Optimizing torpedo trajectories in dynamic naval combat: A deep reinforcement learning approach to real-time target interception and obstacle avoidance
- DRL Framework for Torpedo Guidance: This study addresses a key challenge in naval warfare by introducing Deep Reinforcement Learning (DRL) techniques that offer real-time, optimal torpedo trajectory planning, avoiding restricted zones and intercepting moving targets.
- Overcoming Limitations of Traditional Algorithms: The DRL-based methods outperform traditional approaches, such as the Collision Course Method (CCM) and Bearing-Rider Method (BRM), by providing a more adaptable and efficient solution in complex, dynamic environments.
- Practical Impact on Naval Operations: This AI-based framework can assist the Republic of Korea Navy's next-generation submarines by recommending guided weapon engagement plans, improving decision-making in high-stress combat scenarios, and reducing operational risks.
