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Memorized Rapidly Exploring Random Tree Optimization (MRRTO): An Enhanced Algorithm for Robot Path Planning Cover

Memorized Rapidly Exploring Random Tree Optimization (MRRTO): An Enhanced Algorithm for Robot Path Planning

Open Access
|Mar 2024

References

  1. Yakoubi, M. A., M. T. Laskri. The Path Planning of Cleaner Robot for Coverage Region Using Genetic Algorithms. – Journal of Innovation in Digital Ecosystems, Vol. 3, 2016, No 1, pp. 37-43. DOI: 10.1016/j.jides.2016.05.004.
  2. Stączek, P., J. Pizoń, W. Danilczuk, A. Gola. A Digital Twin Approach for the Improvement of an Autonomous Mobile Robots (AMR’s) Operating Environment – A Case Study. – Sensors, Vol. 21, 2021, No 23, 7830. DOI: 10.3390/s21237830.
  3. Baek, D., M. Hwang, H. Kim, D. Kwon. Path Planning for Automation of Surgery Robot Based on Probabilistic Roadmap and Reinforcement Learning. – In: Proc. of 2018 15th International Conference on Ubiquitous Robots (UR), Honolulu, USA, 2018, pp. 342-347. DOI: 10.1109/urai.2018.8441801.
  4. Ortiz, E., B. Andres, F. J. L. Fraile, R. Poler, A. Ortiz. Fleet Management System for Mobile Robots in Healthcare Environments. – Journal of Industrial Engineering and Management, Vol. 14, 2021, No 1, 55. DOI: 10.3926/jiem.3284.
  5. Du, J., P. Zheng, Z. Xie, Y. Yang, H. Chu, G. Yu. Research on Path Planning Algorithm Based on Security Patrol Robot. – In: Proc. of 2016 IEEE International Conference on Mechatronics and Automation, Harbin, China, 2016, pp. 1030-1035. DOI: 10.1109/ICMA.2016.7558704.
  6. Denk, M., S. Bickel, P. Steck, S. Goetz, H. Völkl, S. Wartzack. Generating Digital Twins for Path-Planning of Autonomous Robots and Drones Using Constrained Homotopic Shrinking for 2D and 3D Environment Modeling. – Applied Sciences, Vol. 13, 2022, No 1. DOI: 10.3390/app13010105.
  7. Muhammad, A., M. K. Ali, S. Turaev, I. H. Shanono, F. Hujainah, M. N. M. Zubir, M. A. Faiz, E. R. M. Faizal, R. Abdulghafor. Novel Algorithm for Mobile Robot Path Planning in Constrained Environment. – Computers, Materials & Continua, Vol. 71, 2022, No 2, pp. 2697-2719. DOI: 10.32604/cmc.2022.020873.
  8. Patle, B. K., B. L. Ganesh, A. Pandey, D. R. Parhi, A. Jagadeesh. A Review: On Path Planning Strategies for Navigation of Mobile Robot. – Defence Technology, Vol. 15, 2019, No 4, pp. 582-606. DOI: 10.1016/j.dt.2019.04.011.
  9. Raafat, S. M., F. A. Raheem. Intelligent and Robust Path Planning and Control of Robotic Systems. – In: Springer eBooks, Springer Nature, 2017, pp. 291-317. DOI: 10.1007/978-3-319-43901-3_13.
  10. Xue, Y., J. Q. Sun. Solving the Path Planning Problem in Mobile Robotics with the Multi-Objective Evolutionary Algorithm. – Applied Sciences, Vol. 8, 2018, No 9, p. 1425, DOI: 10.3390/app8091425.
  11. Sadiq, A. T., A. N. Hasan. Robot Path Planning Based on PSO and D Algorithms in a Dynamic Environment. – In: Proc. of International Conference on Current Research in Computer Science and Information Technology (ICCIT’17), 2017. DOI: 10.1109/crcsit.2017.7965550.
  12. Ahmed, T. S., F. A. Raheem, N. Abbas. Ant Colony Algorithm Improvement for Robot Arm Path Planning Optimization Based on D* Strategy. – International Journal of Mechanical &Mechatronics Engineering, Vol. 21, 2017, No 1, pp. 96-111, 2021
  13. Raheem, F. A., S. M. Raafat, S. M. Mahdi. Robot Path-Planning Research Applications in Static and Dynamic Environments. – In: J. N. Furze, S. Eslamian, S. M. Raafat, K. Swing, Eds. Earth Systems Protection and Sustainability. Cham, Springer, 2022. DOI: 10.1007/978-3-030-85829-2_12.
  14. Wang, D., S. Chen, Y. Zhang, L. Liu. Path Planning of Mobile Robot in Dynamic Environment: Fuzzy Artificial Potential Field and Extensible Neural Network. – Artificial Life and Robotics, Vol. 26, 2021, No 1, pp. 129-139. DOI: 10.1007/s10015-020-00630-6.
  15. Raheem, F. A., U. I. Hameed. Interactive Heuristic D* Path Planning Solution Based on PSO for Two-Link Robotic Arm in Dynamic Environment. – World Journal of Engineering and Technology, Vol. 7, 2019, No 1, pp. 80-99. DOI: 10.4236/wjet.2019.71005.
  16. Klemm, S. O., J. Oberlander, A. Hermann, A. Roennau, T. Schamm, J. M. Zollner, R. Dillmann. – RRT-Connect: Faster, Asymptotically Optimal Motion Planning, 2015. DOI: 10.1109/robio.2015.7419012.
  17. He, D., H. Wang, P. Li. Robot Path Planning Using Improved Rapidly-Exploring Random Tree Algorithm. – In: Proc. of 2018 IEEE Industrial Cyber-Physical Systems (ICPS), St. Petersburg, Russia, 2018, pp. 181-186. DOI: 10.1109/icphys.2018.8387656.
  18. Tian, L., Z. Zhang, C. Zheng, Y. Tian, Y. Zhao, Z. Wang, Z, Y. Qin. An Improved Rapidly-Exploring Random Trees Algorithm Combining Parent Point Priority Determination Strategy and Real-Time Optimization Strategy for Path Planning. – Sensors, Vol. 21, 2021, No 20. DOI: 10.3390/s21206907.
  19. Jin, H., W. Cui, H. Fu. Improved RRT-Connect Algorithm for Urban Low-Altitude UAV Route Planning. – Journal of Physics, Vol. 1948, 2021, No 1. DOI: 10.1088/1742-6596/1948/1/012048.
  20. Kang, J. U., D. W. Lim, Y. S. Choi, W. D. Jang, J. W. Jung. Improved RRT-Connect Algorithm Based on Triangular Inequality for Robot Path Planning. – Sensors, Vol. 21, 2021, No 2. DOI: 10.3390/s21020333.
  21. Zhang, Y., H. Jiang, X. Zhong, X. Zhong, J. Zhao. MI-RRT-Connect Algorithm for Quadruped Robotics Navigation with Efficiently Path Planning. – Journal of Physics, Vol. 2402, 2022, No 1. DOI: 10.1088/1742-6596/2402/1/012014.
  22. Ding, J., Y. Zhou, X. Huang, K. Song, S. Lu, L. Wang. An Improved RRT* Algorithm for Robot Path Planning Based on Path Expansion Heuristic Sampling. – Journal of Computational Science, Vol. 67, 2023. DOI: 10.1016/j.jocs.2022.101937.
  23. Yamashita, T., T. Nishida. Path Planning Using Multilayer Neural Network and Rapidly-Exploring Random Tree. – In: Proc. of 18th International Conference on Control, Automation and Systems, Korea, 2018. https://api.semanticscholar.org/CorpusID:210705126.
  24. Kang, J. U., Y. Choi, J. Jung. A Method of Enhancing Rapidly-Exploring Random Tree Robot Path Planning Using Midpoint Interpolation. – Applied Sciences, Vol. 11, 2021, No 18. DOI: 10.3390/app11188483.
  25. Lonklang, A., J. Botzheim. Improved Rapidly Exploring Random Trees with Bacterial Mutation and Node Deletion for Offline Path Planning of Mobile Robots. – Electronics, Vol. 11, 2022, No 9. DOI: 10.3390/electronics11091459.
  26. Pohan, M. A. R., J. Utama. Efficient Sampling-Based for Mobile Robot Path Planning in a Dynamic Environment Based on the Rapidly-Exploring Random Tree and a Rule-Template Sets. – International Journal of Engineering. Transactions A: Basics, Vol. 36, 2023, No 4, pp. 797-806. DOI: 10.5829/ije.2023.36.04a.16.
  27. Kang, J. U., Y. Choi, J. Jung. A Method of Enhancing Rapidly-Exploring Random Tree Robot Path Planning Using Midpoint Interpolation. – Applied Sciences, Vol. 11, 2021, No 18. DOI: 10.3390/app11188483.
  28. Muhammad, S., Y. Zhou. Path Planning for EVs Based on RA-RRT* Model. – Frontiers in Energy Research, Vol. 10, 2023. DOI: 10.3389/fenrg.2022.996726.
  29. Seif, R. Mobile Robot Path Planning by RRT* in Dynamic Environments. – I. J. Intelligent Systems and Applications, Vol. 5, 2015, pp. 24-30. DOI: 10.5815/ijisa.2015.05.04.
  30. Rasheed, A. A., A. S. Al-Araji, M. N. Abdullah. Static and Dynamic Path Planning Algorithms Design for a Wheeled Mobile Robot Based on a Hybrid Technique. – International Journal of Intelligent Engineering and Systems, Vol. 15, 2022, No 4, pp. 167-181. http://dx.doi.org/10.22266/ijies2022.0831.16.
  31. Zhang, Z., D. Wu, J. Gu, F. Li. A Path-Planning Strategy for Unmanned Surface Vehicles Based on an Adaptive Hybrid Dynamic Stepsize and Target Attractive Force-RRT Algorithm. – Journal of Marine Science and Engineering, Vol. 7, 2019, No 5. DOI: 10.3390/jmse7050132.
  32. LaValle, S. M. Rapidly-Exploring Random Trees: A New Tool for Path Planning. – The Annual Research Report, Computer Science Dept., Iowa State University, October 1998.
  33. Kuffner, J. J., S. M. LaValle. RRT-Connect: An Efficient Approach to Single-Query Path Planning. – In: Proc. of 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065), San Francisco, CA, USA, Vol. 2, 2000, pp. 995-1001. DOI: 10.1109/ROBOT.2000.844730.
  34. Karaman, S., M. R. Walter, A. Perez, E. Frazzoli, S. Teller. Anytime Motion Planning Using the RRT*. – In: Proc. of IEEE International Conference on Robotics and Automation, Shanghai, China, 2011, pp. 1478-1483. DOI: 10.1109/ICRA.2011.5980479.
  35. Gammell, J. D., S. S. Srinivasa, T. D. Barfoot. Informed RRT*: Optimal Sampling-Based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic. – In: Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, IL, USA, 2014, pp. 2997-3004. DOI: 10.1109/IROS.2014.6942976.
  36. Nasir, J., F. Islam, U. Malik, Y. Ayaz, O. Hasan, M. Khan, M. S. Muhammad. RRT*-SMART: A Rapid Convergence Implementation of RRT*. – International Journal of Advanced Robotic Systems, Vol. 10, 2013, No 7, 299. DOI: 10.5772/56718.
  37. Fragkopoulos, C., A. Graeser. Extended RRT Algorithm with Dynamic N-Dimensional Cuboid Domains. – In: Proc. of 12th International Conference on Optimization of Electrical and Electronic Equipment, Brasov, Romania, 2010, pp. 851-857. DOI: 10.1109/OPTIM.2010.5510401.
  38. Zhou, M., N. Gao. Research on Optimal Path Based on Dijkstra Algorithms. – In: Proc. of 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT’19), Advances in Computer Science Research, 2019. DOI: 10.2991/icmeit-19.2019.141.
  39. Suwoyo, H., A. Adriansyah, J. Andika, A. Ubaidillah, M. F. Zakaria. An Integrated RRT*SMART-A* Algorithm for Solving the Global Path Planning Problem in a Static Environment. – IIUM Engineering Journal, Vol. 24, 2023, No 1, pp. 269-284. DOI: 10.31436/iiumej.v24i1.2529.
  40. Poli, R., J. Kennedy, T. Blackwell. Particle Swarm Optimization. – Swarm Intelligence, Vol. 1, 2007, No 1, pp. 33-57. DOI: 10.1007/s11721-007-0002-0.
  41. Dorigo, M., M. Birattari, T. Stützle. Ant Colony Optimization. Chapman and Hall/CRC, 2007, pp. 417-430. DOI: 10.1201/9781420010749-33.
DOI: https://doi.org/10.2478/cait-2024-0011 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702
Language: English
Page range: 190 - 204
Submitted on: Jan 22, 2024
Accepted on: Feb 7, 2024
Published on: Mar 23, 2024
Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2024 Dena Kadhim Muhsen, Ahmed T. Sadiq, Firas Abdulrazzaq Raheem, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.