The research, titled "Swarm Intelligence-Based Path Planning for Unmanned Surface Vehicles in Maritime Patrol Missions," was conducted by a team of experts from the Republic of Indonesia Defense University (Unhan RI), led by Muhammad Fajar Indra Afrianta along with Imanuel Dindin, Ade Bagdja, Gita Amperiawan, and Muhammad Zainal Furqon. This study is vital as modern maritime operations now demand advanced navigation solutions for coastal and border surveillance without risking human personnel.
Smart Solutions from Nature’s Behavior
For a long time, automated ship navigation has faced significant hurdles in dynamic oceans, ranging from strong sea currents and harsh weather to the risk of collision with other vessels. Conventional methods often fail to respond to changing situations in real-time.
The research team from the Defense University analyzed 12 computational methods developed between 2020 and 2025. They found that nature-inspired algorithms, such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), possess extraordinary capabilities in solving complex routing problems.
"This swarm-based approach utilizes decentralized decision-making, similar to how thousands of ants collaborate to find food, allowing automated ships to coordinate with extreme efficiency," the researchers wrote in their report.
Key Findings: Navigation Speed and Accuracy
Based on an analysis of 89 global studies, the research summarized several vital findings for the development of automated patrol ship fleets:
- PSO is the Fastest Algorithm: The Particle Swarm Optimization (PSO) variant proved to be the most popular method (used in 42.7% of studies) because it offers the best balance between data processing speed and result accuracy. This algorithm can calculate optimal routes in just 0.8 to 3.5 seconds.
- Hybrid Methods are More Robust: Combining two methods, such as PSO and ACO, proved far more effective in dealing with unpredictable sea conditions. These hybrid methods achieved a collision avoidance success rate of over 98%.
- Energy Efficiency: Using smart navigation can save ship energy consumption by 12% to 43% compared to standard navigation methods.
- Compliance with Maritime Rules: The integration of International Regulations for Preventing Collisions at Sea (COLREGS) into autonomous systems has shown significant progress, ensuring robot ships comply with maritime law while operating in international waters.
Challenges Behind the Simulation
Despite promising laboratory results, Muhammad Fajar Indra Afrianta’s team provided a critical note. Approximately 73% of current research is still limited to computer simulations. When this technology is tested in real sea environments, performance tends to drop by 18% to 42% due to sensor interference and field communication constraints.
"The gap between simulation and reality is an urgent challenge that must be addressed so that these autonomous fleet systems are truly ready for military operations or coast guard duties," the research team emphasized.
Impact on National Security and Economy
The implementation of swarm technology for automated ships is predicted to reshape the maritime and defense industries. For governments, this technology offers much wider maritime surveillance at lower operational costs compared to manned vessels. In the economic sector, more efficient navigation can reduce maritime logistics costs and improve shipping safety.
This research reinforces Indonesia's position, through the Defense University, in the global development of independent and AI-based future defense technology.
Author Profile: Muhammad Fajar Indra Afrianta is a researcher in the field of Motion Power Technology, Faculty of Engineering and Defense Technology, Republic of Indonesia Defense University. He has expertise in autonomous systems and maritime navigation optimization. Along with fellow lecturers and heads of the Defense Industry study program such as Imanuel Dindin and Ade Bagdja, the team focuses on developing modern military technology innovations to strengthen Indonesia's maritime sovereignty.
Research Source:
Afrianta, M. F. I., Dindin, I., Bagdja, A., Amperiawan, G., & Furqon, M. Z. (2026). Swarm Intelligence-Based Path Planning for Unmanned Surface Vehicles in Maritime Patrol Missions. Indonesian Journal of Advanced Research (IJAR), 5(3), 299-320.
DOI:
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