References
- S. Alshammrei, S. Boubaker, L. Kolsi ”Improved Dijkstra Algorithm for Mobile Robot Path Planning and Obstacle Avoidance,” Computers, Materials and Continua,” vol. 3, no. 72, 2022, pp. 5939–5954; https://doi.org/10.32604/cmc.2022.028165.
- D. Xiang, H. Lin, J. Ouyang, J. et al., ”Combined Improved A* and Greedy Algorithm for Path Planning of Multi-objective Mobile Robot,” Scientific Reports, vol. 12,2022,13273; https://doi.org/10.1038/s41598-022-17684-0.
- S.A.M. Abgenah and A.J. Azrul, ”Multi-Robot Path Planning Using Dijkstra’s Algorithm with Multilayer Dictionaries,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 10, 2021.
- Q. Ma, et al., ”Path Planning for Mobile Robots Based on Improving Dijkstra Algorithm,” Proc. SPIE 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 127121F, 2023; https://doi.org/10.1117/12.2678830.
- K. Li, et al.,”Research on Reinforcement Learning Based Warehouse Robot Navigation Algorithm in Complex Warehouse Layout,” arXiv, 2024; https://arxiv.org/abs/2411.06128.
- X. Xing, et al., ”Research on Global Path Planning Algorithm for Mobile Robots Based on Improved A*,” Expert Systems with Applications, vol. 243, 2024; https://doi.org/10.1016/j.eswa.2023.122922.
- S. Vaish, Shreyam, and S. Singhal, ”Segmented Approach to Path Planning,” 2020 Sixth International Conference on Parallel, Distributed and Grid Computing” (PDGC), 2020, pp. 266-271; https://doi:10.1109/PDGC50313.2020.9315788.
- A. Ubaidillahand H. Sukri, „Application of Odometry and Dijkstra Algorithm as Navigation and Shortest Path Determination System of Warehouse Mobile Robot,” Journal of Robotics and Control, vol. 4, no. 3, 2023, pp. 413-423; https://doi.org/10.18196/jrc.v4i3.18489.
- Y. Tang, M.A. Zakaria, and M. Younas, „Path Planning Trends for Autonomous Mobile Robot Navigation: ”A Review,” Sensors, vol. 25, no. 4, 2025, 1206; https://doi.org/10.3390/s25041206.
- Z. Duraklı and V. Nabiyev, ”A New Approach Based on Bezier Curves to Solve Path Planning Problems for Mobile Robots,” Journal of Computational Science, vol. 58, 2022, 101540; https://doi.org/10.1016/j.jocs.2021.101540.
- J. Li et al., ”Fusion of Improved A* Algorithm and Dynamic Window Approach for Path Planning,” 2022 China Automation Congress (CAC), 2022, pp. 390-394; doi: 10.1109/CAC57257.2022.10056090.
- B. Wu et al., ”Dynamic Path Planning for Forklift AGV Based on Smoothing A* and Improved DWA Hybrid Algorithm,”, Sensors, vol. 22, 2022, 7079; https://doi.org/10.3390/s22187079.
- Y. Shi, S. Huangand M. Li, ”An Improved Global and Local Fusion Path-Planning Algorithm for Mobile Robots,” Sensors, vol. 24, 2024, 7950; https://doi.org/10.3390/s24247950.
- Y. Zhou and N. Huang, ”Airport AGV Path Optimization Model Based on Ant Colony Algorithm to Optimize Dijkstra Algorithm in Urban Systems,” Sustainable Computing, Informatics and Systems, vol. 35, 2022, 100716; https://doi.org/10.1016/j.suscom.2022.100716.
- R. Kumar et al., ”Performance Comparison of A* Search Algorithm and Hill-Climb Search Algorithm: A Case Study,” Multifaceted approaches for Data Acquisition, Processing & Communication, 1st ed. CRC Press, 2024, pp. 185194,; https://doi.org/10.1201/9781003470939.
- C. Cheng et al., „Path Planning and Obstacle Avoidance for AUV: A Review,” Ocean Engineering, vol. 235, 2021, 109355; https://doi.org/10.1016/j.oceaneng.2021.10935.
- H. Du et al., ”Multi-Objective Loosely Synchronized Search for Multi-Objective MultiAgent Path Finding with Asynchronous Actions,” Journal of Shanghai Jiaotong University (Science), vol. 29, 2024, pp. 667–677; https://doi.org/10.1007/s12204-024-2744-x.
- L. Ait Ben Mouh et al., (2023). ”A Hybrid Approach of Dijkstra’s Algorithm and A* Search, with an Optional Adaptive Threshold Heuristic,” Business Intelligence, R. El Ayachi, M. Fakir, and M. Baslam, eds. Springer, 2023; https://doi.org/10.1007/978-3-031-37872-09.
- P.M. deA. Brasil et al.,”Dijkstra and A* Algorithms for Global Trajectory Planning in the TurtleBot 3 Mobile Robot,” Advances in Intelligent Systems and Computing, Springer, 2020; https://doi.org/10.1007/978-3-030-71187032.
- J. S.S. Pavithra et al., ”Contrastive Analysis of Path Planning Algorithms for Mobile Robots: A Case Study,” 2023 International Conference on Energy, Materials and Communication Engineering (ICEMCE), 2023, pp. 1-6; doi: 10.1109/ICEMCE57940.2023.10434168.
- A.R. Warrier et al., ”Implementation of Classical Path Planning Algorithms for Mobile Robot Navigation: A Comprehensive Comparison,” 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 2022; https://doi.org/10.1109/ic eccme55909.2022.99.88092.
- T. Yoshida et al. „Automated Harvesting by a Dual-arm Fruit Harvesting Robot,” Robomech Journal, vol. 9, no. 19, 2022; https://doi.org/10.1186/s40648-022-00233-9.
- H.S. Yang, ”Analysis and Study on Path Planning Algorithms in the Further Mobile Action,” Journal of Physics, vol. 2824, no. 1, 2024, 012006; https://doi.org/10.1088/17426596/2824/1/0120 06.
- A.A.F. Sakri et al. „Autonomous Person-Following Telepresence Robot Using Monocular Camera and Deep Learning YOLO,” Applications of Modelling and Simulation, Vol. 8, 2024, pp. 101109.
- Robot Simulator CoppeliaSim: Create, Compose, Simulate, Any Robot, Coppelia Robotics.
- Webots Documentation: Adept’s Pioneer 3-DX.