MOBİL ROBOTLAR İÇİN YAPAY POTANSİYEL ALAN TABANLI YOL PLANLAMA ALGORİTMALARININ ORTAK SORUNLARINA ETKİLİ ÇÖZÜMLER

Öz

Otonom Yol Planlaması (OYP) yeteneği bir mobil robotun otonom seviyesini belirleyen başlıca faktörlerden birisidir. Literatürde her ne kadar faklı yöntemler otonom yol planlaması için kullanılıyor olsa dahi, Yapay Potansiyel Alanlara (YPA) dayalı yol planlaması yaklaşımı modelleme kolaylığı ve hesaplama performansı ile oldukça yaygın bir kullanım alanına sahiptir. Grid tabanlı bir yol planlaması yaklaşımı olan YPA tabanlı OYP, genellikle çok sayıda temel hareketi modelleyen itici ve çekici yönde bileşenin belirli bir denklem ile bir araya getirilmesi ve bu potansiyel alanın gradientinin hesaplanarak vektör alanın elde edilmesi ile gerçekleştirilir. Bu çalışma kapsamında YPA tabanlı OYP amacıyla kullanılan temel modeller incelenmiş, nasıl gerçekleştirildiğiklerine değinilmiş ve bileşke potansiyel alanın nasıl üretildiğinden bahsedilmiştir. Her ne kadar YPA tabanlı OYP yaklaşımlarının avantajları olsa dahi yerel minimum, çok yakın konumlandırılmış engeller, osilasyon ve engellere çok yakın konumlandırılmış hedef gibi problemleri de vardır. Çalışma kapsamında bu problemlerin teker teker tanımlamaları yapılmış ve literatürde bu problemlerin çözümü için önerilen yaklaşımlara detaylı olarak değinilmiştir. Sonuç olarak etkin bir YPA tabanlı OYP çözümü elde etmek için kıvrımsız vektör alanı üretilmesi, temel potansiyel alanların üssel fonksiyonlar ile sınırlandırılması, sanal potansiyel alanların kullanılması ve harmonik fonksiyonlar ile modellemelerin gerçekleştirilmesi gerektiği görülmüştür.

Anahtar Kelimeler

• yol planlaması
• otonom sistemler
• İHA
• yapay potansiyel alanlar

EFFECTIVE SOLUTIONS FOR COMMON PROBLEMS OF ARTIFICIAL POTENTIAL FIELD BASED PATH PLANNING ALGORITHMS FOR MOBILE ROBOTS

Öz

Abstract Autonomous Path Planning (APP) capability is one of the main factors determining the autonomous level of a mobile robot. Although different methods are used for APP in the literature, the path planning approach based on Artificial Potential Fields (APF) has a very common usage area with its modeling ease and computational performance. APF-based APP, which is a grid-based path planning approach, is usually performed by combining a repulsive and attractive component that models many basic motions with a certain equation and calculating the gradient of this potential field to obtain the vector field. In this study, the basic models used for APF-based APP are examined, and how they are realized and how the resultant potential field is produced are mentioned. Although APF-based APP approaches have advantages, they also have problems such as local minimum, obstacles positioned too close, oscillation, and targets positioned too close to obstacles. Within the scope of the study, these problems were defined one by one and the approaches suggested in the literature for the solution of these problems were mentioned in detail. As a result, it has been seen that to obtain an effective APF-based APP solution, it is necessary to generate a convolutional vector field, limit the fundamental potential fields with exponential functions, use virtual potential fields and perform models with harmonic functions.

Anahtar Kelimeler

• path planning
• autonomous systems
• UAV
• artificial potential fields
• yol planlaması
• otonom sistemler
• İHA
• yapay potansiyel alanlar

Kaynakça

1. Cetin, O. (2015). Parallel programming based path planning for multi autonomous unmanned vehicles. [Doctoral dissertation, Turkish Air Force Academy]. Dissertation ID: 397130. https://tez.yok.gov.tr/UlusalTezMerkezi/tezSorguSonucYeni.jsp
2. Cetin, O., & Yilmaz, G. (2014). GPGPU accelerated real-time potential field based formation control for Unmanned Aerial Vehicles. 2014 International Conference on Unmanned Aircraft Systems (ICUAS), 2014, 103-114. https://doi.org/10.1109/ICUAS.2014.6842245
3. Cetin, O., & Yilmaz, G. (2016). Real-time Autonomous UAV Formation Flight with Collision and Obstacle Avoidance in Unknown Environment. Journal of Intelligent & Robotic Systems, 84, 415–433. https://doi.org/10.1007/s10846-015-0318-8
4. Chen, J., Ling, F., Zhang, Y., You, T., Liu, Y., & Du, X. (2022). Coverage path planning of heterogeneous unmanned aerial vehicles based on ant colony system. Swarm and Evolutionary Computation, 69. https://doi.org/10.1016/j.swevo.2021.101005
5. Choi, D., Lee, K., & Kim, D. (2020). Enhanced Potential Field-Based Collision Avoidance for Unmanned Aerial Vehicles in a Dynamic Environment. AIAA Scitech 2020 Forum, Detect and Avoid Technologies for UAS. https://doi.org/10.2514/6.2020-0487 Dai, J., Qiu, J., Yu, H., Zhang, C., Wu, Z., & Gao, Q. (2022). Robot Static Path Planning Method Based on Deterministic Annealing. Machines 2022, 10 (8), 600. https://doi.org/10.3390/machines10080600
6. Duhé, JF., Victor, S., & Melchior, P. (2021). ContribUtions on Artificial Potential Field Method for Effective Obstacle Avoidance. Fractional Calculus and Applied Analysis, 24, 421– 446. https://doi.org/10.1515/fca-2021-0019
7. Faria, G., Romero, R. A. F., Prestes, E., & Idiart, M. A. P. (2004). Comparing harmonic functions and potential fields in the trajectory control of mobile robots. IEEE Conference on Robotics, Automation and Mechatronics, 2004, 2, 762-767. https://doi.org/10.1109/RAMECH.2004.1438014
8. Feng, S., Qian, Y., & Wang, Y. (2021). Collision avoidance method of autonomous vehicle based on improved artifial potential field algorithm. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 235 (14), 3416-3430. https://doi.org/10.1177/09544070211014319

9. GE, S. S., & Cui, Y. J. (2000). New potential functions for mobile robot path planning. IEEE Transactions on Robotics and Automation, 16 (5), 615-620. https://doi.org/10.1109/70.880813
10. Heidari, H., & Saska, M. (2021). Collision-free trajectory planning of multi-rotor UAVs in a wind condition based on modified potential field. Mechanism and Machine Theory, 156, 104140 https://doi.org/10.1109/STA.2015.7505223
11. Iswanto, I., Ma’arif, A., Wahyunggoro, O., & Cahyadi, A. I. (2019). Artificial Potential Field Algorithm Implementation for Quadrotor Path Planning. International Journal of Advanced Computer Science and Applications (IJACSA), 2018, 10 (8). https://doi.org/10.14569/IJACSA.2019.0100876
12. Khatib, O. (1985). Real-time obstacle avoidance for manipulators and mobile robots. Proceedings. 1985 IEEE International Conference on Robotics and Automation, 500- 505, https://10.1109/ROBOT.1985.1087247.
13. Klančar, G., Zdešar, A., & Krishnan, M. (2022). Robot Navigation Based on Potential Field and Gradient Obtained by Bilinear Interpolation and a Grid-Based Search. Sensors 2022, 22 (9), 3295. https://doi.org/10.3390/s22093295
14. Koren, Y., & Borenstein, J. (1991). Potential field methods and their inherent limitations for mobile robot navigation. Proceedings. 1991 IEEE International Conference on Robotics and Automation, 2, 1398-1404. https://doi.org/10.1109/ROBOT.1991.131810
15. Lamini, C., Benhlima, S., & Elbekri, Ali. (2018). Genetic Algorithm Based Approach for Autonomous Mobile Robot Path Planning. Procedia Computer Science, 127, 180-189. https://doi.org/10.1016/j.procs.2018.01.113
16. Li, C., Cui, G., & Lu, H. (2010). The design of an obstacle avoiding trajectory in unknown environment using potential fields. The 2010 IEEE International Conference on Information and Automation, 2010, 2050-2054, https://10.1109/ICINFA.2010.5512513.
17. Li, G., Yamashita, A., Asama, H., & Tamura, Y. (2012). An efficient improved artificial potential field based regression search method for robot path planning. 2012 IEEE International Conference on Mechatronics and Automation, 2021, 1227-1232 https://10.1109/ICMA.2012.6283526.
18. Li, H., Gong, D., & Yu, J. (2021). An obstacles avoidance method for serial manipulator based on reinforcement learning and Artificial Potential Field. Int J Intell Robot Appl, 5, 186– 202. https://doi.org/10.1007/s41315-021-00172-5
19. Liu, G., Du, Y., Li, X., & Dou, S. (2021). Research on Path Planning of Logistics Storage Robot Based on Fuzzy Improved Artificial Potential Field Method. In: Liu, S., Bohács, G., Shi, X., Shang, X., Huang, A. (eds) LISS 2020, 265. https://doi.org/10.1007/978-981-33- 4359-7_19
20. Matoui, F., Boussaid, B., & Abdelkrim, M. N. (2015). Local minimum solution for the potential field method in multiple robot motion planning task. 2015 16th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), 452-457. https://doi.org/10.1109/STA.2015.7505223
21. Rezaee, H., & Abdollahi, F. (2012). Adaptive artificial potential field approach for obstacle avoidance of unmanned aircrafts. 2012 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2012, 1-6. https://10.1109/AIM.2012.6305268.
22. Rybus, T., Wojtunik, M., & Basmadji, F. L. (2022). Optimal collision-free path planning of a free-floating space robot using spline-based trajectories. Acta Astronautica, 190, 395- 408. https://doi.org/10.1016/j.actaastro.2021.10.012
23. Sabudin, E. N., Omar, R., Debnath, S. K., & Sulong, M. S. (2021). Efficient robotic path planning algorithm based on artificial potential field. International Journal of Electrical and Computer Engineering (IJECE), 11 (6), 4840-4849. http://doi.org/10.11591/ijece.v11i6.pp4840-4849
24. Szczepanski, R., Bereit, A., & Tarczewski, T. (2021). Efficient Local Path Planning Algorithm Using Artificial Potential Field Supported by Augmented Reality. Energies 2021, 14 (20), 6642. https://doi.org/10.3390/en14206642
25. Tang, J., Sun, J., Lu, C., & Lao, S. (2019). Optimized artificial potential field algorithm to multiunmanned aerial vehicle coordinated trajectory planning and collision avoidance in three-dimensional environment. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 233 (16), 6032-6043. https://doi.org/10.1177/095441001984443
26. Tang, L., Dian, S., Gu, G., Zhou, K., Wang, S., & Feng, X. (2010). A novel potential field method for obstacle avoidance and path planning of mobile robot. 2010 3rd International Conference on Computer Science and Information Technology, 2010, 633-637. https://10.1109/ICCSIT.2010.5565069.
27. Wang, H., Li, G., Hou, J., Chen, L., & Hu, N. (2022). A Path Planning Method for Underground Intelligent Vehicles Based on an Improved RRT* Algorithm. Electronics 2022, 11 (3), 294. https://doi.org/10.3390/electronics11030294
28. Wang, S., Lin, F., Wang, T., Zhao, Y., Zang, L., & Deng, Y. (2022). Autonomous Vehicle Path Planning Based on Driver Characteristics Identification and Improved Artificial Potential Field. Actuators 2022, 11 (2), 52. https://doi.org/10.3390/act11020052
29. Wang, W., Wu, Z., Lou, H., & Zhang, B. (2022). Path Planning Method of Mobile Robot Using Improved Deep Reinforcement Learning. Journal of Electrical and Computer Engineering, 2022, Article ID: 5433988. https://doi.org/10.1155/2022/5433988 Weerakoon, T., Ishii, K., & Nassiraei, A. A. F. (2015). An Artificial Potential Field Based Mobile Robot Navigation Method to Prevent from Deadlock. JAISCR, 2015, 5 (3), 189-203. https://doi.org/10.1515/jaiscr-2015-0028
30. Xi, M., Yang, J., Wen, J., Liu, H., Li, Y., & Song, H. H. (2022). Comprehensive Ocean Information-Enabled AUV Path Planning Via Reinforcement Learning. IEEE Internet of Things Journal, 9, 18, 17440-17451. https://doi.org/10.1109/JIOT.2022.3155697
31. Xiang, D., Lin, H., Ouyang, J., & Huang, D. (2022). Combined improved A* and greedy algorithm for path planning of multi-objective mobile robot. Scientific Reports, 12 (1), 13273. https://doi.org/10.1038/s41598-022-17684-0
32. Yan, X., Jiang, D., Miao, R., & Li, Y. (2021). Formation Control and Obstacle Avoidance Algorithm of a Multi-USV System Based on Virtual Structure and Artificial Potential Field. J. Mar. Sci. Eng 2021, 9 (2), 161. https://doi.org/10.3390/jmse9020161
33. Yang, L., Qi, J., & Han, J. (2012). “Path planning methods for mobile robots with linear programming” in Proceedings of 2012 International Conference on Modelling, Identification and Control, ICMIC 2012 (pp. 641–646).
34. Yuan, J., Liu, Z., Lian, Y., Chen, L., An, Q., Wang, L., & Ma, B. (2022). Global Optimization of UAV Area Coverage Path Planning Based on Good Print Set and Genetic Algorithm. Aerospace 2022, 9 (2), 86. https://doi.org/10.3390/aerospace9020086
35. Zammit, C., & Kampen, E. J. V. (2022). Comparison Between A* and RRT Algorithms for 3D UAV Path Planning. Unmanned Systems, 10 (2), 129-146. https://doi.org/10.1142/S2301385022500078
36. Zhang, T.W., Xu, G.H., Zhan, X. S., & Han, T. (2022). A new hybrid algorithm for path planning of mobile robot. The Journal of Supercomputing, 78, 4158-4181. https://doi.org/10.1007/s11227-021-04031-9
37. Zhang, Z., Wu, J., Dai, J., & He, C. (2022). Optimal path planning with modified A-Star algorithm for stealth unmanned aerial vehicles in 3D network radar environment. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 236 (1), 72-81. https://doi.org/10.1177/0954410021100738
38. Zheng, J., Mao, S., Wu, Z., Kong, P., & Qiang, H. (2022). Improved Path Planning for Indoor Patrol Robot Based on Deep Reinforcement Learning. Symmetry 2022, 14 (1), 132. https://doi.org/10.3390/sym14010132
39. Zheyi, C. & Bing, X. (2021). AGV Path Planning Based on Improved Artificial Potential Field Method, IEEE International Conference on Power Electronics, Computer Application (ICPECA 2021), 32-37, https://10.1109/ICPECA51329.2021.9362519