Otonom Araçla Genetik Algoritma Kullanılarak Haritalama ve Lokasyon

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.

Anahtar Kelimeler:

Genetik Algoritma, Haritalama, Lokasyon, Otonom Araç, SLAM

Mapping and Location Using Genetic Algorithm with Autonomous Vehicle

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,

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