APF–BUG-BASED INTELLIGENT PATH PLANNING FOR AUTONOMOUS VEHICLE WITH HIGH PRECISION IN COMPLEX ENVIRONMENT, 277-283.

Lingyu Sun, Zhumu Fu, Fazhan Tao, Pengju Si, Shuzhong Song, and Chang Sun

References

  1. [1] U. Lee, S. Yoon, H.C. Shim, P. Vasseur, and C. Demonceaux,Local path planning in a complex environment for self-drivingcar, Proc. IEEE Annual Int. Conf. on Cyber Technology inAutomation Control and Intelligent Systems, Hong Kong, 2014,445–450.
  2. [2] X.X. Fu, Y.H. Jiang, D.X. Huang, K. Huang, J. Wang,and G. Lu, A novel real-time trajectory planning algorithmfor intelligent vehicles, Control and Decision, 30(10), 2015,1751–1758.
  3. [3] M. Fethi, B. Boumedyen, and A.M. Naceur, Distributed pathplanning of a multi-robot system based on the neighborhoodartificial potential field approach, SIMULATION: Transactionsof the Society for Modeling and Simulation International,95(7), 2019, 627–657.
  4. [4] L. Xianxia, L. Zhaoying, and S. Xueling, Research onpath planning of mobile robot with improved APF method,Computer Simulation, 35(4), 2018, 291–294.
  5. [5] J. Song, C. Hao, and J. Su, Path planning for unmannedsurface vehicle based on predictive artificial potential field,International Journal of Advanced Robotic Systems, 17(2),2020, 1–13.
  6. [6] M. Zohaib, S.M. Pasha, N. Javaid, and J. Iqbal, IBA: Intelligentbug algorithm—a novel strategy to navigate mobile robotsautonomously, Communications in Computer and InformationScience, 414(69), 2014, 291–299.
  7. [7] F. Matoui, B. Boussaid, and M.N. Abdelkrim, Distributed pathplanning of a multi-robot system based on the neighborhoodartificial potential field approach, Simulation, 95(7), 2019,637–657.
  8. [8] J. Sun, G. Liu, G. Tian, and J. Zhang, Smart obstacle avoidanceusing a danger index for a dynamic environment, AppliedSciences, 9(8), 2019, 1589.
  9. [9] X.X. Liang, C.Y. Liu, X.L. Song, and Y.K. Zhang, Researchon improved artificial potential field approach in local pathplanning for mobile robot, Computer Simulation, 35(4), 2018,291–361.
  10. [10] S.O. Park, M.C. Lee, and J. Kim, Trajectory planning withcollision avoidance for redundant robots using Jacobian andartificial potential field-based real-time inverse kinematics,International Journal of Control, Automation and Systems,18(8), 2020, 2095–2107.
  11. [11] Q. Wu, Z. Chen, and L. Wang, Real-time dynamic pathplanning of mobile robots: A novel hybrid heuristic optimizationalgorithm, Sensors (Basel, Switzerland), 20(1), 2020, 1–18.
  12. [12] L. Xianxia, L. Zhaoying, and S. Xueling, Research onpath planning of mobile robot with improved APF method,Computer Simulation, 35(4), 2018, 291–294.
  13. [13] M. Fethi, B. Boumedyen, and A.M. Naceur, Distributed pathplanning of a multi-robot system based on the neighborhoodartificial potential field approach, SIMULATION: Transactionsof The Society for Modeling and Simulation International,95(7) 2019, 627–657.
  14. [14] F. Xu, H. Zhao, Z. Nie, X. Zhou, and Z. Tao, Path planningbased on improved artificial potential field method, Proc. Int.Conf. on Artificial Life and Robotics, Kunming, 2020, 592–598.
  15. [15] K.N. McGuire, G.C.H.E. de Croon, and K.A. Tuyls,Comparative study of bug algorithms for robot navigation,Robotics and Autonomous Systems, 10(16), 2019, 1–22.
  16. [16] F.A. Raheem and M.M. Badr, Development of modified pathplanning algorithm using APF (APF) based on PSO forfactors optimization, American Scientific Research Journal forEngineering, Technology, and Sciences, 37(1), 2017, 316—328.

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