Süt Taşımacılığında İnsan Sağlığını Koruma Odaklı Çoklu Ajanlı Yol Planlaması: Bulanık Mantık ve Hibrit Yaklaşımların Entegrasyonu

Yıl 2024, Cilt: 3 Sayı: 2, 1 - 18

Cem Özkurt

Öz

Genellikle taşıma süreçlerinde yaşanan gecikmeler veya keyfi kararlar, özellikle gıda ve tarım
sektörlerinde sıkça karşılaşılan bir sorundur. Mevcut navigasyon sistemlerinin genellikle sadece hız veya yakıt
tasarrufu odaklı olması, kısa ömürlü ve özel ürünlerin korunmasını ihmal etmesine neden olmaktadır. Bu çalışma,
özellikle süt taşıma süreçlerinde ortaya çıkan bu sorunlara çözüm getirmeyi hedeflemektedir. Süt taşımacılığında,
verim, sıcaklık ve kütlenin kullanımı gibi belirli standartlar belirlenerek, dinamik bir yapı kullanılarak karar
alınması önerilmektedir. Çalışma, genetik algoritmalar, bulanık mantık ve hibrit yaklaşımların çoklu ajan
sistemleri ve otonom robot yol planlamasında kullanımına dair önemli perspektiflere dayanmaktadır. Literatürde,
koordinasyon davranışları, bulanık kurallı yol planlaması, dil değişkenleri ve işbirliğine dayalı öğrenme yöntemleri
gibi çeşitli alanlar incelenmektedir. Bu çalışmanın odaklandığı konu, "A* algoritması ile çoklu hedefli ajanları
kullanarak bulanık mantık" üzerine yoğunlaşarak, bu temelleri daha da geliştirmeyi ve çoklu ajanlı yol planlama
senaryoları için güçlü çözümler sunmayı amaçlamaktadır.

Anahtar Kelimeler

Mobil Yol Planlaması, A* algoritması ile Yol Bulma, Çok Hedefli Ajanlar Yol Planlaması, Bulanık Mantık

Multi-Agent Route Planning Focused on Protecting Human Health in Milk Transportation: Integration of Fuzzy Logic and Hybrid Approaches

Öz

In transportation processes, delays and arbitrary decisions are often encountered, especially in the
food and agriculture sectors. The prevailing focus of current navigation systems on speed or fuel efficiency often
neglects the preservation of perishable and specialty products. This study aims to address these issues, particularly
in milk transportation processes. It proposes decision-making through the use of specific standards such as
efficiency, temperature, and mass, employing a dynamic framework. The study draws insights from the utilization
of genetic algorithms, fuzzy logic, and hybrid approaches in multi-agent systems and autonomous robot path
planning. Various aspects are explored in the literature, including coordination behaviors, fuzzy rule-based path
planning, linguistic variables, and collaboration-based learning methods. The focus of this study lies in enhancing
these fundamentals, particularly emphasizing on utilizing the A* algorithm with multi-target agents using fuzzy
logic, aiming to provide robust solutions for multi-agent path planning scenarios.

Anahtar Kelimeler

Mobile Path Planning, Pathfinding with A* algorithms, Multi-Target Agents Path Planning, Fuzzy Logic

Kaynakça

• [1] Castillo, O., Soria, J., Arias, H., Morales, J. B., & Inzunza, M. 2007. Intelligent control and planning of autonomous mobile robots using fuzzy logic and multiple objective genetic algorithms. In Analysis and Design of Intelligent Systems using Soft Computing Techniques (pp. 799-807). Berlin, Heidelberg: Springer Berlin Heidelberg.
• [2] Booch, G., Rumbaugh, J., & Jacobson, I. (2005). The Unified Modeling Language User Guide (2nd ed.). Addison-Wesley Professional.
• [3] Gireesh Kumar, T., Poornaselvan, K. J., & Sethumadhavan, M. (2010). Fuzzy Support Vector Machine-based Multi-agent Optimal Path Planning Approach to Robotics Environment. Defence Science Journal, 60(4).
• [4] Cerami, C., Rapp, T., Lin, F. C., Tompkins, K., Basham, C., Muller, M. S., ... & Smith, J. 2021. High household transmission of SARS-CoV-2 in the United States: living density, viral load, and disproportionate impact on communities of color. medRxiv. Published online. DOI, 10(2021.03), 10-21253173.
• [5] Fayad, C., & Webb, P. 2006. Development of a hybrid crisp-fuzzy logic algorithm optimised by genetic algorithms for path-planning of an autonomous mobile robot. Journal of Intelligent & Fuzzy Systems, 17(1), 15-26.
• [6] Kumar, G., & Vijayan, V. P. 2007. A multi-agent optimal path planning approach to robotics environment. In International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) (Vol. 1, pp. 400-404). IEEE.
• [7] Lamini, C., Fathi, Y., & Benhlima, S. 2017. H-MAS architecture and reinforcement learning method for autonomous robot path planning. In 2017 Intelligent Systems and Computer Vision (ISCV) (pp. 1-7). IEEE.
• [8] Luviano, D., & Yu, W. (2017). Continuous-time path planning for multi-agents with fuzzy reinforcement learning. Journal of Intelligent & Fuzzy Systems, 33(1), 491-501.
• [9] Makita, Y., Hagiwara, M., & Nakagawa, M. 1994. A simple path planning system using fuzzy rules and a potential field. In Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference (pp. 994-999). IEEE.
• [10] Sabo, C., Cohen, K., Kumar, M., & Abdallah, S. (2009, June). Path Planning for a Fire-Fighting Aircraft using Fuzzy Logic. In 47th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition (p. 1353).
• [11] Shibata, T., & Fukuda, T. 1993. Coordinative behavior by genetic algorithm and fuzzy in evolutionary multi-agent system. In Proceedings IEEE International Conference on Robotics and Automation (pp. 760-765). IEEE.
• [12] Walker, K., & Esterline, A. C. 2000. Fuzzy motion planning using the Takagi-Sugeno method. In Proceedings of the IEEE SoutheastCon 2000. 'Preparing for The New Millennium'(Cat. No. 00CH37105) (pp. 56-59). IEEE.
• [13] Zadeh, L. A. (1988). Fuzzy logic. Computer, 21(4), 83-93.
• [14] Klir, G., & Yuan, B. (1995). Fuzzy sets and fuzzy logic (Vol. 4, pp. 1-12). New Jersey: Prentice hall.
• [15] Rao, M., Bast, A., & De Boer, A. (2021). Valorized food processing by-products in the EU: Finding the balance between safety, nutrition, and sustainability. Sustainability, 13(8), 4428.
• [16] Arenas-Parra, M., Bilbao-Terol, A., & Jiménez, M. (2016). Standard goal programming with fuzzy hierarchies: a sequential approach. Soft Computing, 20, 2341-2352.
• [17] Kahraman, C. (Ed.). (2008). Fuzzy multi-criteria decision making: theory and applications with recent developments (Vol. 16). Springer Science & Business Media.
• [18] Russell, S., & Norvig, P. (2010). Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall