Mapping and Location Using Genetic Algorithm with Autonomous Vehicle

Abstract

Significant progress has been made in autonomous systems in the light of technological advances and accumulated knowledge to date. In this way, autonomous systems, collision avoidance, traffic sign detection, mapping and so on. It can perform numerous intelligent functions. The most challenging problem of real-time autonomous vehicles is that the vehicle can perform self-mapping and location operations. Optimized location application using Genetic Algorithm (GA) is expected to increase driving safety for autonomous vehicles. This study focuses on a laser based localization and mapping technique. In the system, a virtual test environment was established and experiments were performed on an autonomous vehicle. Within the scope of the study, virtual machines were created and Linux operating system was installed on them. Then, TurtleBot3 was installed in these virtual machines in ROS environment and a map was obtained by localizing the interior. This map is used to find the shortest distances by genetic algorithm. As a result of the observations, it was concluded that the robot in the simulation environment can go to the desired position with high performance.

Keywords

• Genetic Algorithm,Mapping,Location,Autonomous Vehicle,SLAM

Otonom Araçla Genetik Algoritma Kullanılarak Haritalama ve Lokasyon

Abstract

Teknolojik gelişmeler ve bu zamana kadar biriken bilgilerin ışığında otonom sistemlerde muazzam bir ilerleme kaydedilmiştir. Bu sayede otonom sistemler çarpışmadan kaçınma, trafik işareti tespiti, haritalama vb. sayısız akıllı işlevleri gerçekleştirebilmektedir. Gerçek zamanlı otonom araçların en zorlu problemi aracın kendi kendine haritalandırma ve lokasyon işlemlerini yapabilmesidir. Genetik Algoritma (GA) kullanarak optimize edilmiş lokasyon uygulaması ile otonom araçlar için sürüş güvenliğinin artması beklenmektedir. Bu çalışmada lazer tabanlı bir lokalizasyon ve haritalama tekniğinin üzerine odaklanılmıştır. Gerçekleştirilen sistemde sanal bir test ortamı kurulmuş ve bir otonom araç üzerinde denemeler yapılmıştır. Çalışma kapsamında sanal makineler oluşturularak üzerlerine Linux işletim sistemi kurulmuştur. Sonra bu sanal makinelere ROS ortamında TurtleBot3 kurulmuş ve iç mekân lokalizasyonu yapılarak bir harita elde edilmiştir. Bu harita genetik algoritma ile en kısa mesafelerin bulunmasını sağlamak için kullanılmaktadır. Gözlemler neticesinde simülasyon ortamındaki robot yüksek başarımla istenilen konuma gidebildiği sonucuna ulaşılmıştır.

Keywords

• Genetik Algoritma,Haritalama,Lokasyon,Otonom Araç,SLAM

References

1. [1] The Road Sign Recognition Group (2019, 5 Ağustos ) Road Sign Recognition Survey”,[Online]: Available: http://euler.fd.cvut.cz/research/rs2/files/skoda-rs-survey.html.
2. [2] A. Lindgren and F. Chen, “In State of the art analysis: an overview of advanced driver assistance systems (ADAS) and possible human factors issues”, Human Factors and Economics Aspects on Safety, 2006, ss. 38–50.
3. [3] M. Aeberhard et al., “Experience, results and lessons learned from automated driving on Germany’s highways”, IEEE Intelligent Transportation System Magazine, c. 7, s.1, ss. 42–57, 2015.
4. [4] J. Ziegler et al., “Making bertha drive—an autonomous journey on a historic route”, IEEE Intelligent Transportation System. Magazine, c. 6, s. 2, ss. 8–20, 2014,.
5. [5] M. Fu and Y. Huang,” in A survey of traffic sign recognition”, Wavelet Analysis and Pattern Recognition –ICWAPR, 2010, ss. 119–124.
6. [6] A. Driving, “Levels of driving automation are defined in new SAE international standard J3016: 2014”, SAE International: Warrendale, PA, USA, 2014.
7. [7] C.Y. Fang, S. W. Chen, C. S. Fuh, “Road-Sign Detection and Tracking,” Vehicular Technology, IEEE Transactions , c. 52, s. 5, ss. 1329- 1341.
8. [8] K.Paslıoğlu, “Otonom Mobil Robotlarda Dağılımlı Kalman Filtresi Tabanlı Eş Zamanlı Lokalizasyon ve Haritalama”, Yüksek lisans tezi, Fizik Mühendisliği, Fen Bilimleri Enstitüsü, İstanbul Teknik Üniversitesi, İstanbul, Türkiye, 2010.

9. [9] T.Stahl, A. Wischnewski, J. Bethz and M. Lienkamp, “ROS-based localization of a race vehicle at high-speed using LIDAR”, E3S Web of Conferences ICPEME, 2019, c. 95.
10. [10] M. Yaktubay, “A Genetic Algorithm Based Solution Approach For Vehicle Routing Problem”, Yükse lisans tezi, Endüstri Mühendisliği, Fen Bilimleri Enstitüsü, Adana Bilim ve Teknoloji Üniversitesi, Adana, Türkiye, 2018.
11. [11] M.-J. Jung, H. Myung, S.-G. Hong, D. Park, H.-K. Lee, and S. Bang, “Structured light 2D range finder for simultaneous localization and map-building (SLAM) in home environments,” in Proc. of the 2004 International Symposium on Micro-Nanomechatronics and Human Science, and 2004 The Fourth Symposium Micro-Nanomechatronics for Information-Based Society, 2004, ss. 371–376.
12. [12] Y. Misono, Y. Goto, Y. Tarutoko, K. Kobayashi, and K. Watanabe, “Development of laser rangefinder-based SLAM algorithm for mobile robot navigation,” in Proc. of the SICE 2007 Annual Conference, ss. 392–396.
13. [13] M. Begum, G. K. Mann, and R. G. Gosine, “Integrated fuzzy logic and genetic algorithmic approach for simultaneous localization and mapping of mobile robots,” Applied Soft Computing, c. 8, s. 1, ss. 150 – 165, 2008.
14. [14] A. Tuncer, “Otonom Araçlar için Yol Bulma Probleminin Genetik Algoritmalar ve FPGA ile Çözümü ve Gerçekleştirilmesi”, Doktora Tezi, Elektronik ve Bilgisayar Eğitimi, Fen Bilimleri Enstitüsü, Kocaeli Üniversitesi, Kocaeli, Türkiye, 2013.
15. [15] D. J. Feng, S. Wijesoma and A. P. Shacklock, “Genetic Algorithmic Filter Approach to Mobile Robot Simultaneous Localization and Mapping”, IEEE 9th International Conference on Control, Automation, Robotics and Vision, Singapore, 2007. [16] D. J. Feng and S. Wijesoma, “Improving Rao-Blackwellised Genetic Algorithmic Filter SLAM Through Genetic Learning”, IEEE 10th International Conference on Control, Automation, Robotics and Vision, Hanoi, Vietnam, 2008.
16. [17] R. R. Murphy, “Introduction to AI Robotics”, MIT Press, London, 2000.
17. [18] R. C. Arkin, “Behavior-Based Robotics”, John Wiley and Sons Press, England, 2002.
18. [19] J. Aulinas, Y. Petillot and J. Salvi, X. Lladó, “The SLAM problem: a survey”, Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence (CCIA), October, 2008, Spain.
19. [20] S. Thrun, “Robotic mapping: A survey”. Exploring Artificial Intelligence in the New Millenium. The Morgan Kaufmann Series in Artificial Inteligence, ISBN ISBN-10: 1558608117, 2002.
20. [21] P.M. Newman and J.J. Leonard, “Consistent, Convergent, and constant-time SLAM”, International Joint Conference on Artificial Intelligence (IJCAI), Mexico,2003, ss. 1143-U1150.
21. [22] P. Jensfelt, D. Kragic, J. Folkesson and M. Björkman, “A framework for vision based bearing only 3D SLAM”, in Proc. IEEE International Conference on Robotics and Automation, ICRA,2006, ss. 1944–1950.
22. [23] S. Se, D. Lowe and J. Little, “Mobile robot localization and mapping with uncertainty using scaleinvariant visual landmarks”, The International Journal of Robotics Research, c. 21, s. 8, ss. 735–758, 2002.
23. [24] J. E. Guivan and E. M. Nebot, “Optimization of the Simultaneous Localization and Map- Building Algorithm for Real-Time Implementation”, IEEE Transactions on Robotics and Automation, c. 17, s. 3, 2001.
24. [25] S. Thrun and Y. Liu, “Multi-robot SLAM with sparse extended information filers”, 11th International Symposium of Robotics Research (ISRR’03), Sienna, Italy, 2003.
25. [26] S. Thrun, C. Martin, Y. Liu, D. Hähnel, R. Emery-Montemerlo, D. Chakrabarti and W. Burgard, “A real-time expectation maximization algorithm for acquiring multi-planar maps of indoor environments with mobile robots”, IEEE Transactions on Robotics and Automation, c. 20, s. 3, ss. 433–442, 2004.
26. [27] M. Montemerlo, S. Thrun, D. Koller and B. Wegbreit, “FastSLAM: A factored solution to the simultaneous localization and mapping problem”, in Proc. of the National Conference on Artificial Intelligence, 2002, ss. 593–598.
27. [28] M. Montemerlo, S. Thrun, D. Koller and B. Wegbreit, “FastSLAM 2.0: An improved particle filtering algorithm for simultaneous localization and mapping that provably converges”, 18th International Joint Conference on Artificial Intelligence (IJCAI), 2003, Mexico, ss. 1151–1156.
28. [29] W. Burgard, D. Fox, H. Jans, C. Matenar and S. Thrun, “Sonar-based mapping with mobile robots using EM”, 16th International Conference on Machine Learning, 1999.
29. [30] S.Solak, “Gezgin Robotların Konom Belirleme ve Engel Sakınım Probleminin Tek Kartlı Bilgisayar Sistemi Kullanılarak Çözümü”, Doktora Tezi, Elektronik ve Bilgisayar Eğitimi, Fen Bilimleri Ensitüsü, Kocaeli Üniversitesi, Kocaeli, Türkiye, 2016.