Autonomous Missile Guidance Using Deep Reinforcement Learning
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Solution Overview
Problem
The development of advanced missile systems with guided artificial intelligence poses a threat to national security, necessitating the need for new guided missile technology capable of optimizing trajectory control and ensuring accurate target engagement.
Innovation Solution
The integration of deep reinforcement learning algorithms with missile systems to unify perception and decision-making, utilizing sensors like LiDAR and radiation-hardened field programmable gate arrays to optimize missile trajectories and control thrust vectors for precise target engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional missile control systems are used, then the system structure is simple and reliable, but the trajectory optimization and target engagement accuracy are insufficient
Solution Approach 1:
The patent replaces traditional mechanical control systems with an artificial intelligence-based decision-making system. The AI agent processes sensor data, performs reasoning, and generates control commands, substituting complex mechanical control mechanisms with an intelligent software system that achieves superior trajectory optimization and target engagement accuracy while maintaining manageable system complexity through modular architecture.
2Reliability
If autonomous control with AI is implemented, then trajectory optimization and decision-making accuracy improve, but the system becomes more vulnerable to external interference and requires higher computational resources
Solution Approach 1:
The patent implements radiation-hardened field programmable gate arrays and error-correcting codes to protect the AI system against radiation and external interference before they can cause damage. These protective measures are built into the hardware architecture in advance, creating a cushioning effect that maintains autonomous control reliability even in harsh electromagnetic and radiation environments.
3Ease of operation
If deep reinforcement learning algorithms are used, then intelligent decision-making and trajectory optimization are enhanced, but the computational processing time and energy consumption increase
Solution Approach 1:
The patent pre-loads the deep reinforcement learning model into the field programmable gate array before missile launch. During flight, the AI agent executes inference operations using pre-trained weights and parameters, eliminating the need for real-time training computations. This preliminary preparation significantly reduces computational processing time and energy consumption during the critical flight phase while maintaining full intelligent decision-making capability.
Data Source
AI summary
The present disclosure provides methods for controlling a guided missile to account for environmental uncertainties and maintain optimal mission performance and minimize error in hitting a defined target anywhere on Earth. First, sensors collect data about the missile's environment, passing the information to storage in the missile's database and processor. Second, the missile's processor manipulates the database with a deep reinforcement learning algorithm producing instructions. Third, the instructions command the missile's control system for optimal control, target engagement, and impact by manipulating the missile's thrust vectors for guidance. In short, the disclosure provides methods for autonomous missile control which command the missile from launch to target with certainty regardless of weather conditions, environment dynamics, or defensive missile interference.