A fusion chemical reaction optimization algorithm based on random molecules (RMCRO) is proposed
to meet the special demand of power transmission line inspection. This new algorithm improves the shortcomings
of chemical reaction algorithm by merging the idea of repellent-attractant rule and accelerates convergence by using
difference algorithm. The molecules in this algorithm avoid obstacles and search optimal path of transmission
line inspection by using sensors on multi-rotor unmanned aerial vehicle (UAV). The option of optimal path is
based on potential energy of molecules and cost function without repeated parameter adjustment and complicated
computation. By compared with an improved particle swarm optimization (IMPSO) in different circumstances
of simulation, it can be concluded that the new algorithm presented not only can obtain more optimal path and
avoid to trap in local minimum, but also can keep related sensors in a more stable status.
YANG Qing (杨轻), YANG Zhong (杨忠), HU Guoxiong (胡国雄), DU Wei (杜威)
. A New Fusion Chemical Reaction Optimization Algorithm Based on Random Molecules for Multi-Rotor UAV Path Planning in Transmission Line Inspection[J]. Journal of Shanghai Jiaotong University(Science), 2018
, 23(5)
: 671
-677
.
DOI: 10.1007/s12204-018-1981-2
[1] LIN Y, LI B, WANG D, et al. Application of multirotorUAV patrol system in UHV power grid construction[J]. Electric Power, 2017, 50(12): 141-147 (inChinese).
[2] TANG J, YANG J, GONG X. Multi-rotor UAV aerialsurvey technology for power grid inspection: researchand application [J]. Bulletin of Surveying and Mapping,2017, (5): 67-70 (in Chinese).
[3] AGIRREBEITIA J, AVIL′ES R, DE BUSTOS I F, etal. A new APF strategy for path planning in environmentswith obstacles [J]. Mechanism and MachineTheory, 2005, 40(6): 645-658.
[4] AHMED S U, MALIK U A, IQBAL M, et al. A guidedautowave PCNN for improved real time path planning[C]//Proceedings of the 2013 International Joint Conferenceon Neural Networks. Dallas, TX, USA: IEEE,2013: 290-296.
[5] AKTER S, LEE D J, LIM S T, et al. Grid basedpath planning using CNN and artificial potential fieldmethod [J]. Applied Mechanics and Materials, 2013,392: 830-836.
[6] HUANG H C, TSAI C C. Global path planning for autonomousrobot navigation using hybrid metaheuristicGA-PSO algorithm [C]//Proceeding of SICE AnnualConference. Tokyo, Japan: IEEE, 2011: 1338-1343.
[7] DAS P K, BEHERA H S, TRIPATHY H K, et al. A hybridimproved PSO-DV algorithm for multi-robot pathplanning in a clutter environment [J]. Neurocomputing,2016, 207: 735-753.
[8] PURCARU C, PRECUP R E, IERCAN D, et al. HybridPSO-GSA robot path planning algorithm in staticenvironments with danger zones [C]//Proceedings ofthe 17th International Conference on System Theory,Control and Computing. Sinaia, Romania: IEEE, 2013:434-439.
[9] SHAKIBA R, NAJAFIPOUR M R, SALEHI M E.An improved PSO-based path planning algorithm forhumanoid soccer playing robots [C]//Proceedings ofthe 3rd Joint Conference of AI and Robotics and 5thRobocup Iran Open International Symposium. Tehran,Iran: IEEE, 2013: 1-6.
[10] GENG N, SUN X, GONG D, et al. Solving robot pathplanning in an environment with terrains based on intervalmulti-objective PSO [J]. International Journalof Robotics and Automation, 2016, 31(2): 100-110.
[11] CH?AARI I, KOUB?AA A, BENNACEUR H, et al.SmartPATH: A hybrid ACO-GA algorithm for robotpath planning [C]//Proceedings of the 2012 IEEECongress on Evolutionary Computation. Brisbane,QLD, Australia: IEEE, 2012: 1-8.
[12] OLEIWI B K, ROTH H, KAZEM B I. A hybrid approachbased on ACO and GA for multi objective mobilerobot path planning [J]. Applied Mechanics andMaterials, 2014, 527: 203-212.
[13] CHEN X, KONG Y, FANG X, et al. A fast two-stageACO algorithm for robotic path planning [J]. NeuralComputing and Applications, 2013, 22(2): 313-319.
[14] MAZINAN A H, SAGHARICHIHA F. A novel hybridPSO-ACO approach with its application to SPP [J].Evolving Systems, 2015, 6(4): 293-302.
[15] GIGRAS Y, CHOUDHARY K, GUPTA K, et al.A hybrid ACO-PSO technique for path planning[C]//Proceedings of the 2nd International Conferenceon Computing for Sustainable Global Development,New Delhi, India: IEEE, 2015: 1616-1621.
[16] LAM A Y S, LI V O K. Chemical-reaction-inspiredmetaheuristic for optimization [J]. IEEE Transactionson Evolutionary Computation, 2010, 14(3): 381-399.
[17] MOHAJER B, KIANI K, SAMIEI E, et al. A new onlinerandom particles optimization algorithm for mobilerobot path planning in dynamic environments [J].Mathematical Problems in Engineering, 2013, (2): 1-9.
