Yan-Jun Shen and Qin-Tian Wang
[1] M. Indri, C. Possieri, F. Sibona, P.D.C. Cheng, and V.D.Hoang, Supervised global path planning for mobile robotswith obstacle avoidance, Proc. 24th IEEE International Conf.on Emerging Technologies and Factory Automation (ETFA),Zaragoza, 2019, 601–608.428 [2] Z. Lin, Z. Yingjie, and L. Yangfan, Mobile robot path planningbased on improved localized particle swarm optimization, IEEESensors Journal, 21(5), 2021, 6962–6972. [3] L. Zhu, J.Z. Fan, J. Zhao, X.G. Wu, and G. Liu, Global pathplanning and local obstacle avoidance of searching robot in minedisasters based on grid method, Zhongnan Daxue Xuebao (ZiranKexue Ban)/Journal of Central South University (Science andTechnology), 42(11), 2011, 3421–3428. [4] L.I. Ping, J.Y. Zhu, F. Peng, and L. Yang, Path planning basedon visibility graph and A algorithm, Computer Engineering,40(3) 2014, 193–196. [5] P. Biber, S. Fleck, and T. Duckett, 3D modeling ofindoor environments for a robotic security guard, Proc.IEEE Computer Society Conf. Computer Vision and PatternRecognition (CVPR’05) - Workshops, San Diego, CA, 2005,124–124. [6] S.M.H. Jafri and K. Rahul, Motion planning for an outdoormobile robot on a probabilistic costmap, International Journalof Robotics and Automation, 34(6), 2019, 627–631. [7] G.Z. Tan, H.E. Huan, and A. Sloman, Global optimal pathplanning for mobile robot based on improved Dijkstra algorithmand ant system algorithm, Journal of Central South Universityof Technology, 13(1), 2006, 80–86. [8] Z. Zhou, J. Wang, Z. Zhu, D. Yang, and J. Wu, Tangentnavigated robot path planning strategy using particle swarmoptimized artificial potential field, Optik - International Journalfor Light and Electron Optics, 158, 2017, 639–651. [9] H.K. Dong, A. Abraham, and J.H. Cho, A hybrid genetic algo-rithm and bacterial foraging approach for global optimization,Information Sciences, 177(18), 2007, 3918–3937. [10] H. Yang, J. Qi, Y. Miao, H. Sun, and J. Li, A new robotnavigation algorithm based on a double-layer ant algorithmand trajectory optimization, IEEE Transactions on IndustrialElectronics, 66(11), 2019, 8557–8566. [11] W. Tam, L. Cheng, T. Wang, W. Xia, and L. Chen, Animproved genetic algorithm based robot path planning methodwithout collision in confined workspace, International Journalof Modelling, Identification and Control, 33(2), 2019, 120. [12] Z. Nie, X. Yang, S. Gao, Y. Zheng, J. Wang, and Z. Wang,Research on autonomous moving robot path planning based onimproved particle swarm optimization, Proc. IEEE Congresson Evolutionary Computation (CEC), Vancouver, BC, 2016,2532–2536. [13] P. Chen, Q. Li, C. Zhang, J. Cui, and H. Zhou, Hybrid chaos-based particle swarm optimization-ant colony optimizationalgorithm with asynchronous pheromone updating strategyfor path planning of landfill inspection robots, InternationalJournal of Advanced Robotic Systems, 16(4), 2019, 1–11. [14] V. Jamshidi, V. Nekoukar, and M.H. Refan, Analysis of parallelgenetic algorithm and parallel particle swarm optimizationalgorithm UAV path planning on controller area network,Journal of Control, Automation and Electrical Systems, 31(3),2019, 129–140. [15] H. Zhangfang, F. Chunyi, L. Yuan, and M. Ziping, Improvedparticle swarm optimization algorithm for mobile robot pathplanning, Application Research of Computers, 38(10), 2021,3089–3092. [16] C. Sahu, P.B. Kumar, and D.R. Parhi, An intelligentpath planning approach for humanoid robots using adaptiveparticle swarm optimization, International Journal on ArtificialIntelligence Tools, 27(5), 2018, 1850015. [17] J. Cheng, Z. Miao, L. Bing, and W. Xu, An improvedACO algorithm for mobile robot path planning, Proc. IEEEInternational Conf. on Information and Automation (ICIA),Lijiang, 2016, 963–968. [18] X. Chen, Y. Kong, X. Fang, and Q. Wu, A fast two-stageACO algorithm for robotic path planning, Neural Computing& Applications, 22(2), 2013, 313–319. [19] X. Ye, W. Dong, P. Li, and S. Reza, Hierarchical multialgorithmparallel circuit simulation, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 30(1), 2011,45–58. [20] C.C. Tsai, H.C. Huang, and C.K. Chan, Parallel elite geneticalgorithm and its application to global path planning forautonomous robot navigation, IEEE Transactions on IndustrialElectronics, 58(10), 2011, 4813–4821. [21] Y.H. Cheng, C.N. Kuo, and C.M. Lai, Effective natural PCR-RFLP primer design for SNP genotyping using teaching-learning-based optimization with elite strategy, IEEE Trans-actions on Nanobioscience, 15(7), 2016, 657–665. [22] P.H. Chen, Two-level hierarchical approach to unit commitmentusing expert system and elite PSO, IEEE Transactions onPower Systems, 27(2), 2012, 780–789. [23] Y.-Q. Qin, D.-B. Sun, N. Li, and Q. Ma, Path planning formobile robot based on particle swarm optimization, Robot,26(3), 2004, 222–225. [24] Y. Wang, Y. Zhao, S.A. Bortoff. and K. Ueda, A real-timeenergy-optimal trajectory generation method for a servomotorsystem, IEEE Transactions on Industrial Electronics, 62(2),2015, 1175–1188. [25] L. Wang and C. Luo, A hybrid genetic tabu search algorithmfor mobile robot to solve AS/RS path planning, InternationalJournal of Robotics and Automation, 33(2), 2018, 161–168. [26] L. Chen, Z. Ji, L. Jing, and Y. Chen, Bidirectional Dijkstraalgorithm for best-routing of urban traffic network, Proc. ofSPIE - The International Society for Optical Engineering,Nanjing, 2007, 431–438. [27] M. Liu, Five-elements cycle optimization algorithm for solvingcontinuous optimization problems, Proc. IEEE InternationalConf. on Soft Computing & Machine Intelligence (ISCMI),Mauritius, 2017, 75–79. [28] S. Pattanayak and B.B. Choudhury, Trajectory planning ofan autonomous mobile robot, International Journal of SwarmIntelligence, 4(2), 2019, 96–110. [29] X.M. Ma, H. Wang, B. Wu, Ziheng, An improved RRT pathplanning algorithm based on JPS strategy for mobile robot,Journal of Chinese Inertial Technology, 28(6), 2020, 761–768.
Important Links:
Go Back
