Page 111 - Read Online
P. 111

Page 18 of 19                     Mai et al. Intell Robot 2023;3(4):466-84  I http://dx.doi.org/10.20517/ir.2023.37



               DECLARATIONS
               Authors’ contributions
               Made substantial contributions to the conception and design of the study and performed the analysis of the
               results: Dong N
               Carries out algorithm design and improvement and conducted theoretical analysis: Mai X, Liu S, Chen H


               Availability of data and materials
               Not applicable.


               Financial support and sponsorship
               This work was supported by the National Natural Science Foundation of China (No. 62273253), the Tianjin
               Natural Science Foundation Key Project (No. 22JCZDJC00330), and the funding of Joint Laboratory for Elec-
               tricPowerRobotsofChinaSouthernPowerGridCo.,Ltd. andElectricPowerResearchInstituteofGuangdong
               Power Grid Co., Ltd (No. GDDKY2022KF06).


               Conflicts of interest
               All authors declared that there are no conflicts of interest.


               Ethical approval and consent to participate
               Not applicable.


               Consent for publication
               Not applicable.


               Copyright
               © The Author(s) 2023.



               REFERENCES
               1.  Cerotti D, Distefano S, Merlino G, Puliafito A. A crowd-cooperative approach for intelligent transportation systems. IEEE Trans Intell
                  Transp Syst 2017;18:1529-39. DOI
               2.  Zhao Y, Zheng Z, Liu Y. Survey on computational-intelligence-based UAV path planning. Knowl Based Syst 2018;158:54-64. DOI
               3.  Duchoň F, Babinec A, Kajan M, et al. Path planning with modified a star algorithm for a mobile robot. Procedia Eng 2014;96:59-69. DOI
               4.  Zhang T, Zhu Y, Song J. Real-time motion planning for mobile robots by means of artificial potential field method in unknown environment.
                  Ind Rob 2010;37:384-400. DOI
               5.  Wang H, Yu Y, Yuan Q. Application of Dijkstra algorithm in robot path-planning. In: 2011 Second International Conference on Mechanic
                  Automation and Control Engineering; 2011 Jul 15-17; Hohhot. IEEE; 2011. pp. 1067-97. DOI
               6.  Wu PPY, Campbell D, Merz T. Multi-objective four-dimensional vehicle motion planning in large dynamic environments. IEEE Trans
                  Syst Man Cybern B 2011;41:621-34. DOI
               7.  Shorakaei H, Vahdani M, Imani B, Gholami A. Optimal cooperative path planning of unmanned aerial vehicles by a parallel genetic
                  algorithm. Robotica 2016;34:823-36. DOI
               8.  Roberge V, Tarbouchi M, Labonte G. Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path
                  planning. IEEE Trans Industr Inform 2013;9:132-41. DOI
               9.  Abeywickrama HV, Jayawickrama BA, He Y, Dutkiewicz E. Potential field based inter-UAV collision avoidance using virtual target
                  relocation. In: 2018 IEEE 87th Vehicular Technology Conference (VTC Spring); 2018 Jun 03-06; Porto, Portugal. IEEE; 2018. p. 1-5.
                  DOI
               10. Vanegas G, Samaniego F, Girbes V, Armesto L, Garcia-Nieto S. Smooth 3D path planning for non-holonomic UAVs. In: 2018 7th
                  International Conference on Systems and Control (ICSC); 2018 Oct 24-26; Valencia, Spain. IEEE; 2018. p. 1-6. DOI
               11. Jain G, Yadav G, Prakash D, Shukla A, Tiwari R. MVO-based path planning scheme with coordination of UAVs in 3-D environment. J
                  Comput Sci 2019;37:101060. DOI
               12. Wang L, Cai R, Lin M, Zhong Y. Enhanced list-based simulated annealing algorithm for large-scale traveling salesman problem. IEEE
                  Access 2019;7:144366-80. DOI
               13. Li G, Li J. An improved tabu search algorithm for the stochastic vehicle routing problem with soft time windows. IEEE Access
                  2020;8:158115-24. DOI
   106   107   108   109   110   111   112   113   114   115   116