THERMAL SCIENCE

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Mert Bal

AN OVERVIEW OF PATH PLANNING TECHNOLOGIES FOR UNMANNED AERIAL VEHICLES

ABSTRACT
Unmanned aerial vehicles, due to their superior maneuverability and reduced costs can easily perform tasks that are too difficult and complex to be performed with manned aircraft, under all conditions. In order to cope with various obstacles and operate in complex and unstable environmental conditions, the unmanned aerial vehicles must first plan its path. One of the most important problems to investigated in order to find an optimal path between the starting point and the target point of the unmanned aerial vehicles is path planning and choosing the appropriate algorithm. These algorithms find the optimal and shortest path, and also provide a collision-free environment for unmanned aerial vehicles. It is important to have path planning algorithms to calculate a safe path to the final destination in the shortest possible time. However, algorithms are not guaranteed to provide full performance in each path planning situation. Also, each algorithm has some specifications, these specifications make it possible to make them suitable in complex situations. Although there are many studies in path planning literature, this subject is still an active research area considering the high maneuverability of unmanned aerial vehicles. In this study, the most used methods of graph search, sampling-based algorithms and computational intelligence-based algorithms, which have become one of the important technologies for unmanned aerial vehicles and have been the subject of extensive research, are examined and their pros and cons are emphasized. In addition, studies conducted in the field of unmanned aerial vehicles with these algorithms are also briefly mentioned.
KEYWORDS
Unmanned Aerial Vehicle, path planning, sampling-based algorithms, graph search, computational intelligence-based algorithms
PAPER SUBMITTED: 2021-05-05
PAPER REVISED: 2021-11-09
PAPER ACCEPTED: 2022-05-12
PUBLISHED ONLINE: 2022-07-23
DOI REFERENCE: https://doi.org/10.2298/TSCI2204865B
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2022, VOLUME 26, ISSUE Issue 4, PAGES [2865 - 2876]
REFERENCES
  1. Villasenor, C., et al., Ellipsoidal Path Planning for Unmanned Aerial Vehicles, Applied Sciences, 11 (2021), 17, 7997
  2. Puento-Castro, A., et al., A Review of Artificial Intelligence Applied to Path Planning in UAV Swarms, Neural Computing and Applications, 34 (2022), Oct., pp.153-170
  3. Emilien, A., et al., The UAV & Satellite Synergies for Optimal Remote Sensing Applications: A Literature Review, Sci. Remote Sens., 3 (2021), 100019
  4. Balamuralidhar, N., et al., MultEYE: Monitoring System for Real-Time Vehicle Detection, Tracking and Speed Estimation from UAV Imagery an Edge-Computing Platform, Remote Sens., 13 (2021), 573
  5. Erdelj, M., et al., Help from the Sky: Leveraging UAV Disaster Management, IEEE Pervasive Computing, 16 (2017), 1, pp. 24-32
  6. Stocker, C., et al., Measuring Gullies by Synergetic Application of UAV and Close Range Photogrammetry-A Case Study from Andalusia, Spain, Catana, 132 (2015), Sept., pp. 1-11
  7. Yuan, C., et al., A Survey on Technologies for Automatic Forest Fire Monitoring, Detection and Fighting Using Unmanned Aerial Vehicles and Remote Sensing Techniques, Can. J. For. Res., 45 (2015), Mar., pp. 783-792
  8. Majeed, A., Hwang, S. O., Path Planning Method for UAV Based on Constrained Polygonal Space and an Extremely Sparse Waypoint Graph, Applied Sciences, 11 (2021), 5340
  9. Wang, H., Pan, W., Research on UAV Path Planning Algorithms, IOP Publishing, 693 (2021), 012120
  10. Shen, Y., et al., The UAV Path Planning Based on Multi-Stage Constraint Optimization, Drones, 5 (2021), 144
  11. Howlett, J. K., et al., Learning Real Time A* Path Planner for Unmanned Air Vehicle Target Sensing, Brigham Young University Faculty Publications, BYU Scholar Archive, 3 (2006), 3, pp. 108-122
  12. Tsourdos, A., et al., Cooperative Path Planning of Unmanned Aerial Vehicles, John Wiley and Sons, West Sussex, UK, 2011
  13. Zhao, Y., et al., Survey on Computational-Intelligence Based UAV Path Planning, Knowledge Based Systems, 158 (2018), Oct., pp. 54-64
  14. Zhu, W. R., Duan, H. B., Chaotic Predator-Prey Biogeoghraphy-Based Optimization Approach for UCAV Path Planning, Aeorosp. Sci. Technol., 32 (2014), 1, pp. 153-161
  15. Yao, P., Wang, H., Dynamic Adaptive Ant Lion Optimizer Applied to Route Planning for Unmanned Aerial Vehicle, Soft Computing, 21 (2017), Apr., pp. 5475-5488
  16. Chen, X., et al., Path Planning and Cooperative Control for Multiple UAV Based on Consistency Theory and Voronoi Diagram, Proceedings, 29th Chinese Control and Decision Conference (CCDC), Chongqing, China, 2017, pp.881-886
  17. Shen, Z., et al., A Dynamic Airspace Planning Framework with ADS-B Tracks for Manned and Unmanned Aircraft at Low-Altitude Sharing Airspace, Proceedings, IEEE/AIAA 36th Digital Avionics Systems Conference (DASC), St. Petersburg, Fla., USA, 2017, pp. 1-7
  18. Aggarwal, S., Kumar, N., Path Planning Techniques for Unmanned Aerial Vehicles: A Review, Solutions, and Challenges, Computer Communications, 149 (2020), Jan., pp. 270-299
  19. Tong, H., et al., Path Planning of UAV Based on Voronoi Diagram and DPSO, Procedia Engineering, 29 (2012), Dec., pp. 4198-4203
  20. Hart, P. E., et al., A Formal Basis for the Heuristic Determination of Minimum Cost Paths, IEEE Trans. Syst. Sci. Cybern., 4 (1968), 2, pp. 100-107
  21. Dijkstra, E. W., A Note on Two Problems in Connection with Graphs, In Numerical Mathematics, 1 (1959), Dec., pp. 269-271
  22. Wang, H., et al., An Efficient and Robust Improved A* Algorithm for Path Planning, Symmetry, 13 (2021), 2213
  23. Zhang, C., et al., Analysis for UAV Heuristic Tracking Path Planning based on Target Matching, Proceedings, 9th Int. Conf. on Mechanical and Aerospace Engineering (ICMAE), Budapest, Hungary, 2018, pp. 34-39
  24. Gupta, S. K., et al., A Control Algorithm for Co-Operatively Aerial Survey by Using Multiple UAV, Proceedings, 2017 Recent Developments in Control, Automation & Power Engineering (RDCAPE), Noida, India, 2017, pp. 280-285
  25. Li, B. Y., et al., On 3-D Autonomous Delivery Systems: Design and Development, Proceedings, Int. Conf. on Advanced Robotics and Intelligent Systems (ARIS), Taipei, Taiwan, 2017, pp. 1-6
  26. Stentz, A., Optimal and Efficient Path Planning for Partially-Known Environments, Proceedings, Int. Conf. on Robotic and Automation (ICRA), San Diego, Cal., USA, 1994, pp. 3310-3317
  27. Koenig, S., Likhachev, M., Improved Fast Replanning for Robot Navigation in Unknown Terrain, Proceedings, Int. Conf. on Robotics and Automation (ICRA), Washington DC, USA, 2002, pp. 968-975
  28. Likhachev, M., Koenig, S., Incremental A*, Proceedings, 14th Int. Conf. on Neural Information Processing Systems (NIPS), Vancouver, BC, Canada, 2001, pp. 1539-1546
  29. Chao, N., et al., The DL-RRT* Algorithm for Least Dose Path Re-Planning in Dynamic Radioactive Environments, Nuclear Engineering and Technology, 51 (2019), 3, pp. 825-836
  30. Kavraki, L. E., et al., Randomized Query Processing in Robot Planning, Journal Comput.System Sci., 57 (1998), 1, pp. 50-60
  31. Lavalle S. M., Rapidly-Exploring Random Trees: A New Tool for Path Planning, Technical Report, Iowa State University, Ames, Ia., USA, 1998
  32. Ibrahim, N. S. A., Saparudin, F. A., Review on Path Planning Algorithm for Unmanned Aerial Vehicles, Indonesian Journal of Electrical Engineering and Computer Science, 24 (2021), 2, pp. 1017-1026
  33. Cheng, C., et al., Path Planning and Obstacle Avoidance for AUV: A Review, Ocean Engineering, 235 (2021), 109355
  34. Devaurs, D., et al., Optimal Path Planning in Complex Cost Spaces with Sampling-Based Algorithms, IEEE Transactions on Automation Science and Engineering, 13 (2016), 2, pp. 415-424
  35. Lee, D., Shim, D. H., The RRT-Based Path planning for Fixed-Wing UAV with Arrival Time and Approach Direction Constraints, Proceedings, 2014 International Conference on Unmanned Aircraft Systems (ICUAS), Orlando, Fla., USA, 2014, pp. 317-328
  36. Yang, F., et al., Obstacle Avoidance Path Planning for UAV Based on Improved RRT Algorithm, Discrete Dynamics in Nature and Society, 2022 (2022), ID4544499
  37. Dorigo, M., et al., Distributed Optimization by Ant Colonies, Proceedings, 1st European Conference on Artificial Life, Paris, France, 1991, pp. 131-142
  38. Dorigo, M., et al., Ant System: Optimization by A Colony of Cooperating Agents, IEEE Transaction Syst. Man Cybern. Part B (Cybern.), 26 (1996), 1, pp. 29-41
  39. Karur, K., et al., A Survey of Path Planning Algorithms for Mobile Robots, Vehicles, 3 (2021), 3, pp. 448-468
  40. Shafiq, M., et al., A Cluster-Based Hierarchical Approach for the Path Planning of Swarm, Applied Sciences, 11 (2021), 6864
  41. Zaza, T., Richards, A., Ant Colony Optimization for Routing and Tasking Problems for Teams of UAV, Proceedings, Int. Conf. on Control, 2014, Loughborough, UK, pp. 652-655
  42. Gao, C., et al., A Self-Organized Search and Attack Algorithm for Multiple Unmanned Aerial Vehicles, Aerosp. Sci. Technol., 54 (2016), July, pp. 229-240
  43. Zhen, Z., et al., An Intelligent Cooperative Mission Planning Scheme on UAV Swarm in Uncertain Dynamic Environment, Aerosp. Sci. Technol., 100 (2020), 105826
  44. Ma, Z., et al., An UAV Path Planning Method in Complex Mountainous Area Based-on a New Improved Ant Colony Algorithm, Proceedings, International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM), Dublin, Ireland, 2019, pp.125-129
  45. Sharma, A., et al., Path Planning for Multiple Targets Interception by the Swarm of UAV Based on Swarm Intelligence Algorithms: A Review, IETE Technical Review, 2021, On-line first, doi.org/10.1080/02564602.2021.1894250
  46. Huang, C., et al., A New Dynamic Path Planning Approach for Unmanned Aerial Vehicles, Complexity, 2018 (2018), ID8420294
  47. Hao, W., Xu, X., Immune Ant Colony Optimization Network Algorithm for Multi-Robot Path Planning, Proceedings, Int. Conf. on Software Engineering and Service Science, Beijing, China, 2014, pp.1118-1121
  48. Eberhart, R., Kennedy, J., A New Optimizer Using Particle Swarm Theory, Proceedings, Int. Symposium on 6th Micro Machine in Human Science, (MMHS), Nagoya, Japan, 1995
  49. Kennedy, J., Eberhart, R., A Discrete Binary Version of the Particle Swarm Algorithm, Proceedings, IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, Florida, USA, 1997, pp. 4104-4108
  50. Shao, S., et al., Efficient Path Planning for UAV Formation via Comprehensively Improved Particle Swarm Optimization, ISA Transactions, 97 (2020), Feb., pp. 415-430
  51. Dewang, H. S., et al., A Robust Path Planning for Mobile Robot Using Smart Particle Swarm Optimization, Procedia Computer Science, 133 (2018), Jan., pp. 290-297
  52. Oh, G., et al., The PSO-Based Optimal Task Allocation for Cooperative Timing Missions, IFAC-Papers Online, 49 (2016), 17, pp. 314-319
  53. Yihu, W, Siming, W., The UAV Path Planning Based-on Improved Particle Swarm Algorithm, Computer Engineering and Science, 42 (2020), Jan., pp. 1690-1696
  54. Wei, Z., et al., Anti-Collision Technologies for Unmanned Aerial Vehicles: Recent Advances and Future Trends, IEEE Internet of Thing Journal, 9 (2022), 10, pp. 7619-7638
  55. Al-Taharwa, I., et al., A Mobile Robot Path Planning Using Genetic Algorithm in Static Environment, Journal Comput. Sci., 4 (2008), 4, pp. 341-344
  56. Arantes, J. S., et al., Heuristic and Genetic Algorithm Approaches for UAV Path Planning Under Critical Situation, Int. J. Artificial Intelligence Tools, 26 (2017), 1760008
  57. Liu, Y., et al., Path Planning for Unmanned Aerial Vehicle Under Geo-Fencing and Minimum Safe Separation Constraints, Proceedings, 12th World Congress on Intelligent Control and Automation (WCICA), Guilin, China, 2016, pp. 28-31
  58. Oh, S., Suk, J., Evolutionary Design of the Controller for the Search of Area with Obstacles Using Multiple UAV, Proceedings, Int. Conf. on Control Automation and Systems, Gyeonggi-do, South Korea, 2010, pp. 2541-2546
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An overview of path planning technologies for unmanned aerial vehicles

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence