COVERAGE PATH PLANNING BASED ON IMPROVED CELLULAR DECOMPOSITION

Jian Zhou, Xiaoyou He, Taihong Lv, Hu He, and Yuhe Qiu

References

  1. [1] H.B. Duan, L. Xin, Y. Xu, and G. Guozhi, Eagle-vision-inspiredvisual measurement algorithm for UAV’s autonomous landing,International Journal of Robotics Automation, 35(2), 2020,94–100.
  2. [2] R. Dong, C. Liu, X. Wang, and X. Han, 3D path planning ofUAVs for transmission lines inspection, International Journalof Robotics and Automation, 35, 2020, 146–158.
  3. [3] Y. Yan, L. Zhiying, H. Ping, and Y. Jinbiao, Rapid selectingUAVs for combat based on three-way multiple attributedecision, International Journal of Robotics and Automation,36, 2021, 296–305.
  4. [4] L. Jing, Y. Wei, and Z. Wei, Research on path planningalgorithm for full coverage of mobile robots, Industrial ControlComputer, 32(12), 2019, 52–54.
  5. [5] M. Chen, Research on path planning algorithms for fullcoverage, Ph.D. Dissertation, Huazhong University of Scienceand Technology, Wuhan, China, 2020.
  6. [6] D. Zhu, C. Tian, B. Sun, and C. Luo, Complete coverage pathplanning of autonomous underwater vehicle based on GBNNalgorithm, Journal of Intelligent & Robotic Systems 94, 2019,237–249.
  7. [7] T. Cabreira, P. Ferreira, C. Di Franco, and G. Buttazzo,Grid-based coverage path planning with minimum energy overirregular-shaped areas with UAVs, Proceeding InternationalConference on Unmanned Aircraft Systems (ICUAS), Atlanta,GA, 2019, 758–767.
  8. [8] A.K. Lakshmanan, R.E. Mohan, B. Ramalingam, L.A. Vu, P.Veerajagadeshwar, K. Tiwari, and M. Ilyas, Complete coveragepath planning using reinforcement learning for tetromino basedcleaning and maintenance robot, Automation in Construction,112, 2020, 103078.
  9. [9] L. Jiang, H. Hongyun, and D. Zuohua, Path planning forintelligent robots based on deep Q-learning with experiencereplay and heuristic knowledge, IEEE/CAA Journal ofAutomatica Sinica, 7(4), 2019, 1179–1189.
  10. [10] D. Hong and S. Xu, Intelligent robot path planning forfull coverage based on fish swarm algorithm, ComputerMeasurement & Control, 31(7), 2023, 222–227.
  11. [11] H. Choset, Coverage of known spaces: The Boustrophedoncellular decomposition, Autonomous Robots, 9(3), 2000,247–253.
  12. [12] W.H. Huang, Optimal line-sweep-based decompositions forcoverage algorithms, IEEE International Conference onRobotics and Automation, 1, 2001, 27–32.
  13. [13] R. B¨ahnemann, N. Lawrance, J.J. Chung, M. Pantic, R.Siegwart, and J. Nieto, Revisiting Boustrophedon coveragepath planning as a generalized traveling salesman problem,Proceedings in Advanced Robotics, Singapore, 2021, 277–290.
  14. [14] S. Singh and E.A. Lodhi, Study of variation in TSP usinggenetic algorithm and its operator comparison, InternationalJournal of Soft Computing and Engineering, 3, 2013, 264–267.
  15. [15] K. Genova and D.P. Williamson, An experimental eval-uation of the best-of-many Christofides’ algorithm forthe traveling salesman problem, Algorithmica, 78(4), 2017,1109–1130.
  16. [16] K. Chaudhari and A. Thakkar, Travelling salesman problem:An empirical comparison between ACO, PSO, ABC, FAand GA, in Emerging Research in Computing, Information,Communication and Applications. Singapore: Springer, 2019,397–405.
  17. [17] P. Vaishnav, N. Choudhary, and K. Jain, Traveling salesmanproblem using genetic algorithm: A survey, International Jour-nal of Scientific Research in Computer Science, Engineeringand Information Technology, 2(3), 2017, 105–108.
  18. [18] X. Yang, Optimization study of facility layout in engineassembly workshop based on genetic simulated annealingalgorithm, Master’s Thesis, Southwest Jiaotong University,Chengdu, China, 2018.

Important Links:

Go Back