Autonomous Rocket Control via Deep Reinforcement Learning
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Solution Overview
Problem
Current rocket control systems are unable to generalize about environmental uncertainties, particularly in inclement weather conditions, leading to limited launch windows and reduced safety and efficiency in landing operations.
Innovation Solution
Implementing a deep reinforcement learning algorithm integrated with convolutional neural networks and sensors like LiDAR and cameras, which process real-time data to optimize thrust vector control and adjust the rocket's trajectory for optimal landing performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional rocket control systems are used, then the control system is simple and reliable, but the system cannot generalize about environmental uncertainties and is limited in inclement weather conditions
Solution Approach 1:
The patent replaces traditional mechanical control systems with an artificial intelligence-based control system that uses deep reinforcement learning and convolutional neural networks. This substitution enables the system to process sensor data (LiDAR, cameras) and generalize about environmental uncertainties, allowing autonomous adaptation to inclement weather conditions while maintaining reliability through simulation training.
Solution Approach 2:
The control system is trained in simulation environments before actual deployment, allowing it to learn and generalize about a wide range of environmental conditions including inclement weather. This preliminary training action enables the system to handle uncertainties during actual rocket operations without requiring complex real-time adjustments.
2Productivity
If deep reinforcement learning algorithms are implemented, then the rocket can make instantaneous adjustments in uncertain environments, but the device complexity and computational requirements increase
Solution Approach 1:
The patent uses simulation environments to create virtual copies of the rocket and its operating conditions. The deep reinforcement learning algorithm is trained extensively in these simulated environments before deployment, allowing it to learn optimal control strategies without requiring complex real-time computations during actual flights. This copying approach enables high productivity while managing computational complexity.
Solution Approach 2:
The control system uses onboard sensors (LiDAR, cameras) to autonomously perceive and interpret the environment, eliminating the need for external guidance or complex ground-based control systems. The AI algorithm makes instantaneous adjustments based on real-time sensor data, enabling self-service operation that improves landing efficiency without proportionally increasing overall system complexity.
3Reliability
If autonomous control with AI algorithms is used, then the rocket can operate safely in inclement weather, but the reliability and safety verification becomes more difficult
Solution Approach 1:
The patent performs extensive safety verification and testing in simulation environments before actual deployment. The deep reinforcement learning algorithm is trained on diverse environmental conditions including inclement weather, allowing thorough safety verification in virtual tests. This preliminary action enables the system to achieve high reliability in uncertain environments while simplifying actual flight safety verification.
Solution Approach 2:
The control system continuously monitors sensor data and compares actual performance against expected behavior, providing real-time feedback for safety verification. The onboard AI algorithm adjusts control actions based on environmental conditions, and this feedback mechanism allows for ongoing safety assessment during actual operations, making reliability verification more manageable despite the complexity of autonomous control.
Data Source
AI summary
Rocket control is a difficult and unpredictable task in environments with inclement weather. As a result, launch missions are often strictly limited based on weather conditions. The present invention provides a method for controlling a rocket to account for environmental uncertainties and maintain optimal mission performance. First, sensors collect data about the rocket's environment, passing the information to storage in the rocket's database. Second, the rocket's processor manipulates the database with an optimization algorithm producing instructions. Third, the instructions command the rocket's control system for an optimal end-to-end trajectory and to enable the rocket to perform a safe landing.