Engel Yoğun Çalışma Alanlarında Kapsama ve Geçiş Planlaması

Yıl 2025, Cilt: 8 Sayı: 2, 154 - 162, 30.11.2025

Suat Karakaya

https://doi.org/10.34088/kojose.1694778

Öz

Bu çalışma, sabit engeller içeren iki boyutlu grid tabanlı haritalarda çalışan otonom mobil robotlar için verimli bir kapsama yol planlama algoritması sunmaktadır. Önerilen yaklaşım, çalışma alanını satır tabanlı hücresel bölgelere ayırarak her hücre içinde sistematik bir tarama gerçekleştirmekte ve hücreler arası geçişleri toplam mesafeyi en aza indirmeye yönelik olarak optimize etmektedir. Hücreler arası geçişlerde doğrudan bağlantıların engellere çarpışması durumları için güvenli yol üretimi A* algoritması kullanılarak sağlanmaktadır. Ayrıca, her hücre için giriş ve çıkış yönleri geçiş maliyetine göre ayrı ayrı optimize edilmektedir. Geliştirilen algoritma, 5×5, 15×15 ve 50×50 boyutlarında üç farklı karmaşıklık seviyesine sahip haritalar üzerinde test edilmiştir. Deneysel analizlerde kapsama yoğunluğu, ortalama hücre uzunluğu, yol verimliliği ve A* geçiş oranı gibi çok boyutlu performans metrikleri hesaplanmış ve önerilen yöntemin hem kapsama başarısı hem de geçiş güvenliği açısından tutarlı sonuçlar verdiği gösterilmiştir. Bulgular, yöntemin farklı ölçeklerdeki haritalarda uygulanabilirliğini ve genişletilebilirliğini ortaya koymaktadır.

Anahtar Kelimeler

Kapsama Planlaması , Mobil Robotlar , Hücre Bölütleme , Yol Optimizasyonu , A* Algoritması , Engelden Kaçınma

Coverage and Transition Planning in Obstacle-Rich Workspaces

Öz

This study proposes a comprehensive and efficient algorithm for coverage path planning, specifically designed for autonomous mobile robots operating on two-dimensional grid-based maps with fixed obstacles. The suggested approach merges several established techniques into a cohesive framework, encompassing row-based cellular decomposition, A*-based safe transition planning, and the dynamic optimization of entry and exit points for each segment of coverage. This synthesis allows for the creation of thorough and secure coverage paths while minimizing redundancy. The algorithm undergoes evaluation on maps of varying intricacy (5×5, 15×15, and 50×50), and its performance is measured using various metrics, including coverage density, average cell length, A* transition rate, and overall path efficiency. Results indicate that the proposed integration-based method consistently achieves high levels of coverage and path efficiency, while ensuring safe transitions between different areas. This framework proves to be not only practically efficient but also flexible across different scales, showcasing significant potential for real-world uses such as autonomous cleaning, agricultural monitoring, and emergency rescue operations.

Anahtar Kelimeler

Coverage Planning , Mobile Robots , Cell Partitioning , Path Optimization , A* Algorithm , Obstacle Avoidance

Kaynakça

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