Derin Takviyeli Öğrenme Tabanlı Bilinmeyen Ortamda Çoklu Robot Navigasyonu

Günümüzde mobil robotların navigasyon problemini derin takviyeli öğrenme (DRL) ile çözmeye çalışmak ilgi çekici konulardan birisi haline gelmiştir. Tekli mobil robotlarda DRL yüksek başarı oranlarına ulaşmıştır. Çoklu robot sistemlerinde ise, problemin karmaşıklığı üstel bir şekilde arttığı için maliyeti yüksek ve daha zorlu iş haline gelmektedir. Bu çalışmada ise DRL ile çoklu robot navigasyonu problemine çözüm getirilmeye çalışılmıştır. Önerilen yaklaşımdaki sistemde eşzamanlı bir ortam, bu ortamda birden fazla robot, hedef, engel bulunmaktadır. Ortamda robotlar sırasıyla eylem seçerek, hareket ederler. Aynı zamanda robotlar kendilerinden başka robotlar için dinamik bir engel işlevi görmektedir. Robotlar kendi hedeflerine en kısa yoldan herhangi bir çarpışma yaşamadan ulaşmaya çalışırlar. Aynı zamanda robotlar bir başka robotla çarpışmayacak şekilde veya bir başka robotun rotasını uzatmayacak şekilde yol planlaması yapmaya çalışırlar. Bunları sağlayabilmek için çok ajanlı deep q-network (DQN) algoritması, hedefe yönelik bir durum verisi, güçlendirilmiş adaptif ödül mekanizması kullanılmıştır. Önerilen yaklaşım doğrultusunda oluşturulan sistem tek bir robotun navigasyon başarısı, çoklu robot sisteminin navigasyon başarısı, birim-kare başına düşen robot sayısına göre başarı oranı olarak değerlendirilmiştir. Bu değerlendirmeler önerilen yaklaşımın performansını doğrulamıştır.

Anahtar Kelimeler:

bilinmeyen ortamda navigasyon, çoklu robot navigasyonu, çok ajanlı derin takviyeli öğrenme

Multi-Robot Navigation in Unknown Environment Based on Deep Reinforcement Learning

To solve multi-robot navigation with traditional methods, many algorithms can be used in combination, both for navigation and for the cooperation of these robots. These traditional methods using multiple algorithms are costly. Deep reinforcement learning (DRL) is simpler and less costly when compared to traditional methods. Nowadays, it is tried to solve real-world problems with DRL for these reasons. In this study, it has been tried to solve the multi-robot navigation problem with DRL. In the system in the proposed approach, there is a synchronous environment and more than one robot, target and obstacle in this environment. The robots in the environment move by selecting an action, respectively. At the same time, the robots as a dynamic obstacle for other robots. The robots try to reach their targets in the shortest path without any collision. At the same time, the robots try to plan paths so that they do not collide with another robot or extend the path of another robot. In order to provide these, multi-agent DQN algorithms, target-oriented state data, and reinforced adaptive reward mechanism were used. The system in the proposed approach was evaluated as the navigation success of a single robot, the navigation success of the multi-robot system, and the success rate according to the number of robots per unit square.

Keywords:

navigation in unknown environment, navigation for multi-robots, multi-agent deep reinforcement learning,

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