1. Patle BK, Ganesh BL, Anish P et al (2019) A review:on path planning strategies for navigation of mobile robot. Def Technol 15:582-606 2. Gonzalez R, Kloetzer M, Mahulea C (2017) Comparative study of trajectories resulted from cell decomposition path planning approaches. In:2017 21st international conference on system theory, control and computing, Sinaia, pp 49-54 3. Zhang Z, Yang X (2019) Bio-inspired motion planning for reaching movement of a manipulator based on intrinsic tau jerk guidance. Adv Manuf 7:315-325 4. Yang K, Tang Y, Zhang Z (2021) Parameter identification and state-of-charge estimation for lithium-ion batteries using separated time scales and extended Kalman filter. Energies 14(4):1054. https://doi.org/10.3390/en14041054 5. Lee K, Choi D, Kim D (2021) Incorporation of potential fields and motion primitives for the collision avoidance of unmanned aircraft. Appl Sci Basel 11(7):3103. https://doi.org/10.3390/app11073103 6. Guruji AK, Agarwal H, Parsediya DK (2016) Time-efficient A* algorithm for robot path planning. In:The 3rd international conference on innovations in automation and mechatronics engineering, Elsevier, Vallabh Vidhyanagar, pp 144-149 7. Chen C, Cai J, Wang Z et al (2020) An improved A* algorithm for searching the minimum dose path in nuclear facilities. Prog Nucl Energy 126:103394. https://doi.org/10.1016/j.pnucene.2020.103394 8. Chen G, Luo N, Liu D et al (2021) Path planning for manipulators based on an improved probabilistic roadmap method. Robot Comput Integr Manuf 72:102196. https://doi.org/10.1016/j.rcim.2021.102196 9. Sun Y, Zhang C, Sun P et al (2020) Safe and smooth motion planning for mecanum wheeled robot using improved RRT and cubic spline. Arab J Sci Eng 45:3075-3090 10. Wu X, Xu L, Zhen R et al (2019) Biased sampling potentially guided intelligent bidirectional RRT algorithm for UAV path planning in 3D environment. Math Probl Eng 2019:5157403. https://doi.org/10.1155/2019/5157403 11. Montiel O, Orozco-Rosas U, Sepúlveda R (2015) Path planning for mobile robots using bacterial potential field for avoiding static and dynamic obstacles. Expert Syst Appl 42:5177-5191 12. Jose K, Pratihar DK (2016) Task allocation and collision-free path planning of centralized multi-robots system for industrial plant inspection using heuristic methods. Robot Auton Syst 80:34-42 13. Yan F, Liu YS, Xiao JZ (2013) Path planning in complex 3D environments using a probabilistic roadmap method. Int J Autom Comput 10:525-533 14. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46-61 15. Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80-98 16. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51-67 17. Xue J, Shen B (2020) A novel swarm intelligence optimization approach:sparrow search algorithm. Syst Sci Control Eng 8:22-34 18. Xu R, Cao M, Huang M et al (2018) Research on the quasi-TSP problem based on the improved grey wolf optimization algorithm:a case study of tourism. Geogr Geo Inf Sci 34:14-21 19. Tian T, Liu C, Guo Q et al (2018) An improved ant lion optimization algorithm and its application in hydraulic turbine governing system parameter identification. Energies 11:95. https://doi.org/10.3390/en11010095 20. Yildiz AR (2019) A novel hybrid whale-Nelder-Mead algorithm for optimization of design and manufacturing problems. Int J Adv Manuf Technol 105:5091-5104 21. Wang X, Shi H, Zhang C (2016) Path planning for intelligent parking system based on improved ant colony optimization. IEEE Access 8:65267-65273 22. Niu H, Ji Z, Savvaris A et al (2020) Energy efficient path planning for nnmanned surface vehicle in spatially-temporally variant environment. Ocean Eng 196:106766. https://doi.org/10.1016/j.oceaneng.2019.106766 23. Zhang C, Ding S (2021) A stochastic configuration network based on chaotic sparrow search algorithm. Knowl Based Syst 220:106924. https://doi.org/10.1016/j.knosys.2021.106924 24. Liu G, Shu C, Liang Z et al (2021) A modified sparrow search algorithm with application in 3D route planning for UAV. Sensors 21:1224. https://doi.org/10.3390/s21041224 25. Raouf F, Mohammed B, Tamer R et al (2020) Enhancing path quality of real-time path planning algorithms for mobile robots:a sequential linear paths approach. IEEE Access 8:167090-167104 26. Ajeil FH, Ibraheem KI, Sahib MA et al (2018) Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm. Appl Soft Comput 89:106076. https://doi.org/10.1016/j.asoc.2020.106076 27. Li X, Huang Y, Zhou Y et al (2018) Robot path planning using improved artificial bee colony algorithm. In:2018 IEEE 3rd advanced information technology, electronic and automation control conference, Chongqing, China, pp 603-607 28. Zhang D, You X, Liu S et al (2020) Dynamic multi-role adaptive collaborative ant colony optimization for robot path planning. IEEE Access 8:129958-129974 29. Zinage V, Ghosh S (2020) Directional sampling-based generalized shape expansion for accelerated motion planning in 2-D obstacle-cluttered environments. IEEE Contr Syst Lett 5:1067-1072 30. Huang Y, Li Z, Jiang Y et al (2019) Cooperative path planning for multiple mobile robots via HAFSA and an expansion logic strategy. Appl Sci Basel 9:672. https://doi.org/10.3390/app9040672 31. Alaa T, Mohamed E, Aboul EH et al (2019) Intelligent Bézier curve-based path planning model using chaotic particle swarm optimization algorithm. Cluster Comput 22:4745-4766 32. Hassani I, Maalej I, Rekik C (2018) Robot path planning with avoiding obstacles in known environment using free segments and turning points algorithm. Math Probl Eng 2018:2163278. https://doi.org/10.1155/2018/2163278 33. Wang Z, Xiang X (2018) Improved A star algorithm for path planning of marine robot. In:2018 37th Chinese control conference. IEEE, Wuhan, China, pp 5410-5414 |