Sürü Robotiğinde Yol Planlanmasının Hızla Keşfedilen Rastgele Ağaç- Saf Takip İş Birliği Tabanlı Algoritma ile İncelenmesi
Abstract
Bu çalışmada sürü robotiğinde yol planlama davranışı için RRT (Hızla Keşfedilen Rastgele Ağaç Algoritması)-Pure Pursuit iş birliği tabanlı algoritma geliştirilmiştir. Sürü robotlarının belirlenen arenalarda başlangıç konumdan hedef konuma ulaşabilmeleri için yol planlama yaklaşımı önerilmiştir. Ayrıca geliştirilen bu algoritma yol planlama sürecinde sürü robotlarının engelden kaçınmasını sağlamayı amaçlamıştır. Bu çalışmada yol planlama yaklaşımı, esnek ve ölçeklenebilir davranış gösteren sürü robotları üzerinde uygulanabilirliği gösterilmiştir. Geliştirilen algoritma ile her bir sürü robotu, üç farklı arenalarda hedef konuma ulaşmak için takip edilecek yola olan ileriye bakma mesafesine bağlı olarak organize bir şekilde sürü davranışı sergilenmektedir. RRT-Saf Takip tabanlı iş birliğine dayalı algoritmada arenalarda önceden belirlenmiş başlangıç ve hedef konum arasında engelsiz bir yol oluşturulur. Oluşturulan yolun, Pure Pursuit (Saf Takip) algoritması ile takip edilmesi amaçlanmıştır. Geliştirilen algoritmada 3 farklı arenada l_d değeri 0.5 ile robot sayıları 3,5 ve 7 incelenmiştir. Robot sayıları artış gösterdikçe simülasyon tamamlanma süreleri artmaktadır. Yani arena içerisindeki robot sayısı ile simülasyon tamamlanma süresi doğru orantılıdır. Ve arenalar içerisindeki engellere ve hedef konuma olan uzaklığa göre de robotların katettikleri mesafeler değişiklik göstermektedir.
References
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Investigation of Path Planning in Swarm Robotics with Rapidly Exploring Random Tree-Pure Pursuit Collaboration-Based Algorithm
Abstract
In this study, an RRT-Pure Pursuit collaboration-based algorithm was developed for path planning behavior in swarm robotics. A path planning approach has been proposed so that the swarm robots can reach the target location from the starting location in the determined arenas. In addition, this developed algorithm aims to ensure that the swarm robots avoid obstacles in the path planning process. In this study, the applicability of the path planning approach on flexible and scalable swarm robotics has been demonstrated. With the developed algorithm, each swarm robot exhibits swarm behavior in three different arenas in an organized manner depending on the distance to look forward to the path to be followed to reach the target location. In the RRT-Pure Pursuit-based collaborative algorithm, an unobstructed path is created in arenas between a predetermined start and target location. It is aimed to follow the path created with the Pure Pursuit algorithm. In the developed algorithm, l_d value of 0.5 and robot numbers 3,5 and 7 were examined in 3 different arenas. As the number of robots increases, the simulation completion times increase. In other words, the number of robots in the arena and the simulation completion time are directly proportional. And according to the obstacles in the arenas and the distance to the target location, the distances traveled by the robots vary.
References
- [1] Mısır, O., & Gökrem, L. (2020). Sürü Robotları için Esnek ve Ölçeklenebilir Toplanma Davranışı Metodu. European Journal of Science and Technology, 100-109. https://doi.org/10.31590/ejosat.779162
- [2] Mısır, O., & Gökrem, L. (2021). Flocking-Based Self-Organized Aggregation Behavior Method for Swarm Robotics. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 45(4), 1427-1444. https://doi.org/10.1007/s40998-021-00442-9
- [3] Mısır, O., Gökrem, L., & Serhat Can, M. (2020). Fuzzy-based self organizing aggregation method for swarm robots. Biosystems, 196, 104187. https://doi.org/10.1016/j.biosystems.2020.104187
- [4] Mısır, O., & Gökrem, L. (2021). Dynamic interactive self organizing aggregation method in swarm robots. Biosystems, 207, 104451. https://doi.org/10.1016/j.biosystems.2021.104451
- [5] Stormont, D. P. (2005). Autonomous rescue robot swarms for first responders. CIHSPS 2005. Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety, 2005., 151-157. https://doi.org/10.1109/CIHSPS.2005.1500631
- [6] Tan, Y., & Zheng, Z. (2013). Research Advance in Swarm Robotics. Defence Technology, 9(1), 18-39. https://doi.org/10.1016/j.dt.2013.03.001
- [7] Mısır, O. (2023). Dynamic local path planning method based on neutrosophic set theory for a mobile robot. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 45(3), 1-13. https://doi.org/10.1007/s40430-023-04048-6
- [8] Hosseini, S. (2016). Path Tracking Methodologies For Mobile Robots [Fen Bilimleri Enstitüsü]. http://hdl.handle.net/11527/15511
- [9] Yaşar, E. (2020). Sürü Robotların Hareket Planlamada Kullanılması. Avrupa Bilim ve Teknoloji Dergisi, 20, Article 20. https://doi.org/10.31590/ejosat.763444
- [10] Lozano-Pérez, T., & Wesley, M. A. (1979). An algorithm for planning collision-free paths among polyhedral obstacles. Communications of the ACM, 22(10), 560-570. https://doi.org/10.1145/359156.359164
- [11] Panerati, J., Gianoli, L., Pinciroli, C., Shabah, A., Nicolescu, G., & Beltrame, G. (2018). From Swarms to Stars: Task Coverage in Robot Swarms with Connectivity Constraints. 2018 IEEE International Conference on Robotics and Automation (ICRA), 7674-7681. https://doi.org/10.1109/ICRA.2018.8463193
- [12] Li, Y. (2021). An RRT-Based Path Planning Strategy in a Dynamic Environment. 2021 7th International Conference on Automation, Robotics and Applications (ICARA), 1-5. https://doi.org/10.1109/ICARA51699.2021.9376472
- [13] Huang, Y., Tian, Z., Jiang, Q., & Xu, J. (2020). Path Tracking Based on Improved Pure Pursuit Model and PID. 2020 IEEE 2nd International Conference on Civil Aviation Safety and Information Technology (ICCASIT, 359-364. https://doi.org/10.1109/ICCASIT50869.2020.9368694
- [14] Saf Takip Denetleyicisi—MATLAB & Simulink. (t.y.). Geliş tarihi 25 Mart 2023, gönderen https://www.mathworks.com/help/robotics/ug/pure-pursuit-controller.html?searchHighlight=pure%20pursuit&s_tid=srchtitle_pure%20pursuit_1
- [15] Rapidly-exploring random tree. (2023). İçinde Wikipedia. https://en.wikipedia.org/w/index.php?title=Rapidly-exploring_random_tree&oldid=1139597246
- [16] Garip, Z. (2018). Mobil robotların yol planması için metasezgisel hibrit algoritmalar geliştirilmesi ve uygulanması [DoctoralThesis, Sakarya Üniversitesi]. https://acikerisim.sakarya.edu.tr/handle/20.500.12619/74245
- [17] Nemec, D., Janota, A., Hruboš, M., Gregor, M., & Pirnik, R. (2017). Mutual acoustic identification in the swarm of e-puck robots. International Journal of Advanced Robotic Systems, 14, 172988141771079. https://doi.org/10.1177/1729881417710794
- [18] Zhang, W., Yi, C., Gao, S., Zhang, Z., & He, X. (2020). Improve RRT Algorithm for Path Planning in Complex Environments. 2020 39th Chinese Control Conference (CCC), 3777-3782. https://doi.org/10.23919/CCC50068.2020.9188970
- [19] Mısır, O., Çeli̇k, M., & Gökrem, L. (2022). Waypoint-Based Path Tracking Approach For Self-Organized Swarm Robots. Uluslararası Muhendislik Arastirma ve Gelistirme Dergisi, 14, 799-815. https://doi.org/10.29137/umagd.1118039
Details
| Primary Language | English |
|---|---|
| Subjects | Electrical Engineering (Other) |
| Journal Section | Research Articles |
| Authors | |
| Early Pub Date | December 27, 2023 |
| Publication Date | January 3, 2024 |
| Submission Date | June 30, 2023 |
| Acceptance Date | November 17, 2023 |
| Published in Issue | Year 2023 Volume: 2023 Issue: 19 |