas pipeline networks, including physical feasibility, the performance of mission, cooperation, real-time implementation and three-dimensional(3-D) space, is a strategic problem due to its large-scale,complexity as well as the need for efficiency. In this work, a novel mixed-integer nonlinear programming model is proposed that takes into account the constraints of the mission scenario and the safety performance of unmanned aerial vehicles. To minimize the total length of the inspection path, the model is solved by a two-stage solution method. Finally, a virtual pipeline network and a practical pipeline network are set as two examples to demonstrate the performance of the optimization schemes. Moreover, compared with the traditional genetic algorithm and simulated annealing algorithm, the self-adaptive genetic simulated annealing algorithm proposed in this paper provides strong stability.
Oil and gas pipeline networks are a key link in the coordinated development of oil and gas both upstream and downstream.To improve the reliability and safety of the oil and gas pipeline network, inspections are implemented to minimize the risk of leakage, spill and theft, as well as documenting actual incidents. In recent years, unmanned aerial vehicles have been recognized as a promising option for inspection due to their high efficiency. However, the integrated optimization of unmanned aerial vehicle inspection for oil and gas pipeline networks, including physical feasibility, the performance of mission, cooperation, real-time implementation and three-dimensional(3-D) space, is a strategic problem due to its large-scale,complexity as well as the need for efficiency. In this work, a novel mixed-integer nonlinear programming model is proposed that takes into account the constraints of the mission scenario and the safety performance of unmanned aerial vehicles. To minimize the total length of the inspection path, the model is solved by a two-stage solution method. Finally, a virtual pipeline network and a practical pipeline network are set as two examples to demonstrate the performance of the optimization schemes. Moreover, compared with the traditional genetic algorithm and simulated annealing algorithm, the self-adaptive genetic simulated annealing algorithm proposed in this paper provides strong stability.
Oil and gas pipeline networks are a key link in the coordinated development of oil and gas both upstream and downstream.To improve the reliability and safety of the oil and gas pipeline network, inspections are implemented to minimize the risk of leakage, spill and theft, as well as documenting actual incidents. In recent years, unmanned aerial vehicles have been recognized as a promising option for inspection due to their high efficiency. However, the integrated optimization of unmanned aerial vehicle inspection for oil and gas pipeline networks, including physical feasibility, the performance of mission, cooperation, real-time implementation and three-dimensional(3-D) space, is a strategic problem due to its large-scale,complexity as well as the need for efficiency. In this work, a novel mixed-integer nonlinear programming model is proposed that takes into account the constraints of the mission scenario and the safety performance of unmanned aerial vehicles. To minimize the total length of the inspection path, the model is solved by a two-stage solution method. Finally, a virtual pipeline network and a practical pipeline network are set as two examples to demonstrate the performance of the optimization schemes. Moreover, compared with the traditional genetic algorithm and simulated annealing algorithm, the self-adaptive genetic simulated annealing algorithm proposed in this paper provides strong stability.
引文
Akbaripour H, Masehian E. Semi-lazy probabilistic roadmap:a parameter-tuned, resilient and robust path planning method for manipulator robots. Int J Adv Manuf Technol. 2017;89(5):1401-30. https://doi. org/10.1007/s00170-016-9074-6.
Bhattacharya P, Gavrilova ML. Voronoi diagram in optimal path planning. In:4th International Symposium on Voronoi Diagrams inScience and Engineering(ISVD 2007); 2007. p. 38-47. https://doi.org/10.1109/isvd.2007.43.
Candeloro M, Lekkas AM, Srensen AJ. A Voronoi-diagram-based dynamic path-planning system for underactuated marine vessels.Control Eng Pract. 2017;61:41-54. https://doi.org/10.1016/j.conengprac.2017.01.007.
Chen C, Rickert M, Knoll A. Path planning with orientation-aware space exploration guided heuristic search for autonomous parking and maneuvering. In:2015 IEEE Intelligent Vehicles Symposium(IV);2015. p. 1148-53. https://doi.org/10.1109/ivs.2015.7225838.
Chen X, Chen X. The UAV dynamic path planning algorithm research based on Voronoi diagram. In:Proceedings of the26th Chinese Control and Decision Conference(2014 CCDC).Changsha, China; 2014. p. 1069-71. https://doi.org/10.1109/ccdc.2014.6852323.
Gammell JD, Srinivasa SS, Barfoot TD. Batch Informed Trees(BIT*):sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs. In:Proceedings of2015 IEEE International Conference on Robotics and Automation(ICRA). Seattle, WA, USA; 2015. p. 3067-74. https://doi.org/10.1109/icra.2015.7139620.
Garrido S, Moreno L, Abderrahim M, et al. Path planning for mobile robot navigation using Voronoi diagram and fast marching. In:Proceedings of 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. Beijing, China; 2006. p. 2376-81.https://doi.org/10.1109/iros.2006.282649.
Geraerts R, Overmars MH. Sampling and node adding in probabilistic roadmap planners. Robot Auton Syst. 2006;54:165-73. https://doi.org/10.1016/j.robot.2005.09.026.
Gomez C, Green DR. Small unmanned airborne systems to support oil and gas pipeline monitoring and mapping. Arab J Geosci.2017;10(9):202. https://doi.org/10.1007/s12517-017-2989-x.
Guo J, Wang X, Fan S, et al. Forward and reverse logistics network and route planning under the environment of low-carbon emissions:a case study of Shanghai fresh food E-commerce enterprises.Comput Ind Eng. 2017;106:351-60. https://doi.org/10.1016/j.cie.2017.02.002.
Hart PE, Nilsson NJ, Raphael B. A formal basis for the heuristic determination of minimum cost paths. IEEE Trans Syst Sci Cybern.1968;4(2):100-7. https://doi.org/10.1109/TSSC. 1968.300136.
Hu Y, Yang SX. A knowledge based genetic algorithm for path planning of a mobile robot. In:Robotics and Automation, 2004. Proceedings. ICRA'04. Proceedings of 2004 IEEE International Conference on Robotics and Automation. New Orleans, LA, USA;2004;(2). p. 4350-55. https://doi.org/10.1109/robot.2004.1302402.
Li J, Huang Y, Xu Z, et al. Path planning of UAV based on hierarchical genetic algorithm with optimized search region. In:Proceedings of 2017 13th IEEE International Conference on Control&Automation(ICCA). Ohrid, Macedonia; 2017. p. 1033-8. https://doi.org/10.1109/icca.2017.8003203.
Nazarahari M, Khanmirza E, Doostie S. Multi-objective multi-robot path planning in continuous environment using an enhanced genetic algorithm. Expert Syst Appl. 2019;115:106-20. https://doi.org/10.1016/j.eswa.2018.08.008.
Patle BK, Parhi DRK, Jagadeesh A, et al. Matrix-Binary Codes based Genetic Algorithm for path planning of mobile robot. Comput Electr Eng. 2018;67:708-28. https://doi.org/10.1016/j.compelecen g.2017.12.011.
Reddy HP, Narasimhan S, Bhallamudi SM, et al. Leak detection in gas pipeline networks using an efficient state estimator. Part-Ⅰ:theory and simulations. Comput Chem Eng. 2011;35(4):651-61. https://doi.org/10.1016/j.compchemeng.2010.10.006.
Sedighi KH, Ashenayi K, Manikas TW, et al. Autonomous local path planning for a mobile robot using a genetic algorithm. In:Proceedings of the 2004 Congress on Evolutionary Computation(IEEECat. No. 04TH8753). Portland, OR, USA; 2004. p. 1338-45. https://doi.org/10.1109/cec.2004.1331052.
Shen X, Sang J, Sun Y, et al. Application of improved ant colony algorithm in distribution network patrol route planning. In:Proceedings of 2016 7th IEEE International Conference on Software Engineering and Service Science(ICSESS). Beijing, China; 2016.p. 560-63. https://doi.org/10.1109/icsess.2016.7883132.
Szczerba RJ, Galkowski P, Glicktein IS, et al. Robust algorithm for real-time route planning. IEEE Trans Aerosp Electron Syst.2000;36(3):869-78. https://doi.org/10.1109/7.869506.
Tsai CC, Huang HC, Chan CK. Parallel elite genetic algorithm and its application to global path planning for autonomous robot navigation. IEEE Trans Ind Electron. 2011;58(3):4813-21.https://doi.org/10.1109/TIE.2011.2109332.
Tu J, Yang SX. Genetic algorithm based path planning for a mobile robot. In:Proceedings of 2003 IEEE International Conference on Robotics and Automation(Cat. No. 03CH37422). Taipei,Taiwan(China); 2003. p. 1221-6. https://doi.org/10.1109/robot.2003.1241759.
Wang B, Liang Y, Zheng J, et al. A methodology to restructure a pipeline system for an oilfield in the mid to late stages of development.Comput Chem Eng. 2018a;115:133-40. https://doi.org/10.1016/j.compchemeng.2018.04.008.
Wang B, Yuan M, Zhang H, et al. An MILP model for optimal design of multi-period natural gas transmission network. Chem Eng Res Des. 2018b;129:122-31. https://doi.org/10.1016/j.cherd.2017.11.001.
Wang Z, Cai J. Probabilistic roadmap method for path-planning in radioactive environment of nuclear facilities. Prog Nucl Energy.2018;109:113-20. https://doi.org/10.1016/j.pnucene.2018.08.006.
Yu J, La Valle SM. Optimal multirobot path planning on graphs:complete algorithms and effective heuristics. IEEE Trans Robot.2016;32(5):1163-77. https://doi.org/10.1109/TR0.2016.2593448.
Zhang H, Liang Y, Liao Q, et al. A hybrid computational approach for detailed scheduling of products in a pipeline with multiple pump stations. Energy. 2017a;119:612-28. https://doi.org/10.1016/j.energy.2016.11.027.
Zhang H, Liang Y, Ma J, et al. An MILP method for optimal offshore oilfield gathering system. Ocean Eng. 2017b;141:25-34. https://doi.org/10.1016/j.oceaneng.2017.06.011.
Zhang H, Liang Y, Ma J, et al. An improved PSO method for optimal design of subsea oil pipelines. Ocean Eng. 2017c;141:154-63.https://doi.org/10.1016/j.oceaneng.2017.06.023.
Zhang H, Liang Y, Zhang W, et al. A unified MILP model for topological structure of production well gathering pipeline network.J Pet Sci Eng. 2017d; 152:284-93. https://doi.org/10.1016/j.petro1.2017.03.016.
Zhang H, Liang Y, Zhang W, et al. Improved PSO-based method for leak detection and localization in liquid pipelines. IEEE Trans Ind Inf. 2018a;14(7):3143-54. https://doi.org/10.1109/TII.2018.2794987.
Zhang H, Yuan M, Liang Y, et al. A risk assessment based optimization method for route selection of hazardous liquid railway network. Saf Sci. 2018b;110:217-29. https://doi.org/10.1016/j.ssci.2018.04.003.
Bhattacharya P, Gavrilova ML. Voronoi diagram in optimal path planning. In:4th International Symposium on Voronoi Diagrams inScience and Engineering(ISVD 2007); 2007. p. 38-47. https://doi.org/10.1109/isvd.2007.43.
Candeloro M, Lekkas AM, Srensen AJ. A Voronoi-diagram-based dynamic path-planning system for underactuated marine vessels.Control Eng Pract. 2017;61:41-54. https://doi.org/10.1016/j.conengprac.2017.01.007.
Chen C, Rickert M, Knoll A. Path planning with orientation-aware space exploration guided heuristic search for autonomous parking and maneuvering. In:2015 IEEE Intelligent Vehicles Symposium(IV);2015. p. 1148-53. https://doi.org/10.1109/ivs.2015.7225838.
Chen X, Chen X. The UAV dynamic path planning algorithm research based on Voronoi diagram. In:Proceedings of the26th Chinese Control and Decision Conference(2014 CCDC).Changsha, China; 2014. p. 1069-71. https://doi.org/10.1109/ccdc.2014.6852323.
Gammell JD, Srinivasa SS, Barfoot TD. Batch Informed Trees(BIT*):sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs. In:Proceedings of2015 IEEE International Conference on Robotics and Automation(ICRA). Seattle, WA, USA; 2015. p. 3067-74. https://doi.org/10.1109/icra.2015.7139620.
Garrido S, Moreno L, Abderrahim M, et al. Path planning for mobile robot navigation using Voronoi diagram and fast marching. In:Proceedings of 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. Beijing, China; 2006. p. 2376-81.https://doi.org/10.1109/iros.2006.282649.
Geraerts R, Overmars MH. Sampling and node adding in probabilistic roadmap planners. Robot Auton Syst. 2006;54:165-73. https://doi.org/10.1016/j.robot.2005.09.026.
Gomez C, Green DR. Small unmanned airborne systems to support oil and gas pipeline monitoring and mapping. Arab J Geosci.2017;10(9):202. https://doi.org/10.1007/s12517-017-2989-x.
Guo J, Wang X, Fan S, et al. Forward and reverse logistics network and route planning under the environment of low-carbon emissions:a case study of Shanghai fresh food E-commerce enterprises.Comput Ind Eng. 2017;106:351-60. https://doi.org/10.1016/j.cie.2017.02.002.
Hart PE, Nilsson NJ, Raphael B. A formal basis for the heuristic determination of minimum cost paths. IEEE Trans Syst Sci Cybern.1968;4(2):100-7. https://doi.org/10.1109/TSSC. 1968.300136.
Hu Y, Yang SX. A knowledge based genetic algorithm for path planning of a mobile robot. In:Robotics and Automation, 2004. Proceedings. ICRA'04. Proceedings of 2004 IEEE International Conference on Robotics and Automation. New Orleans, LA, USA;2004;(2). p. 4350-55. https://doi.org/10.1109/robot.2004.1302402.
Li J, Huang Y, Xu Z, et al. Path planning of UAV based on hierarchical genetic algorithm with optimized search region. In:Proceedings of 2017 13th IEEE International Conference on Control&Automation(ICCA). Ohrid, Macedonia; 2017. p. 1033-8. https://doi.org/10.1109/icca.2017.8003203.
Nazarahari M, Khanmirza E, Doostie S. Multi-objective multi-robot path planning in continuous environment using an enhanced genetic algorithm. Expert Syst Appl. 2019;115:106-20. https://doi.org/10.1016/j.eswa.2018.08.008.
Patle BK, Parhi DRK, Jagadeesh A, et al. Matrix-Binary Codes based Genetic Algorithm for path planning of mobile robot. Comput Electr Eng. 2018;67:708-28. https://doi.org/10.1016/j.compelecen g.2017.12.011.
Reddy HP, Narasimhan S, Bhallamudi SM, et al. Leak detection in gas pipeline networks using an efficient state estimator. Part-Ⅰ:theory and simulations. Comput Chem Eng. 2011;35(4):651-61. https://doi.org/10.1016/j.compchemeng.2010.10.006.
Sedighi KH, Ashenayi K, Manikas TW, et al. Autonomous local path planning for a mobile robot using a genetic algorithm. In:Proceedings of the 2004 Congress on Evolutionary Computation(IEEECat. No. 04TH8753). Portland, OR, USA; 2004. p. 1338-45. https://doi.org/10.1109/cec.2004.1331052.
Shen X, Sang J, Sun Y, et al. Application of improved ant colony algorithm in distribution network patrol route planning. In:Proceedings of 2016 7th IEEE International Conference on Software Engineering and Service Science(ICSESS). Beijing, China; 2016.p. 560-63. https://doi.org/10.1109/icsess.2016.7883132.
Szczerba RJ, Galkowski P, Glicktein IS, et al. Robust algorithm for real-time route planning. IEEE Trans Aerosp Electron Syst.2000;36(3):869-78. https://doi.org/10.1109/7.869506.
Tsai CC, Huang HC, Chan CK. Parallel elite genetic algorithm and its application to global path planning for autonomous robot navigation. IEEE Trans Ind Electron. 2011;58(3):4813-21.https://doi.org/10.1109/TIE.2011.2109332.
Tu J, Yang SX. Genetic algorithm based path planning for a mobile robot. In:Proceedings of 2003 IEEE International Conference on Robotics and Automation(Cat. No. 03CH37422). Taipei,Taiwan(China); 2003. p. 1221-6. https://doi.org/10.1109/robot.2003.1241759.
Wang B, Liang Y, Zheng J, et al. A methodology to restructure a pipeline system for an oilfield in the mid to late stages of development.Comput Chem Eng. 2018a;115:133-40. https://doi.org/10.1016/j.compchemeng.2018.04.008.
Wang B, Yuan M, Zhang H, et al. An MILP model for optimal design of multi-period natural gas transmission network. Chem Eng Res Des. 2018b;129:122-31. https://doi.org/10.1016/j.cherd.2017.11.001.
Wang Z, Cai J. Probabilistic roadmap method for path-planning in radioactive environment of nuclear facilities. Prog Nucl Energy.2018;109:113-20. https://doi.org/10.1016/j.pnucene.2018.08.006.
Yu J, La Valle SM. Optimal multirobot path planning on graphs:complete algorithms and effective heuristics. IEEE Trans Robot.2016;32(5):1163-77. https://doi.org/10.1109/TR0.2016.2593448.
Zhang H, Liang Y, Liao Q, et al. A hybrid computational approach for detailed scheduling of products in a pipeline with multiple pump stations. Energy. 2017a;119:612-28. https://doi.org/10.1016/j.energy.2016.11.027.
Zhang H, Liang Y, Ma J, et al. An MILP method for optimal offshore oilfield gathering system. Ocean Eng. 2017b;141:25-34. https://doi.org/10.1016/j.oceaneng.2017.06.011.
Zhang H, Liang Y, Ma J, et al. An improved PSO method for optimal design of subsea oil pipelines. Ocean Eng. 2017c;141:154-63.https://doi.org/10.1016/j.oceaneng.2017.06.023.
Zhang H, Liang Y, Zhang W, et al. A unified MILP model for topological structure of production well gathering pipeline network.J Pet Sci Eng. 2017d; 152:284-93. https://doi.org/10.1016/j.petro1.2017.03.016.
Zhang H, Liang Y, Zhang W, et al. Improved PSO-based method for leak detection and localization in liquid pipelines. IEEE Trans Ind Inf. 2018a;14(7):3143-54. https://doi.org/10.1109/TII.2018.2794987.
Zhang H, Yuan M, Liang Y, et al. A risk assessment based optimization method for route selection of hazardous liquid railway network. Saf Sci. 2018b;110:217-29. https://doi.org/10.1016/j.ssci.2018.04.003.
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