Reinforcement Learning for Space Vehicle Decoupling Location

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Solution Overview

Problem

In-space manufacturing units face challenges in delivering products to appropriate locations on Earth, leading to increased ground transportation costs due to inefficient decoupling and landing strategies for space vehicles.

Innovation Solution

The implementation of a computer-implemented system using reinforcement learning to identify optimal decoupling locations for space vehicles in space, allowing them to land closer to delivery locations on Earth, thereby minimizing surface transportation costs and time.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional decoupling strategies are used for space vehicles, then the manufacturing unit can maintain operational simplicity, but ground transportation costs and time increase due to inefficient landing locations

Engineering Contradiction:

Improvedelivery efficiencyVSAvoidground transportation time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The reinforcement learning model performs preliminary computation to identify optimal decoupling locations before the space vehicle needs to be decoupled. By pre-calculating the best decoupling point based on current manufacturing unit position, product destination, and orbital parameters, the system ensures that space vehicles land as close as possible to their final delivery locations, thereby minimizing ground transportation time and maximizing delivery efficiency

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically adjusts decoupling location decisions based on real-time or near-real-time parameters including the manufacturing unit's current orbital position, the destination requirements on Earth, and orbital mechanics constraints. This dynamic optimization allows the system to continuously improve delivery efficiency by selecting the best possible decoupling point for each specific mission scenario

Inventive Principle:

Principle #15Dynamics

2Productivity

If traditional decoupling strategies are used for space vehicles, then the system structure can remain simple, but transportation costs increase due to suboptimal landing locations

Engineering Contradiction:

Improvesupply chain effectivenessVSAvoidground transportation cost

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system converts the constraint of orbital mechanics and manufacturing unit trajectory into a benefit by using these same factors as input parameters for the reinforcement learning model. The model learns to work within the physical constraints while optimizing the decoupling point to minimize ground transportation distance, effectively turning the limitations of space operations into opportunities for cost reduction

Inventive Principle:

Principle #22Blessing in disguise (Convert harm into benefit)

3Productivity

If reinforcement learning is implemented to identify optimal decoupling locations, then delivery efficiency and transportation optimization improve, but computational complexity and system requirements increase

Engineering Contradiction:

Improvedelivery efficiencyVSAvoidcomputational system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The reinforcement learning model acts as an intermediary computational layer between the manufacturing unit's operational parameters and the decoupling decision. Instead of requiring complex real-time simulations or manual optimization, the pre-trained model provides straightforward recommendations for optimal decoupling locations, simplifying the system architecture while maintaining high delivery efficiency

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system uses simulation environments to create virtual copies of the space operations for training the reinforcement learning model. By training in simulated scenarios that replicate real orbital mechanics and operational constraints, the model learns optimal strategies without requiring complex computational resources during actual operations, thereby reducing the computational burden on the real system

Inventive Principle:

Principle #26Copying

Data Source

PatentUS20250128831A1Identifying a space vehicle decoupling location using reinforcement learning

Publication Date: 2025.04.24 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to identifying a decoupling location for a space vehicle using reinforcement learning. The computer-implemented system can comprise a memory that can store computer executable components. The computer-implemented system can further comprise a processor that can execute the computer executable components stored in the memory, wherein the computer executable components can comprise a decoupling component that can use an input from a reinforcement learning model to identify a first location that can be in space, for decoupling a space vehicle from an in-space manufacturing unit, such that the space vehicle can land at a second location that can be on a planetary surface.