Xining Cui, Caiqi Wang, Yi Xiong, and Shiqian Wu


  1. [1] L. Zhang, Z. Liu, and X. Qin, Standards of measurement anddevelopmental challenges in path planning for manipulator,International Journal of Robotics and Automation, 38(3), 2023,208–217.
  2. [2] J.J. Potter, K.L. Sorensen, and W.E. Singhose, Efficientmethod for generating pick-and-place trajectory over obstacles,International Journal of Robotics and Automation, 10(2), 2010,6–24.
  3. [3] L. Deng, X. Ma, J. Gu, Y. Li, Z. Xu, and Y. Wang, Artificialimmune network-based multi-robot formation path planningwith obstacle avoidance, International Journal of Robotics andAutomation, 31(3), 2016, 225–232.
  4. [4] J. Chen, F. Ling, Y. Zhang, T. You, Y. Liu, and X. Du,Coverage path planning of heterogeneous unmanned aerialvehicles based on ant colony system, Swarm and EvolutionaryComputation, 69, 2022, 101005.
  5. [5] N. Wang, Y. Zhang, C.K. Ahn, and Q. Xu, Autonomous pilotof unmanned surface vehicles: Bridging path planning andtracking, IEEE Transactions on Vehicular Technology, 71(3),2021, 2358–2374.
  6. [6] L. Sun, Z. Fu, F. Tao, P. Si, S. Song, and C. Sun,APF–BUG-BASED intelligent path planning for autonomousvehicle with high precision in complex environment, Inter-national Journal of Robotics and Automation, 38(4), 2023,277–283.
  7. [7] W. Wenna, D. Weili, H. Changchun, Z. Heng, F. Haibing, andY. Yao, A digital twin for 3D path planning of large-spancurved-arm gantry robot, Robotics and Computer-IntegratedManufacturing, 76, 2022, 102330.
  8. [8] T. Xue, L. Li, L. Shuang, D. Zhiping, and P. Ming,Path planning of mobile robot based on improved antcolony algorithm for logistics, Mathematical Biosciences andEngineering, 18(4), 2021, 3034–3045.
  9. [9] X. Liu, D. Jiang, B. Tao, G. Jiang, Y. Sun, J. Kong, X. Tong,G. Zhao, and B. Chen, Genetic algorithm-based trajectoryoptimization for digital twin robots, Frontiers in Bioengineeringand Biotechnology, 9, 2022, 1433.
  10. [10] Y. Zhao, Y. Wang, J. Zhang, X. Liu, Y. Li, S. Guo, X.Yang, and S. Hong, Surgical GAN: Towards real-time pathplanning for passive flexible tools in endovascular surgeries,Neurocomputing, 500, 2022, 567–580.
  11. [11] K. Wu, B. Li, Y. Zhang, and X. Dai, Review of researchon path planning and control methods of flexible steerableneedle puncture robot, Computer Assisted Surgery, 27(1),2022, 91–112.
  12. [12] Y. Wang, Z. He, D. Cao, L. Ma, K. Li, L. Jia, and Y. Cui,Coverage path planning for kiwifruit picking robots based ondeep reinforcement learning, Computers and Electronics inAgriculture, 205, 2023, 107593.
  13. [13] G. Lin, L. Zhu, J. Li, X. Zou, and Y. Tang, Collision-freepath planning for a guava-harvesting robot based on recurrent14deep reinforcement learning, Computers and Electronics inAgriculture, 188, 2021, 106350.
  14. [14] C. He, C. Deng, N. Li, and Z. Miao, Design of vision controlsystem of tomato picking robot, Proc. 2021 40th ChineseControl Conf. (CCC), Shanghai, 2021, 4267–4271.
  15. [15] K. Karur, N. Sharma, C. Dharmatti, and J.E. Siegel, A surveyof path planning algorithms for mobile robots, Vehicles, 3(3),2021, 448–468.
  16. [16] S. Katoch, S.S. Chauhan, and V. Kumar, A review on geneticalgorithm: Past, present, and future, Multimedia Tools andApplications, 80, 2021, 8091–8126.
  17. [17] Y. Liang and L. Wang, Applying genetic algorithm and antcolony optimization algorithm into marine investigation pathplanning model, Soft Computing, 24, 2020, 8199–8210.
  18. [18] Y. Ding, B. Xin, L. Dou, J. Chen, and B.M. Chen, A memeticalgorithm for curvature-constrained path planning of messengerUAV in air-ground coordination, IEEE Transactions onAutomation Science and Engineering, 19(4), 2021, 3735–3749.
  19. [19] J. Li, T. Sun, X. Huang, L. Ma, Q. Lin, J. Chen, and V.C. Leung,A memetic path planning algorithm for unmanned air/groundvehicle cooperative detection systems, IEEE Transactionson Automation Science and Engineering, 19(4), 2021,2724–2737.
  20. [20] C. Huang, X. Zhou, X. Ran, J. Wang, H. Chen, and W.Deng, Adaptive cylinder vector particle swarm optimizationwith differential evolution for UAV path planning, EngineeringApplications of Artificial Intelligence, 121, 2023, 105942.
  21. [21] Z. He, X. Tang, Q. Shen, C. Duan, and C. Jia, Optimisationof a six-degree-of-freedom robot trajectory based on improvedmulti-objective PSO algorithm, International Journal ofRobotics and Automation, 38(3), 2023, 218–230.
  22. [22] Q. Luo, H. Wang, Y. Zheng, and J. He, Research on pathplanning of mobile robot based on improved ant colonyalgorithm, Neural Computing and Applications, 32, 2020,1555–1566.
  23. [23] C. Miao, G. Chen, C. Yan, and Y. Wu, Path planningoptimization of indoor mobile robot based on adaptive antcolony algorithm, Computers and Industrial Engineering, 156,2021, 107230.
  24. [24] O. Khatib, Real-time obstacle avoidance for manipulators andmobile robots, The International Journal of Robotics Research,5(1), 1986, 90–98.
  25. [25] Y. Huang, H. Ding, Y. Zhang, H. Wang, D. Cao, N. Xu,and C. Hu, A motion planning and tracking frameworkfor autonomous vehicles based on artificial potential fieldelaborated resistance network approach, IEEE Transactionson Industrial Electronics, 67(2), 2019, 1376–1386.
  26. [26] G. Tang, C. Tang, C. Claramunt, X. Hu, and P. Zhou, GeometricA-star algorithm: An improved A-star algorithm for AGVpath planning in a port environment, IEEE Access, 9, 2021,59196–59210.
  27. [27] S. Koenig and M. Likhachev, Fast replanning for navigationin unknown terrain, IEEE Transactions on Robotics, 21(3),2005, 354–363.
  28. [28] G. Chen, N. Luo, D. Liu, Z. Zhao, and C. Liang, Path planningfor manipulators based on an improved probabilistic roadmapmethod, Robotics and Computer-Integrated Manufacturing, 72,2021, 102196.
  29. [29] S.M. LaValle and J.J. Kuffner Jr., Randomized kinodynamicplanning, The International Journal of Robotics Research,20(5), 2001, 378–400.
  30. [30] L.R. Celsi and M.R. Celsi, On edge-lazy RRT collision checkingin sampling-based motion planning, International Journal ofRobotics and Automation, 36(4), 2021, 240–245.
  31. [31] S. Karaman, M.R. Walter, A. Perez, E. Frazzoli, and S. Teller,Anytime motion planning using the RRT, Proc. 2011 IEEEInternational Conf. on Robotics and Automation, Shanghai,2011, 1478–1483.
  32. [32] C. Urmson and R. Simmons, Approaches for heuristicallybiasing RRT growth, Proc. 2003 IEEE/RSJ InternationalConf. on Intelligent Robots and Systems (IROS 2003) (Cat.No.03CH37453), Las Vegas, NV, , 2003, 1178–1183.
  33. [33] J.D. Gammell, S.S. Srinivasa, and T.D. Barfoot, InformedRRT: Optimal sampling-based path planning focused viadirect sampling of an admissible ellipsoidal heuristic, Proc.2014 IEEE/RSJ International Conf. on Intelligent Robots andSystems, Chicago, IL, 2014, 2997–3004.
  34. [34] F. Islam, J. Nasir, U. Malik, Y. Ayaz, and O. Hasan, RRT-smart: Rapid convergence implementation of RRT towardsoptimal solution, Proc. 2012 IEEE International Conf. onMechatronics and Automation, Chengdu, 2012, 1651–1656.
  35. [35] M. Jordan and A. Perez, Optimal bidirectional rapidly-exploring random trees, Technical Report MIT-CSAIL-TR-2013-021, Computer Science and Artificial IntelligenceLaboratory, Cambridge, 2013.
  36. [36] A.H. Qureshi and Y. Ayaz, Intelligent bidirectional rapidly-exploring random trees for optimal motion planning in complexcluttered environments, Robotics and Autonomous Systems,68, 2015, 1–11.
  37. [37] Z. Tahir, A.H. Qureshi, Y. Ayaz, and R. Nawaz, Potentiallyguided bidirectionalized RRT for fast optimal path planningin cluttered environments, Robotics and Autonomous Systems,108, 2018, 13–27.
  38. [38] N. Chao, Y.-k. Liu, H. Xia, A. Ayodeji, and L. Bai, Grid-basedRRT for minimum dose walking path-planning in complexradioactive environments, Annals of Nuclear Energy, 115, 2018,73–82.
  39. [39] J.J. Kuffner and S.M. LaValle, RRT-connect: An efficientapproach to single-query path planning, Proc. 2000 ICRA.Millennium Conf., San Francisco, CA, 2000, 995–1001.
  40. [40] J. Nasir, F. Islam, U. Malik, Y. Ayaz, O. Hasan, M. Khan,and M.S. Muhammad, RRT-SMART: A rapid convergenceimplementation of RRT, International Journal of AdvancedRobotic Systems, 10(7), 2013, 299.
  41. [41] I.-B. Jeong, S.-J. Lee, and J.-H. Kim, Quick-RRT: Triangularinequality-based implementation of RRT with improvedinitial solution and convergence rate, Expert Systems withApplications, 123, 2019, 82–90.
  42. [42] S. Karaman and E. Frazzoli, Incremental sampling-basedalgorithms for optimal motion planning, Robotics Science andSystems VI, 104(2), 2010.

Important Links:

Go Back